This is a map of an area in North West Pakistan. Part of the Hindu Raj range with the Afghan border just to the North. This is a beautiful and rugged area, and you wouldn’t know unless you went that the map is wrong. We did, and we learned.

Since those early demonstrations, the tech sector has poured over analytics and heatmaps trying to understand how different people interact with different web experiences. This was done to build usability into web products to achieve specific business goals: buying products, clicking likes, and understanding information.

In the geospatial world, we also care about how people think about and use maps (at Sparkgeo, we even built a web maps analytics technology, with mixed success). But I would argue that again, Google “took the map UI biscuit” with the development of Google Maps and Earth. Repeating their previous success with the same strategy: applying simplicity to potentially complex interfaces.

For the past 20 years, the web community has been obsessed with user experience. We did this because we were building experiences for the masses. The power of web products lies in meeting the needs of as many users as possible. That’s the key concept of scalability. That’s both the allure and the foundational business model. So what happens when people can easily build their own software? What then happens to the interface to that software?

Personal software is one question, but agentic computing is another. I built an agent that can pass the GISP. Then I built an agent that can interpret a GIS request and parse it out to a “team” of agents designed to replicate a typical GIS office. You know, the GIS technician, the GIS Analyst, the GIS Developer. These are all archetypes I’ve encountered throughout my career. Across European, North American, and Middle Eastern cultures, I met the same archetypes in GIS offices. I have been one, am one.

There was once a deep technical barrier to using geographic data. One had to be able to diagnose problems with data (because it’s always just various forms of bad). One had to set reasonable expectations for analytic capabilities and approaches to data science questions. One had an encyclopedic knowledge of where useful datasets reside and how to access them. One had to know how to move data from one form to another and safely transform coordinates and areas across projection systems without too much accuracy loss. And then one had to be able to actually design and run a geographic process, a workflow.

Reread that paragraph, and I will let you decide if the technical moat of the digital geographer has been eroded. I find this fearsome, but also somewhat exhilarating. In the end, we should remember that GIS has always been a tool for data exploration. A tool to ensure data makes sense, to get a visual. Actual analysis or workflows would be delivered elsewhere.

I think we are at a moment of change. We have more data than we have ever had. Far too much data for any single organization to adequately parse. We’ve needed machines to help us for years; they’ve just now caught up with our needs. The Earth Observation community can begin meeting some of the expectations the general public has had of us for years.

Here are some core strategic themes to keep in mind as we navigate the next few years.

Time is a first-class citizen in geospatial.

With the torrent of data flowing out of Low Earth Orbit, from swarms of drones, and from the billions of smart devices carried by every networked human, snapshots of analysis are now somewhat meaningless. Time is a first-class citizen in geography. This means maps need to be living representations of our changing planet. Whether that’s changing storefronts, new urban development, or more catastrophic change from conflict, social unrest, or climate change, maps are not static. More importantly, users of mapping and geographic products expect technology to reflect their experiences.

Approaches: Build tools, agents, and experiences for change.

Investing in technology is like buying a car; investing in data is like buying a house.

Continuing the idea of time, we can see how a corpus of data can accrue value over time. The notion of change is really a factor of time; we can only detect anomalies or changes if we have a baseline created over an extended period. So, more data leads to a better baseline and a better understanding of landscape changes or human activity. Investing in data and access to that data is an investment that will grow in value.

Conversely, as soon as an interface is built, it’s out of date. In the mid-2010s, I would say this based on the frequency of JavaScript framework updates and changes. Now it’s because user interfaces have become virtually ephemeral. I know my team have built interfaces that are useful for only 15 minutes. So, with the idea of personal software comes the reality that interfaces depreciate faster than ever.

I remember when it was futuristic to consider an API as a user; now we need to consider agents as users. Practically speaking, an MCP environment is really not much different from an API. But they are different and should be treated as such. For instance, neither should be subject to a 2000-feature limit.

Approaches: robust and secure programmatic data access is a great investment. Care more about data, because your users will be building their own unique interfaces.

Your personal moat might be in your ability to think spatially.

As referenced originally in the GEOFESTO a couple of years ago. Even if LLMs take all our jobs, people still need to think spatially to ask robust questions about geography. So I believe there is value in thinking through the implications of spatial and geographic context. The natural extension of this thought is that, while I worry about the GIS office in general, I believe there will be a job for the GIS Analyst, or ‘spatial data scientist.’ The ability to think spatially is not an attribute everyone is blessed with, and as the technical barriers to digital geography drop, those questions could be incorporated more easily into everyday business decision-making.

Approaches: ‘Spatial’ is a way of thinking, cultivate it, share it.

Geography can (and maybe should) be componentized.

Things are going to have to shake out. There will be disastrous mistakes made by AIs, as there have been terrible stories of people blindly following GPS directions into disaster. But we can reduce these problems by componentizing data products and processes. It’s clear that AI will use convenient components when asked; those tools can ensure robust geographic processes are followed. In many ways, the open community have been using the GEOS and GDAL libraries for this very reason. The same can be true of mapping interfaces, geospatial data access, and a common understanding of time, geography and spectra. In many ways, CEOS’s ARD efforts provide directionality towards a more componentized future.

Approaches: In your organization, build and make available components to ensure that any internal AIs use your authoritative geospatial data and display it correctly. This practice should desilo geospatial activities to minimize the risk of AI misuse. An unintended consequence may be increased use of geographic data and tools, driven by the availability of componentry for broader AI use.

This is the reliability diagram associated with the map above. We were in Area B, where you can see that the map was interpolated from a sketch made by Shipton as he travelled through the area in 1947. So, perhaps not a hallucination, but an attempt by an agency to fill in logical blanks with the “most likely next feature”, does that sound familiar? Also, we should have known better.

Over the last few months, I have been writing about the need for a country, including my country, to understand itself. In The Back Forty, I suggested that Canada’s North, while vast and largely empty of people, is also our most valuable yet least monitored asset. In the past, when space was, in and of itself, a defence against prying eyes and ill intent, we could afford to let the unforgiving North be our deterrent. Now, with technological development and a warming climate, it is our turn to look after that space that has defended us for so long. And in that article, I suggest that we use space to do that, well, satellites in space, at least.

In Deep Horizontals, I point out that the geospatial sector is obscured behind the vertical of GIS. I believe that geography is part of almost every decision we make as professionals, but the GIS sector has evolved into a siloed practice, leading many non-experts to seek an alternative to the profession. And in AI ate my GIS, I suggest that many will turn to modern AI tools as their solution. Thus, the question is: will the geospatial sector engage to build the AI maps of the future, or will it let AI companies themselves dictate the quality and appearance of these interfaces?

This line of questioning brings me to the notion of SovereignAI. If Canada is to develop an AI for Canada, a Sovereign capability, then shouldn’t that capability have an understanding of Canada’s changing human and physical geography built in? Shouldn’t we know ourselves?

(Geo)AI

In a recent presentation by Natural Resources Canada on their Planaura geospatial foundation model, I heard the presenter state very plainly that:

“A good AI model is a national asset.”

This statement is prescient. In the debate on what SovereignAI is, the fact that Canada has developed a foundation model capability is something we should be proud of and talk about. Foundation models are a critical part of the AI landscape (sorry, I couldn’t resist), but not by any means the full story. That said, the advances NRCan has made in this regard are noteworthy and valuable.

While Planaura is a wonderful example, the statement that a model can be a national asset is also critical, and again hints at an alternative way to consider the sovereignty of artificial intelligence. Our ability to parse pixels and points to understand and infer relationships is a part of knowing ourselves, and therefore, sovereign.

Changing climates are having physical effects. Melting permafrost causes infrastructure to subside and, in some cases, literally dissolve. Being able to use a foundation model to look into the infrastructural future of a community under different climate scenarios is a capability that builds both municipal and defence resiliency. To use a phrase du jour, a true “dual use capability.” While Earth observation, specifically InSAR technology, has been used to measure subsidence, applying a foundation model to this technology suggests the capability to project risk under changing climatic patterns. Given less supportive ground, which buildings, runways, roads, piers, water and power plants are at most risk?

As in so many use cases, GeoAI, or even just AI, isn’t the base technology but a layer of scale and fusion that enables easier data integration: an acceleration of today’s practices.

Data storage & distribution

While I have suggested that the practice of GIS could be disrupted, I feel strongly that the value of authoritative geospatial data will only increase. In fact, the storage and distribution of geospatial data via secure and open APIs will allow both humans and machines to easily read data in commonly understood formats. The SpatioTemporal Asset Catalog is a great example of a commonly accepted standard. While Canada does seem to be taking this responsibility seriously, with effort going into the Digital Earth Canada product. It is still unclear how this product will operate, who will have access to it, and when it will launch. That said, Canada has extensive data archives that could be highly valuable to geospatial AI technologies, some of which are available on Geo.ca.

One key question is which companies, nations, or partners will be allowed access to that archive? In no uncertain terms, like good AI models, this data archive is a national asset and should be treated as such.

While Canada is developing a sovereign capability based on a national archive of geospatial data, this archive could also be used by others with less dignified values to develop the same capability. While I feel strongly that we know ourselves deeply, I am less comfortable with others developing a comparable familiarity.

I applaud and support the invaluable contributions of ESA, NASA, and others to the development of scientific-grade global data products: a global public good. But there is a strong argument, within the context of sovereign capabilities, that some critical national products should be kept secure. As one would with critical infrastructure. As we all do with our personal health data.

At Sparkgeo, we feel strongly that in an AI-centric future, authoritative data distribution will be a common theme. That’s why we built our geospatial data distribution stack called Prescient. It’s called prescient because structured cloud-like data distribution enables future-looking, AI-enabled applications. Without good data hygiene, we don’t get to access meaningful SovereignAI.

Geospatial, and owning the map

I tend to default to geospatial data because that is what I know best. But in the development of Sovereign capabilities, the same patterns will hold for other sizes and shapes of data. Finance, health, and sentiment data all inform a country's internal workings, shaping its management and policy.

That said, we should not forget the famously unreferenced cliche suggesting that 80% of data has a location attached to it. Given the proliferation of device-based data, ad-tech, connected vehicles, and satellite imagery, I would politely suggest that the number is now much higher (much, much, much higher). With that in mind, we need to return to the question of sovereignty and mapping. Who will write the maps of our country, and will these maps have access to authoritative data products?

We know that AI technology tends to adopt components of convenience to answer questions. In many ways, this is a very human behaviour. While we were shocked by OpenAI and friends seemingly stealing artistic IP from writers and artists, in the 2000s, Napster was doing the same thing to music. Perhaps we taught the machines to harvest data the way we would ourselves. Nevertheless, I would prefer to see our country benefit from the data we have acquired, which, in many ways, is about us. In this manner, creating AI-consumable componentry for responsible AI consumption would be a productive approach to the problem.

What would components look like? Well, at Sparkgeo, we have been involved in building a mapping component library for the European Space Agency called the APEx platform. This has a series of interface components that tie into data products in known, commonly understood ways. The intent here was to make scientists’ lives easier and to provide a common look and feel for ESA’s scientific output. However, this kind of structure also ensures that AIs do not make elementary geographic mistakes and that data standards are maintained.

From a geospatial community perspective, we let a web search company build the de facto consumer mapping product. Who will we let build the AI maps of the future?

ISR data fusion

If we move to the more secure discussion of surveillance platforms, we find the same problems, but on a larger scale. The US military suggests they will ultimately have a drone per infantry squad. These drones will look around corners and check over features. They are mini Intelligence, Surveillance, and Reconnaissance (ISR) platforms. But where does that data go? If the Canadian infantry is to adopt a similar approach to drones, then the sheer amount of data flow will become quite a burden. But in that data flow will be nuggets of intelligence which humans might easily miss.

This, combined with underwater vehicles, higher-altitude ISR platforms, satellites, vehicles, and people all collecting data, leads to a very confused “common operating picture.” If we throw in jamming and the fog of war, we are left with a hugely problematic software and connectivity picture. One that leaves our military in a confusing labyrinth of data sources, each with its own nuances. This is a complex mess for full-time analysts to untangle. Doing this in the field, under extreme pressure, in a mixed connectivity environment and hostile climate, is impossible. The common operating picture is one problem to solve; another is how that data is subsequently reviewed for after-action activity. Not only is the data to be visualized, but it has to be stored with extreme security.

In a similar problem set to that presented in the monitoring of vast spaces, the decentralized capture of secure data will lead to a massive intelligence-gathering nightmare. Thousands of drones will capture high-resolution, geospatial video in circumstances that will encourage review for intelligence purposes. We can solve the technical questions, but who will be able to watch all the videos? In the end, we know it will have to be machines directing algorithms to look for signs of enemy activity, IEDs, or even search-and-rescue targets.

The problem of data fusion is at the heart of the Earth observation product problem, and the dirty little secret is that not all geographic data lines up. In fact, most data does not align without some additional work; it is likely that GeoAI will play a major role in aligning georeferenced features through space and time for after-action analysis in a digital twin of an operation.

The biggest problems in Earth observation today are all in software. To access robust Geo or SovereignAI, we will need to address some of these complexities of scale.

Training data

As we think about AI and Sovereignty, we should also consider how data becomes AI. And how (and where) we build training data sets. I suppose this is somewhat back to the original case of real estate. But this basic labelling activity is still essential, and much of it is still done offshore. This potential hole in our Sovereign AI strategy can be solved by bringing back some of those technology jobs to Canada, especially around more sensitive subjects. There will be a cost, but there is also a tremendous benefit in shifting technology jobs to lower-cost environments (outside Canada’s big cities, where housing is still affordable) and even to more rural settings. Ultimately, this activity can proliferate technology jobs across the country, reducing barriers to entry and opening the door to a broader tech economy. Rural Canada, while famous for hewing wood and carrying water, could also be a decentralized network of data creators. I say this as someone who lives in Northern British Columbia, with robust, redundant connectivity data could flow faster than any natural resource.

Decentralization for resilience

The idea of distributing the capture of training data to more rural settings is predicated on having a robust connectivity strategy for Canada. At every level of economic and social development, this is absolutely paramount. Access to connectivity is access to knowledge, and it is clearly a point of equity for Canadian citizens.

The added benefit of robust national connectivity is the opportunity to decentralize computing resources, enabling the internet to function as it was designed: a network of decentralized computing hubs. With decentralization comes resilience and, by extension, independence.

Procurement as enabler, not barrier

This is a note on SovereignAI, and while applicable to any country, it’s certainly presented through a Canadian lens. With that in mind, investing in Canadian companies to build the Sovereign AI, or the Sovereign GeoAI, would be logical (if not somewhat self-serving of me to say). There has been a trend towards the Maple-washing of other nations’ technology companies, with their regional hubs in Toronto or Ottawa, trying to look and sound Canadian. I am sure wonderful people work in these establishments, but where are their owners and shareholders? Where are their executives? Whose software are they selling or reselling?

Companies like mine, Sparkgeo, have, for years (since 2010, for me), been forced to work for commercial entities or for foreign governments, beaten out of the Canadian Government market by less-qualified but better-funded competitors. While we’ve worked for ESA, UK Space, and various Saudi Arabian institutions, we found that the Canadian Government procurement system could hardly have been better designed to prevent small Canadian technology companies from engaging. I believe companies should be competitive, and working for commercial companies for fifteen years has taught us how to deliver extreme value under high pressure. So, when I see how the Canadian Government is generally treated by the traditional cadre of service providers, I am disappointed.

However, I am excited to see that teams like Sparkgeo might now have an opportunity to engage, and I look forward to demonstrating how a small team of experts (rather than contractors) can raise the expectations of a nation.

A year or two ago, I came to the conclusion that GIS, as a technology, is a convenient environment for data exploration. It’s best for data with a geographic component, and GIS provides the helpful feature of showing that data in a cartographic manner. But really, this is just a data visualization option, a map. One can also interact with aspatial data, as we call it. Or data without a spatial component. In GIS-land, we have a name of data that GIS can’t natively visualize, which, counter to popular geospatial opinion, is most things. Unironically, outside GIS-land, aspatial data is just data, and spatial data is just data. In many ways, this relationship is comparable to the “Venn of Geospatial People”. But semantics aside, GIS is useful for analyzing spatial data; it’s handy for getting a visual check of a data product's quality, scale, and “look.”

Wait, I’m already logically confused. I confused analysis with visualization. These are two different activities; as geospatial people, we can conveniently conflate them. As an analyst, I want to ensure the data I am using is fit for purpose. That trust-creation process supports the analysis but isn’t necessarily part of it. Traditional GIS excels at that trust-building process. Unfortunately, it’s actually bad at running analysis at scale. Hence, the proliferation of “accelerated geospatial” companies. My observation is that if GIS were good at scale, there wouldn’t be a market for “more scalable” technology. This is the basis for my observation of GIS as a data exploration tool: it is designed for the one-off exploration of one or more data products and for examining the results.

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So, GIS is an excellent tool for visualizing spatial data products, often to tweak, refine, or build trust in those products. The data, once confirmed as authoritative or indeed “good enough,” is published for use elsewhere. I agree with this workflow. Data should be taken to a point of confidence, then published for use by broader systems. This speaks to two of my regular talking points:

1) That geospatial is a deep horizontal, and

2) That, while investing in technology is like buying a car, investing in data is like buying a house*.

Unfortunately, confusion has arisen about the boundaries of GIS as a practice. An argument that I have no energy for now, suffice it to say that the siloization of GIS practitioners has not been good for the geospatial community at large (notably, but not exclusively, in terms of employee compensation and technology integration). But I also think that the GIS practitioner, who might see the map as their product, is facing a deeper problem: personal software.

Personal software

I’ve built two or three applications this month. I can’t remember the actual number because I don’t have them anymore. I built them because I needed to see something or scratch a particular technology itch. I default to Claude Code because that’s what we use at Sparkgeo. But really, it could be any AI of your choice. The point isn’t which AI; the point is that I, the CEO of a small company with a technical background who is probably now “the worst software developer in my team,” am building apps. Those apps I built might last 15 minutes or 15 years. They might be handy or just silly.

An Aside…

For the Earth Observation geeks in my readership: I built an application to recieve a dragged hyperspectral image up to 5GB, then tile appropriately for visualization, then apply a PCA analysis to the bands of the image which are typically outside the wavelengths detectable by the human eye, then run Meta’s “Segment Anything” algorithm over the result and show the original image, the PCA output, and the Segment Anything output as three layers on a slippy map for visualization. The idea being that I was building a visualization of our invisible planet. Well, invisible to us humans. A fun little art project.

This took an hour or so. There is something about the dopamine hit of building something. It’s worth reflecting on the addictive nature of this kind of accelerated building. Too much dopamine too fast is probably unhealthy.

While art projects are fun, this is also an interesting data point: this would have taken me forever to build previously. In fact, I probably wouldn’t have bothered; it would have just sat in the back of my head with a bunch of other ideas getting rusty. But with the right tools, it took me just an hour or so; I satisfied my curiosity and felt like I “built something.” And that dopamine flowed…

Tell me the time

I heard a wonderful analogy last week at the NASA Earth Science & Industry Summit in DC, credit to Christine Wallinger, who I suspect might further reference her university professor:

“We show people how to build clocks, but they just want to know the time.”

Traditionally, we have provided extremely complicated tools to build perfect clocks. And people do care that their time is correctly kept. But a barrier to the adoption of geographic tools is that business people don’t want to build clocks, or have a team that builds clocks for them every time they have a geographic question. In the last two weeks, I learnt that those clocks can be built and discarded with so little fuss that, in many ways, user interfaces have become ephemeral.

The previous week, I was having coffee with an AI researcher from a notable AI company at a lovely Chilean coffee shop near Paddington. As the crema of my coffee slowly dissipated in the cool air, he showed me a workflow his team had built in real time for a government customer. While there was nothing groundbreaking in terms of geospatial and mapping, clearly, this was an application one would describe as a GIS. The workflow had placed points on a map and used them, along with their attributes, to create a least-cost path. Those points were sourced (by the AI) from a specific open data source, and the mapping fabric was open source as well. So, in delivering a traditional GIS product, the AI had happily created a satisfactory experience for the government customer without touching any traditional GIS technology or any explicit geospatial expertise.

Bring it on, feel free to yell

You could say “lack of authoritative data!” or “AI slop!” or “cartographic incompetence!” and you could be right on all counts. That’s not my point. This was not a good map, but it was adequate. My point is about the definition of “good enough” and the disruption that occurs when an underserved market encounters a low-cost product. That underserved market could be the deep geospatial horizontal referred to above, and the low-cost product could be prompt-driven AI.

The question here, then, is whether this new geospatial market will be served by traditional geospatial technology companies or by others. If we remember, Google Maps was created by a web search company, not a mapping company.

So, if we are in the end of days for SAAS applications as the markets are suggesting (SAASmageddon!), and AI will eat the world, as various billionaires will insist (all who have a financial interest in AI eating the world, I should point out). Then where does that leave the geospatial sector? Where does “authoritative custodianship” come into this debate? Have we forgotten to care about data and maps? Who will be designing the maps of the future? Who will sweat over label positions? Who will worry about scale effects in data misrepresenting a particular feature? Who will tell me they don’t like pink on their maps (hint: apparently, middle-aged male foresters, at least they didn’t in the 2000s)? Will AI slop insist that the great circle route is not the fastest way across the Atlantic?

No. Precisely no. In fact, that is the point: we can be comfortable in the fact that our user interfaces will change, and they should, yet data still has an intrinsic and increasing value, and we can extract more and more meaningful analytics from that data. But we will have to think about geospatial service delivery differently, and that shift will come with new business models. If we are very lucky, that change in model will bring greater access to the deep geospatial horizontal market I keep talking about. Ultimately, enabling a much broader array of businesses to use geospatial technology daily at a lower cost.

If we were to summon some optimism in a time of undeniable cynicism, we could see a resurgence of geographic capability and use, because it’s easy.

Where are the geospatial experts?

This future may seem bright for the multitude of non-expert users, but what about those people who used to make maps? Well, for a long time, they still will. The future is, of course, unevenly distributed. As there are still fax machines and people who make horseshoes, mapping will remain a skill. But interfaces will change, fast for some, slow for others.

But for those looking to engage with a new market, we will need to ensure that data products are authoritative, accessible, secure, and consistent. Additionally, we should be building componentry that AI technology can assemble into convenient user interfaces. That componentry should provide the algorithmic or geographic rigour to put the minds of the most fervent geodesists at rest. The components should allow non-experts to assemble geographic experiences without worrying about the complexity of clock-building, while knowing that experts have ensured quality, and to assemble their data products to tell their time with ease.

If it doesn’t or we don’t, then we will have failed, and again, we will have let another industry eat our lunch.

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*I cannot believe I haven’t talked about this subject, but it’s true: data accrues value over time like a house, while technology depreciates almost instantly. We will dig into this more when I write a note about this core Modern Geospatial concept. Watch for it in future posts.

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My journey to North51 usually takes me through the Canadian Rockies. I am privileged to drive south on one of the most beautiful highways in North America to get to Canmore, Alberta. While the winter driving can be a little touchy at times, this year was clear. Clear and absolutely beautiful. And this week’s drive started with a light show.

North51 is not a normal event; it is unique and notoriously small. We call it intimate, but that intimacy means everyone speaks to everyone, and everyone has something meaningful to say. Don’t come to make sales. Instead, come to share ideas; come to build the future.

I was lucky enough to stop for a skate ski on my way through the Rockies, taking the opportunity to stretch my legs after a long drive and assemble my thoughts for an intense few days. There is something in the work of climbing a hill, followed by the cold blast of descending, that provides a unique mental clarity. The event this year was split over Wednesday afternoon and through Thursday. Friday was left to catch up on the final missed conversations and goodbyes over breakfast, or to embark on more adventures.

We used to have keynotes sprinkled throughout the event, but now we have as many fireside chats and panels as anything else. This allows for the free flow of stories and experiences. This is a key feature of our event. We don’t want a canned presentation so much as a realistic discourse. This is embodied in our commitment to the Chatham House Rule. No sessions are recorded, no quotes are attributed; it’s designed to be a safe space. This allows real discussions.

And these discussions are absolutely critical for our industry. Every year, we seem to struggle with business models versus the humanitarian power of Earth Observation. We talk through the friction between resource defence and environmental communities. But in an age of political binanryism, N51 is a place where honest discussion can actually happen.

Because North51 isn’t normal, sponsorship is hard. With that in mind, I want to make a special shoutout to all those who did:

Tilebox, Altalis, T-Kartor, MapGuru, Alberta Data Partnerships, Motivf, Photosat, Wyvern Space.

These brands value integrity and innovation in geospatial technology. I would encourage anyone following North51 to take a second look at each of these organizations, knowing they embody the best of our community.

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We started on Wednesday afternoon with a fireside chat between Dr Emily Darling and me. Emily is an eminent Marine Scientist and Co-Founder of MERMAID at the Wildlife Conservation Society. MERMAID was developed to replace the Excel spreadsheets of individual Marine Scientists. Her leadership has taken MERMAID from a database schema sketched on a napkin to the leading repository of Coral health data and a global view of coral health. Though with only two columns of geospatial data, lat and lng, location is critical to MERMAID. MERMAID has provided a sense of scale to the Coral Herath community, but Emily was also able to tell us about their Bezos Earth Foundation award, which will allow her to scale up again to a crowd-sourced model, powered by AI. There is a valuable subtext here about the arc of innovation: from a single user, to a group of experts, to a crowd of enthusiasts, and how technology can serve communities.

Led by Caity Milton, “the future of collection” was a panel (Chris Robson, David Page, Clint Graumann, Stefan Amberger) that covered the practices and models of satellite collection and distribution. There were discussions about innovative financial approaches, the role of government in earth observation (hard to ignore), and how funding certain infrastructure can provide additional financial and operational benefits to unintended groups. Some thoughts on unlocking the potential of EO and how practitioners could adopt new models when government customers are less agile. Finally, determining the value of archived data is always a hot topic, particularly in light of AI-based applications.

On Thursday, Jon and I were happy to hand over North51 to Priscilla Cole from Geospatial Risk. She provided colour on her organization and introduced Louie Woodall from Climate Proof, who took us through a view on the value of geospatial data. Again, there is a schism between humanitarian value and the need for commercial satellite and aerial operators to keep paying the bills. Louie spent some time digging into the central concepts of climate adaptation, highlighting that climate change is not something that will happen; it is happening now. The insurance industry has been hurt, but won’t accept the same fiscal beating; instead will apply technology to make better bets. The insurance sector doesn’t lose, but we might lose our policies.

Priscilla then welcomed Tee Barr (Geospatial Risk’s Co-Founder) to the stage, and they took us on a discursive journey through the technical elements of measuring risk with geospatial technology. The different elements of the insurance sector, how to think about parametric insurance, the different senses of time, and risk vs catastrophe. This discussion left me feeling that our understanding of risk depends heavily on scale and accuracy. As devices advance, this can unlock more revenue opportunities (or reduce losses) for the risk sector.

After lunch, North51 returned with a panel on “Geospatial beyond the GUI” with Nate Ricklin, Veronique Nell, Ash Hoover, and Ben Tuttle. While geospatial has always been a User interface-centric activity. Increasingly, we are seeing that geospatial processes are happening “under the hood.” This, combined with modern AI technology, is a heady brew. But there is concern with unsupervised machine-created inferences. The concept of human-on-the-loop (HOTL) developed and seemed popular. In contrast to human-in-the-loop (HITL), HOTL seems more agile. Critically, inference speed can be retained while maintaining insight quality.

Not to be outdone, Bill Greer, Steve DeRoy, Ash Hoover, and Peter Rabley took us on a tour de force of the ethical topics facing our industry. “Ethics, Influence, and the Role of the Practitioner” embodied the deep paradox of the geospatial community: that there are so many hugely valuable activities which ought to have a value, but only Governments will pay for. But what happens when those activities are not in a government’s interest? This resonated particularly with Bill’s commentaries on Common Space and Steve’s deep experiences with Indigenous mapping. While we were left with much food for thought, there is a sense that more honest discourse and some empathy would go a long way. Also, there is a sense that “pay what you can” is a concept that sounds naive but could be surprisingly useful.

We rounded off our main day with another fireside chat between Jon Neufeld and Don Murray, the Co-Founder and present CEO of Safe Software. Don has been a humble geospatial luminary for the last 30 years, yet didn’t appreciate being described as a legend. The consistent success of Safe Software and its primary FME product line is undeniable, filling a repeatability gap with practical and valuable software. When described that way, it all sounds easy, and listening to Don would trick you into thinking that everything was straightforward. That’s the thing about people like Don, their humble, understated stories would make you think anyone could succeed. But the arc of success from satisfying a contract for the British Columbia Government to building a profitable company with $250m of annual revenue is not a normal thing.

With that, it was over.

Except it’s not, not really. Ideas tend to stick and grow. This year, the themes for me were:

Arcs of success, seeing examples of small incremental, practical successes building into amazing

Applications of technology, fear of technology (especially AI) is unhelpful, but people do need to be led with integrity and honesty

Risk is apolitical, and increased accuracy is usually a revenue model: this is the only time that bad news sells (apart from the actual news)

The business models of EO are still unsettled, but there is agreement that the “per square kilometre” model is unsatisfactory.

These ideas will now influence my thinking and how I see my community. And I guess, that’s the point.

Hopefully, I’ll see you at N51 next year, and if you like backcountry skiing, talk to me about the Executive Dirtbag Offsite.

Hey, perhaps you haven’t heard, but North51 is coming up in January. This is the geospatial thought leadership event. If you haven’t been before, you should take a look. If you have, you know what to expect, except this time we will be deep in the Canadian winter, and we have some great deals with the local ski hills.

Something a little bit special?

The geospatial community is full of old companies. While I don’t feel Sparkgeo is terribly old, certainly not in comparison to venerable organizations like the UK’s Ordnance Survey or Swiss Topo (Switzerland’s Federal Office of Topography), or even Esri, the leading pure commercial organization, I do feel we’ve “been around the block.” One of the reasons we’ve stayed in business is annoyingly counter to almost all the advice one might hear in the typical technology business circles. Geospatial, as a practice and at scale, is almost always a little bit custom and custom business activities usually demand a services team.

What’s wrong with services?

Services revenue isn’t scalable. That’s the oft-repeated mantra. Software is scalable, you can build once and sell many times, and services are linked to “billable hours,” so one can only scale with more FTE’s. On an exceedingly simplistic level, that’s true. Except that nothing in life is simple, and business mirrors life.

However, let’s take a quick tour through the generalities. When considering services versus product companies, products tend to attract a higher exit value, which, for lack of a better metric, is the central perceived business value: what someone would pay for that business. There is an inherent bias in this statement, as most valuations are driven (reasonably) by investors seeking a return on their investment. In contrast, most service companies don’t need investors. Interestingly, this implies that future revenue is more valuable than actual revenue.

However, the central point here is that the typical investment community views a services component in a company’s revenue stream as a dangerous path to a reduced unit economy. Their “build once, sell many times” mantra is diluted by any billable hours of services tinkering. Worse, if the company in question is publicly traded, and the market views its revenue as consulting or services, then the share pricing implication could be disastrous.

This has led to a situation where new geospatial companies are motivated to be viewed solely as products, and not as services. Yet, in almost every occurrence of the GIS or geospatial experiences I’ve had, very little is genuinely repeatable. Yes, we will use the same tool, but to do different tasks. I would argue that traditional GIS is essentially a data exploration environment. When GIS analysts discuss automation, it is usually in terms of automating an activity for multiple records, rather than supporting many users performing a repeatable activity. Perhaps this is why GIS companies have struggled in consumer-web environments?

Let’s get physical

Our world is a complex system, and when we attempt to model it, the patterns are not typically reusable. Think about car counting. We can count cars in the Port of San Diego from Earth Observation data. But that algorithm will not work well in the Port of Dalian in China. There will be differences in atmospherics, differences in parking patterns, and differences in vehicle types. So the algorithm must be tweaked. This is true of most Earth Observation practices. While we hope that geospatial foundation models will resolve this problem, it persists today for those seeking robust, production-level accuracy. So, who does the tinkering? Consider languages for user interfaces in mapping, various addressing styles and cultures, and different search styles for different types of mid-morning drinks. Who does that work?

There is something about the interface between the complexity of physical geography and the complexity of human geography that, when combined, results in most geospatial platforms being generally applicable everywhere (see the geospatial product trap) but not being easily applied anywhere.

As a community, this profound physical-human complexity has always been difficult to articulate, yet it is something that most practitioners know and have experienced viscerally. In many ways, this is the surprising art of geography, why many of us remain joyfully curious about our world and its evolving systems.

I’ve come to consider geospatial as similar in nature to Artificial Intelligence, in that it is almost always the component of an answer to a question, but rarely the entire answer. As such, I think of geospatial as a deep horizontal. However, the critical extension to this observation is how one combines geospatial technology or data into other interfaces to provide a fuller answer. It’s all about interfaces, some digital, some human. While we can always get better at APIs and open (but secure) standards, and we should, those human interfaces are usually developed by physical people in services teams.

Yet still, there is a long list of companies that tried to execute without a services strategy but failed. And another list of companies that insisted on chasing the next project, thinking they were seeking product-market fit, but should really just have committed to being a services team or a services-led product, by building margin and project management into their services business.

Services-led geospatial

The solution, as I see it, is to commit to the obvious. New geospatial product companies, whether they be data products or software tools, should be comfortable with the idea that services are necessary. That idea needs to be seeded early with any investors, so arguments against the inevitable pushback can be prepared. In many ways, the controversial yet successful company Palantir has employed the “Forward Deployed Engineers” model for almost a decade. This is just a rebrand of a services-led technology product strategy. It is also worth noting that Esri (link above) also has a very robust consulting team, and each country-level organization is, in effect, a reseller and localized services provider. This model has become contentious among some, being perceived as somewhat anti-competitive, but it remains hugely successful.

Having a geospatial tool wrapped in services means that the customization component can be executed while the product is also being sold and developed. There are now two sources of revenue allowing for lower levels of initial dilution, and potentially a more agile product-market fit process. This also benefits the services team as their revenue becomes less “lumpy.”

Finally, it would also be possible to sell this structure more easily as a managed service, ensuring that there are people more closely attuned to how a customer is using or wants to use a product. Hopefully, this model will build a better product for all.

Don’t reflect, refract: time only goes one way

This is the time of year when everyone talks about what happened in the last twelve months. While your experiences do matter, time only goes one way. So, don’t get caught reflecting too long; instead, use your experiences to build forward and through; refract your experiences.

First, don’t forget about booking tickets for North51, January 21–23, 2026. If you like this Substack, you’ll love North51! This is not a traditional conference. It is curated, ideas-driven, and built for people who want real conversations about the future of geospatial. Even better, this year we’ll have sessions especially curated by Priscilla Cole and the Geospatial Risk team.

An executive-level opportunity to talk about the pressing issues in the geospatial community of practice. And this year seems more pressing than most! See you in Canmore, Alberta.

New tools, old patterns

Sometimes, I get a sudden wave of realization. This could be while laying awake in the small hours of long night. It could be that two thirds into a long run, when coming to the crest of a raise, I just stop. It could be, like this time, when I was flitting between sleep and wakefulness on a transatlantic flight. It’s usually at a moment like that when my mind is tricked into wandering. Tricked, out of the fury of “the now”, into a different place. On reflection, there must be something in that liminal state between sleep or meditation (don’t you lose yourself in a run?) and presence. Something in that transition between mental states.

I see patterns

I’ve frequently talked about dominant designs. Why does a car have four wheels? Why are most pens the length they are? Why do glasses look the way they do? Because, through a process of iterative industrial design, we have reached a point of consensus on the way some common things look and operate. Sometimes, that consensus has come in the form of standards: shipping containers, for instance, sometimes we have just agreed that a thing should look a particular way. But it’s worth noting that dominant designs surround us and while they provide comfort and often production efficiencies, they can also be a source of limitation. By “limitation” I mean that the comfort of obviousness of a particular design can be a source of hidden opportunity for the entrepreneurial.

Another critical point is that a dominant design need not just be an article, it can also be a process. Legacy human and digital processes are a curious source of innovation. Very often people do things, because that’s the way they do things, not because that’s the best way to do things. So when we think about dominant designs, don’t forget that these can also be in the way we do things as much as the tools we use. If we are doing an old process with new tools, have we really innovated?

So, today we are discussing dominant designs; patterns of design and behaviour.

And the Cloud’s gone wild!

One dominant design I think about is how we think about data storage and use. Traditionally, we have become used to acquiring data, perhaps through survey, purchasing, or exchange. Then, we would expect to move this data to a place of ownership, and subsequently use the acquired data. This could be as simple as a work colleague sharing something via email or a thumb drive. Or it could be more complex, perhaps involving the purchase of large amounts of remote sensing imagery. While acquiring imagery via API is a step forward, in essence a download API is still adopting the traditional pattern of moving acquired data from location A to location B. Worse, we are not just moving, but copying.

The main problem with moving data from A to B in this manner is our tools have evolved but the process has not. This is a dominant design for a pre-cloud era. When we apply this simple method of exchange to the cloud, the practicalities start to break down. This dominant design becomes wildly expensive, incurring storage costs per exchange, as well as egress and ingress costs and any costs involved with the actual bandwidth consumption.

Solutions to this process might include allowing access for local compute close to the stored data product. This could allow for running algorithms or querying data, perhaps. This workflow might also open the opportunity of location-oriented subscriptions. In this scenario, the needless data movement would be replaced with increasingly complex feature level authentication/authorization needs. But this is a better, more fit for purpose pattern.

In the end a new dominant designs around networked data access will emerge. Indeed, my suggestions is just the one that springs to mind. You are welcome, and I encourage you, to innovate your own. My point is the accidental application of old patterns to new toolkits.

Unprojected augmentations

Another process for future consideration is how we think specifically about spatial data storage and projection systems. The need of a projection system is because we don’t live on a flat Earth (sorry folks, I’m firm on this one). We needed data captured during surveys of our planet (local or global) to be visualized on a flat surface. So, we built mathematical projects to help us do that. There are thousands of these systems, and they each have different levels of accuracy and appropriateness for different locations. Traditionally, that flat surface would have been a paper map. Within the last 30 years, that surface was more likely to be a screen.

But, now we have GPUs (Graphics Processing Units). These are a complementary asset developed primarily from the gaming industry, but now sit in the centre of the AI revolution. GPUs mean that we can use far higher performance user interface technology. So, yes our screen might stay flat for a while, but they need not simply render a flat map anymore. Further, when we consider that Meta’s glasses have not been as throughly rejected as Google’s initial attempts at augmented reality glasses. Perhaps, the timing for this kind of technology has come.

With glasses, the resulting data will clearly not be flat, and it certainly won’t be viewed from a planimetric perspective. So where does that leave the core GIS/Geospatial activity of “reprojecting”? Indeed, if we have semi-accurate representations of our globe, do we need to at all? Even if we do need to reproject, can higher performance computing negate the need for the storage of multiple versions of the same data?

And note the use of the phrase, planimetric mapping. When will maps start to make more sense projected from the users perspective?

Again, this note is less of a series of answers. Instead I want to trigger your reflection on the processes we do every day. Which have we taken for granted that might be completely unnecessary or limiting.

The development of complementary assets around us and their subsequent new capabilities might allow our community to move in a series of new directions or leapfrog cumbersome and aging human processes. We should let them!

Arrive-Can’t

If you’ve followed the Canadian news in the last few years, you will likely have heard about the ArriveCan app procurement debacle. In essence, the Canadian Federal Government paid a contracting organization an astronomical amount of money to build a travel management app during the COVID pandemic. It’s hard to imagine how a company of 5 could spend $54m in 6 months, but that’s what happened. I expect a few late nights burning the midnight caviar were involved.

This piqued my interest because my team has always had a hard time winning Canadian federal government contracts. We have won a couple, but we are not well-designed for them. Generally, the boilerplate in these Requests For Proposals (RFPs) asks for the resume of the actual developers on any project. As a small team of experts, we must keep our team busy, so it’s hard to guarantee that a particular team member will be available for a competitive bid we may or may not win. We’ve always felt that if we won the project but had to change the project team immediately, our customers would think of it as a bait-and-switch. Effectively a corporate lie. So we’ve avoided these projects; much bigger companies or much smaller companies are better designed for these.

Those, and contracting companies.

In fact, contracting companies are the winners in serving the Canadian federal government. This means that most of the Canadian government technology sector serving Ottawa could be described as techno-gig workers.

In many ways, contracting is fine; it’s a low commitment for all. Contractors take on what they want and can write off their expenses as a small business. The companies have minimal fixed staff on payroll while commanding an army of resumes to attach to RFPs.

But this arrangement has resulted in a dearth of major Canadian primes because, in techno-gig land, no one retains any meaningful IP. Without that IP, the contracting companies never do anything other than rent resumes to take on roles. One could argue that this simple accident of boilerplate has resulted in a structure that directly impacts Canadian productivity. Government spending is one of the major levers of fiscal policy. If that lever never creates IP, then it has no long-tail impact, and Canadian productivity is left in neutral.

I have always felt that investing in staff development is a more respectful employee-employer relationship, allowing my team at Sparkgeo to build both human and intellectual capital.

So, in Canada, we have a government procurement problem. That’s no real surprise. But for the sake of this discussion, it means we have few major defence primes. In this industry article highlighting the Canadian defence industry punching above it’s weight, all the six listed top companies are foreign. MDA is a notable exception to this pattern.

As usual, there are two perfect times to solve this problem:

1) Twenty years ago.

2) Now.

Luckily, the brand new Defence Investment Agency and the Canada Defence Procurement Readiness Program were both announced this week. Notably, these focus on defence procurement, not the Canadian Government. Hopefully, with some success, patterns will echo throughout the institution.

Mercator’s conceit

In Canada, we generally assume that our only border of concern is that with the US to the South or West (yeah, Alaska, remember?) Mercator inadvertently taught us** to think East and West. But in many ways, we also share a border with Russia, not to our far West (so far it’s almost East), but to our North. The Canadian Northern border is absolutely enormous, and it’s largely empty. There are some critical Northern First Nations and Inuit communities, but the vastness and ruggedness of this area cannot be understated. It’s also ripping cold, or at least it was.

Because of the nature of this area, one could say that our backyard is somewhat unfenced. For the last fifty years, that was largely fine. We’ve had the Dew Line in case things got really bad, but we are still here, so the gambit of mutually assured destruction seems to have paid off. But now, our political and environmental landscapes have changed. We have a belligerent Russia, we have an opening Northwest Passage, and we have a resource-rich North defrosting. Our backyard has become much more attractive.

Side Note: The Panama Canal is getting shallower as less fresh water is available from the surrounding forested mountains and lakes to keep the canal full. Less precipitation in the surrounding rainforests is having a drastic effect. This will ultimately be an economic disaster for Panama. As that route becomes less viable, the Northwest Passage will be enormously attractive as the quickest route from Europe to Asia.

While Canadian sovereignty has been of deep concern recently, border management is not the only concern in the North. A new climate situation is slowly emerging from the melting Northern permafrost. The methane bomb, which has been forecasted, may or may not cause atmospheric havoc, depending on the area's greening. The fact is, neither scientists nor politicians know what will happen with such an enormous landscape change. What we do know is that we cannot monitor this vast, rugged area using human resources alone.

To compound the above issues, the Canadian North is also a literal gold mine for resources, which could provide well-paid jobs and a secure, independent economic future for Canada. Resources have always been a question of where. A very geographic activity balancing the costs of extraction and logistics against the price of a commodity. That said, the ethical extraction of resources is a hugely contentious issue, yet a pathway to a secure future for our country. Canada is one of the few nations that can genuinely consider the ethical extraction of resources within the context of indigenous communities, environmental management, and broader macro-economic pressures. Indeed, the wealth of a nation is more often than not related directly to the availability of natural resources. This kind of inventory of resources should be considered holistically because, while the Canadian North is not an easy place to reach or operate in, it is possible, and there is a wealth of opportunity.

Finding a needle in a haystack of needles

We have thousands of sensors in various orbits around our planet—far more than ever before. The sheer sensory capability we have access to today, even commercially, is stunning. Petabytes of imagery tumble out of Low Earth Orbit every day. We also have this notion of infinite compute. Obviously, that’s hyperbolic, but in practical terms, we have more storage and compute capability available than any of us is willing to pay for.

In fact, beyond Smart Space and The Cloud, we have at least six critical complementary assets that we can put to work. These assets can be combined in various patterns to build modern geospatial applications.

In fact, given that far more pixels are flowing out of the sky than any organization has the available eyes to examine, we need to leverage as much AI and compute capability as possible to understand our dynamic Earth. If we want to find any needles in this growing haystack of needles, we need to put the machines to work.

Knotty problem

So, we have several threads which we can consider in parallel.

• We have a vast, rugged, remote area which is of increasing value.

• That increasingly valuable asset is subject to an increasingly inhospitable political environment.

• We have a procurement system which has been identified as problematic, thus creating political interest in demonstrating an alternative (sunnier?) way.

• Critically, we have a technology environment that can be used to tie these threads together.

Not discussed above, but relevant:

• Canada has been under increasing pressure to meet NATO commitments, which is hard to do without those major Canadian defence primes to buy things from.

• The Canadian Defence budget has just been bumped up.

• Defence tech is hot.

There seems to be a unique opportunity to think about AI to better understand our enormous and dynamic land. In fact a combination of Earth observation and AI is the only way to do this.

But what does a venture like this need to look like? Is this venture investable? What kind of organization could build or operate this system? Should it be a company, or built by Canadian Government staff developers?

To define these options, we should consider the mechanics of an operation like this and what organizations would be willing to pay for some kind of Northern monitoring capability. Clearly, the Canadian Government would have an interest (that could be a combination of Canadian Border Services, the Coast Guard, the Territories, and possibly the Department of National Defence). Additionally, any shipping or shipping-related organizations would have an interest. Insurance and resource companies will also be motivated to understand ice presence or absence.

But critically, each of these organizations will demand a slightly different products, sculpted from a common monitoring service. These different products could be actuary reports, alert emails, anomaly maps, ice maps, and routing information wrapped up into PDF documents, emails, web maps and Application Programming Interface (API) endpoints. Each product demands a different complexity, time horizon, market positioning, sales structure, cost, and price.

The idea of a series of products emerging from a single landscape engine makes me immediately consider a geospatial foundation model as the base technology to feed a Northern monitoring capability. Powered by modern geospatial technology, one single monitoring capability, a Northern Eye if you like, could be leveraged to address a series of common problems within a particular policy, climate and political environment. This deep horizontal is a great example of how both the Geospatial and AI communities mirror and indeed reflect each other.

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* I still say thank you to Siri

** euro-centric reductionists, see Transitions

Today, we still consult with the sky, asking questions about what it sees. While many still look out, our geospatial and Earth observation community, use orbital sensors to look back down at ourselves. These sensors are the bringers of truth. Looking down from the heavens on us and the world we have sculpted. That truth is only demeaned by humanity's crass interpretations of those pixels. While those sensors, in the silence of orbital velocity, continue to capture, record, and transmit… Capture, record, and transmit…

There is art in bringing the stories of our landscapes to people. Those stories are of colours we can and can’t see. Of measuring the beauty of nature through the electromagnetic reflection of our sun by dirt, rock, or leaf. Of the steaming emisivities of our planet and its industry. And, there is science in measuring the pulsing vitality of our home.

But I fear our community of planetary voyeurs has not yet even touched the subject of truth. Across the media spectrum, we are immersed in imagery of various sorts, and we are increasingly challenged with the question of what is real, what is true, and what is artificial.

Of course, what is real and what is true are questions more of philosophy and interpretation. Truth, as a concept, is never reached. There is always an interpretation in the definition of truth. Interestingly, while truth is impossible to achieve, falsehoods are easy. The negative is much easier to determine than the positive. This is true of business strategy as it is of morals. It’s often easier to know what not to do, and with the negative as a guide, we can chart a path towards the positive.

A phenomenon‘s reality is yet another interpretation. How would we determine reality? As humans, witnessing that phenomenon would be the most visceral method. Can you see it? Can you smell, touch, or hear it? Can we use devices to measure it? I can’t see a muon, but I can build a muon detector, so they must exist… right?

Maybe, but do we trust our devices? Are we willing to trust the nuance of scientific instrumentation? Usually. If I go running with my family, our GPS watches will each have different readings. This is not a digital maliciousness insisting that I run an additional 253m to get to the same distance as the others. Instead, it’s the technical mirage of geographic accuracy. 10 Km for one watch is not 10 km for another. Of course, we can now augment our realities with information overlaid on our senses to provide entertainment or information. But are we augmenting with information, or opinion? So reality can be measured within the context of sensory perception and accuracy.

What we have not tackled as a geospatial community is the artificial. Most of our community uses artificial intelligence for interpretation purposes. We are familiar with the creation of neural networks and now we are highly motivated by foundation models and embeddings. But, what I fear is an inevitability is the emergence of deep fakes from space? When will we consult the sky and have to wonder who those devices are serving, and if the array of pixels presented reflects an existing or artificial landscapes?

The provenance of a pixel is mostly untested. But this subject will become increasingly complex as our global community starts to push against the constraints of accepted international borders. Who gets to write the histories in real time? In a very real sense, our geospatial community is empowered and responsible for reporting on the changes we see. While not journalists, we are custodians of a digital journal, reflections of time, ultimately a flip-book of human activity. When future communities ask “what happened?” our community will have provided the eyewitness account.

So, when we are challenged with an alternative image, how can we know what or who is right? Can AI be used to write a different history? Certainly. How can we defend against that inevitability? What is effective digital provenance.

Provenance can happen in several ways. We could digitally encrypt at the source, or use some kind of blockchain to create a chain of custody. In some ways this kind of activity would be operationally beneficial too. How often is a pixel abused in the preprocessing of an image. It can be squashed and stretched in geographic reprojection, and radiometrically teased during atmospheric correction. While these processes make the product more functional and representative, they also represent a edit to the original image.

Another consideration is that any argument or interpretation benefits from listening to more than one opinion. With that simple concept in mind, perhaps more than one source of data should be sought when trying to interpret a landscape phenomena. This simple sounding statement hides a multitude of technical complexities and cost. But these complexities must be meaningfully addressed by our community anyway.

We will need to address harmonization of engineering and data. Having a common understanding of time and space would be a good start. I have often observed that the EO sector has always been too sensor rather than location obsessed. The net result of this is that comparing images taken at different times from the same sensor rarely “line up” in geographic or radiometric spaces. If we were modelling the planet and had a common understanding of its shape (perhaps some sort of discrete grid system), the changes would be more easily consumed. If we can’t even have imagery from the same sensor line up, think of the challenges of different sensors, different engineering teams, different countries and even different phenologies of data.

Time also is a critical component of this model. Sensors are passing locations at different times so there will almost certainly be discontinuities in the content of each image or data source.

And, then there is the simple barrier of cost. More images imply a higher cost. Even if the data sources are open, there is a processing and storage cost to be considered.

Another consideration is that each image or data source is typically considered in isolation. I suppose this is a remnant of the sensor-approach, suggested earlier. But if we consider that every image is an image of a place, and that place has both a history and a future, then a particular image can be proven or disproven by the history and future of that location. Of course some events are deeply temporal, such as conflicts, protests, or sporting events, but even so, these events can often leave a signature on their landscapes.

So time itself has some built-in provenance if we consider the geospatial community as custodians of a history of our Planet’s surface. So, the final technical opportunity is to build a living digital twin of our planet, fed with new Earth observation data.

This, then is the hope I have for geospatial foundation models: in a time of deep fakes we can develop a series of models, based on agreed representations of our Planet, that will consume new data sources as they become available to act as one source of information on global human activity.

Because right next to the word truth, is the word trust.

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Burlington was a beautiful location, providing both fertile soil for growing ideas and a blank page on which to sketch rough narratives.

In my role, I was asked to paint a picture of the future. I based my thoughts on the poem below. Both setting a scene and then exploring a world where technical difficulties had been diminished.

Petabytes of data flow through the thin skin of our atmosphere every day. Millions of pixels with their associated ephemeris report on the state of our landscapes when assembled. Providing a visual reflection our changing planet in almost real time.

But today, we are not.

The Lament of the Lonely Pixel

Captured by a CCD in the sky,

Never looked at by a human eye,

Memory, consumed by unshared reflection.

I want to explore what the final line of this poem really gets at.

While those pixels can be interpreted into measurements and stories of physical risk, vegetation health, supply chain security, ecological change, and urban development, broad adoption has been troubled.

The Lament of the Lonely Pixel is a poem that attempts to capture a sense of quiet inertia, pent-up potential energy, and opportunity.

Memory is a snapshot of time. Time is a critical and deeply unrenewable commodity. We must protect our time and the storage of those fragile memories. Memory also signifies the digital space a pixel consumes. The consumption of memory reminds us of the constrained nature of our computing and biological systems. These constraints are a practical truth for satellites, but also a broader truth for the digital hoarding our species is over-occupied with. Beyond constraints, memory is also temporal, especially for those systems limited by biology.

So, while time flies, time also robs us of memory. If those memories are unshared, then an opportunity is missed.

If something is unshared, perhaps selfishness has barred its distribution, or perhaps it’s been forgotten. Unshared implies the negative of shared, suggesting that not sharing is more purposeful than neglectful. Unshared is important because, in many ways, a pixel is something to be shared as much as seen. A pixel can be used for numerous purposes; it can be regarded, analyzed, and used to determine a landscape’s features and changes. More so, with concerns over data equity, a shared pixel is a higher-value product. A businessperson will say that capturing once and selling or using a pixel many times is more profitable, and they are right. Those are great unit economics.

But the usage of a pixel to build a deeper understanding of our planet is also key. Because as a place on our planet reflecting that special wavelength, that signature of substance, we know that each and every location is involved in innumerable planetary processes. Every pixel must be shared as it describes part of the system of the everyday climate of our lives.

That pixel is a single measurement of the beauty of nature through the electromagnetic reflection of our sun by dirt, rock, or leaf, witnessed by a camera in the sky. That reflection is a part of the primordial complexity of the everyday. As a personal reflection, this word reminds us to consider our planet and the waste, the abandonment of digital detritus.

The reflection of a complex digital capability consuming space while not being used feels like an important message for a population addicted to consumption.

Because, while every pixel is a problem to solve, it is also part of its own solution.

This poem could be a woeful dystopian view on digital waste, but I feel it’s a call to action for focus and action. Even if those pixels are not being used today, they could be used tomorrow. We have an opportunity to create systems that can put these pixels to work. While there are numerous technical and business model barriers to overcome, there is a clear opportunity in the creation of a living map of the activity on our Planet’s surface.

Given we now have this digital mine of planetary knowledge, how can we explore it for valuable signatures? Those digital ore bodies. How can we make every pixel as useful as it should be, as part of the tangle of landscape processes?

On reflection, I feel the Geospatial Foundation Models presently being built by IBM (TeraMind, Prithvi), ClayAI, LGND, NASA, and ESA are an excellent start. While not perfect yet, these efforts are absolutely critical for the advancement of our community. If the Geospatial community is to build a digital twin of our planet, then these foundation models are undoubtedly the nucleus of that activity.

Thanks to the See Change Sessions team (especially Bret and Julian) for inviting me.

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In the MIT study we discussed yesterday, there is a note about an emerging “shadow AI economy.” This is a simple idea that individuals were using AI to accelerate personal workflows. This is a counterpoint to how those individuals might have been using the pilot GenAI workflows designed corporately. A number of reasons were suggested for this. One was the personalization that individuals had put into their own AI experiences. In essence, their prompts were vibing with the individuals more, and as a result, giving better responses. Another might be that the corporate experiences lacked the foundational depth of experience that the consumer tools have. Or for whatever reason, the corporate experiences were a little clunky and forced the user into a new and uncomfortable workflow.

Any IT manager would be horrified by this, knowing they do not control any sensitive corporate IP or private data that might be discussed with the consumer AI in question. They might be so concerned that they ask ChatGPT for advice on handling this situation…

This is another parallel with the geospatial world. While often provided with corporate routing or mapping tools, how often do individuals drop to Google Maps because their corporate toolkit lacks data recency or a decent user interface? Trust me when I say this is a common situation. I’ve seen it in logistics, municipal/civic environments, defence and intelligence applications, and insurance workplaces. Individuals in some of the most sensitive organizations will drop to Google or other mapping providers when necessary because they need to execute.

So, there is a shadow economy in Geospatial, too. However, the transaction here is not overtly financial; instead, it’s one of data access and acquisition. Google knows where individuals are looking, and that feeds its revenue streams. Given the lack of financial barrier, perhaps this becomes even more tempting? There is also a very grey area around licensing and derivative products created from a Google base map. No, I’m not going to cite any sources on that.

This is all to say that the comparisons between the deeply horizontal AI and geospatial economies are startling.

Summer is a time for forced reflection. When seemingly everyone is taking the time for holidays and long weekends, the space that would be filled with calls instead is filled with consideration.

A focal point of my reflection is always the place of geography within society. With geospatial technology as the digital expression of geography. Whether in consumer applications, such as Google Maps or Strava, or in more industrial uses of geography for logistics or natural resource management. I care about how geography is used. Partly, this is due to the complex mysteries that geography constantly presents, but also because of geography’s ability to connect data and experiences through space and time. I’ve often referred to geospatial as a deep horizontal, because location is often a component of consumer and commercial workflows, but rarely the whole story. While the deep horizontal is an opportunity for geospatial technology to be everywhere, this is also one of geospatial’s greatest problems for broad adoption or even in defining geospatial or GIS as an industry.

Because it’s not, geospatial is a community of practice, precisely because it is everywhere.

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Geospatial data and technology should not be siloed; it works much more effectively when built into workflows. However, the practices that have developed around GIS have become siloed, and this separation becomes a needless battle in many enterprises. I’ve talked ad nauseam about these subjects for years. There is nothing new there. My supposition is that in most business situations, decision-making is almost always made better with robust and accurate geographic technology. That geospatial technology must be embedded into workflows so profoundly that it is virtually invisible. This is also the nub of the geospatial product problem. So, while geography is rarely the whole solution to a problem, it’s almost always a component, a consideration. This is what I think of as a deep horizontal: an activity that could be a valuable component in numerous applications, but without workflow or domain context, may not be helpful in and of itself.

Interestingly, I now see the same patterns in the AI community, particularly in GenAI. AI is also a deep horizontal. It is rarely the whole solution; instead, it can help to speed up or optimize parts of a workflow.

This is borne out in one recent publication by MIT and one older article by a16z. MIT’s study suggests that 95% of GenAI pilots in enterprises are failing to produce any meaningful economic benefit. The reasons for this abject failure are multiple. There are common themes: brittle workflows, lack of contextual learning, and misalignment with day-to-day operations. Seemingly, the real benefits are for individuals using the common consumer-grade tools: ChatGPT, etc, to do things faster. These are not being used within the corporate environment but instead on personal accounts outside the control of corporate IT and any security measures. This is referred to as a shadow AI economy. So though the article is damming of GenAI pilots, it’s really more of a battle between corporate and consumer tools.

In the second article, a16z, back in 2020, said that the AI economy would need to be supported by services. Indeed, most robust AI implementations would need a human team of experts to create appropriate workflows. Why? because almost every robust enterprise use case would be a bit custom. For a16z to say services matter is quite a thing. Traditionally, mixing services into the SAAS revenue stream would be anathema, and frankly, frowned upon by most traditional Valley investors. Fixed cost people really screw up the software economics, and any sniff of a software company behaving like a consulting company in disguise would damage their valuation enormously. For reference, software companies can be valued at 10-40x revenue, whereas services companies are lucky to be valued at 1.5x revenue! So for a16z, one of the most “Valley” of the Silicon Valley investment giants to say “services will matter for AI” was big and interesting.

That a16z article was prescient. MIT agrees that strategic partners are the way to go. Companies building AI tools internally were failing to even finish the products, while bringing in teams to help provided instant expertise, those enterprise pilots that did succeed were built through strategic partners.

Finally, OpenAI has just announced a Palantir-style services team, indicating that the biggest consumer-oriented AI company sees the problem too. This transition is happening in real time. Palantir has always valued the concept of the “forward-deployed engineer, " which, to you and me, is an engineer running their software on behalf of a customer. However, the ability of this individual to provide value to the customer and then go back to the product company they serve is undeniable. You could also call this staff augmentation, or a managed service.

Or simply, a services-led product strategy.

As we move towards a future of geospatial foundation models, where the value still needs to be properly quantified*, we will see a similar pattern. Humans will be necessary to sculpt the commercial workflows to ensure operational alignment. Even with the traditional geospatial technologies we have today, greater use of services teams would create a better return for the EO pilot investment. Sigh, yes, we are still doing those.

Clearly, geospatial technology products, whether it’s Earth observation, data platforms, location tech, routing, or GeoAI** could all benefit from a services strategy. To some extent, our friends at Esri have always approached their market with a serious consulting component to their revenue mix. As a private company, they have always been less prone to the foibles of market valuation.

*To be clear, I believe GFMs to be an inevitability. They WILL be the way to go at some point. At this point, they are not ready for deeply commercial use cases. But the present innovative activity is absolutely necessary, and our entire community will benefit from continued and vigorous experimentation. Valuable use cases will emerge. At Sparkgeo, we are certainly investing time and effort in GFMs.

**My own personal (dystopian) Idaho, the name, not the practice!

This post is a scattergun of thoughts inspired by these two events. For the HLEG, I was lucky enough to act as a rapporteur and facilitator for both a tech and innovation session and two of the three deep dives. As a result, I have a tremendous amount of notes and photos of slides. If you want more specific observations, please comment or reach out directly.

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Given that this event was subject to the Chatham House Rule, all specific commentary is anonymized, and I will try to provide an overview of the tone. My thoughts are focused, as you might expect, more on technology and execution than on policy. I’m staying in my lane for now!

1. Geospatial data is a mess. Maybe not locally, but globally. What I mean by that is that different organizations store and manage data differently. With various standards and different products managed by disparate procedures. We heard stories of files being a pain for small companies, open data portals, and numerous sensors capturing multiple phenologies of data pertaining to single locations, leading to data management and harmonization issues (nightmares?)

   We’ve heard about too much data in some places, but data gaps in others. There is a demand to move from fragmentation to more holistic data management and de-siloing. However, while the word “federation” is used frequently, in practice, this often just means replication. While there is value in redundancy, too much replication is a source of both error, cost, power, compute, and intellectual inefficiency.

   The notion of data federation is interesting. On reflection, I do wonder if a reference to where data is published would be the cloud-native way—in effect, an API proxy. How many times across the web has the Sentinel archive been replicated? Some redundancy is probably helpful and even prudent, but how much redundancy is too much?

   This reminds me of the lament of the lonely pixel, just repeated numerous times.

2. We heard the fearsome phrase, “the freedom to lie.” This is the first time I have heard this observation, and I mentally categorize it alongside the words “alternative truth.”

   From an Earth Observation (EO) perspective, I am filled with a sense of dread. I don’t believe we are equipped for the increasing anti-science rhetoric, bots, and deepfakes that we must undoubtedly contend with. In my naivety, I feel our community has largely been immune to or ignored by nefarious manipulation. But that will almost certainly change. How do we ensure that a pixel captured is the truth we expect it to be? Is there a technical solution scalable enough for pixel or vertex provenance? Is there a human solution to the issue of trust?

   This question can also be considered from the more general geospatial perspective of data processing. What tools do we have for holding the history of data preparation or algorithmic application? This may become increasingly important as we see more Artificial Intelligence (AI) tools and as we note the difference between data and applied information derived from that data. It will become increasingly important to be able to document what has happened to pixels. Regulators within the financial sector will demand this, and those motivated by transparency will also need to see this algorithmic chain of custody.

3. Consider the technical opportunities and challenges of multi-scale, multi-sensor, multi-source data acquisition, including hyperspectral (with its extra-dense spectral load). We have an astonishing data flow from the surface, sky, and space. This growing torrent of data leads me to consider the use of Geospatial Foundation Models (GFMs) to discover hidden spectral signatures in the stacks of pixels and points we collect. Yet, the black-boxish nature of GFMs concerns me, certainly given the notes on transparency and algorithmic custodianship above. However, traditional methods may simply be too slow and specific to account for the sheer volume of monitoring data available.

4. Yet again, I am left to think about data harmonization. It’s worth briefly considering data standards again. But with all these sensors, how can we not?

   Standards, standards, standards… sigh.

   In particular, considering whether our approach to standards has been sensor-centric rather than phenomena-centric. For instance, when we think about the EO activities, do we actually care about the images? Do we care about the pixels, an image, or a strip? The swath of an image is defined by the sensor, not by the landscape reflected in those pixels. In the end, do we care about the sensor or the location? While an image captured can support numerous derived products, it is rarely the final product. The image is more of an unrefined compound, awaiting fractional distillation into information. I’ve said this before, but every pixel is, in fact, a problem to solve. That problem will change within the context of geography and an expert’s application. But it’s still a problem.

   So, should our standards be for the image/sensor or the measured phenomena? Come to think of it, is Overture’s GERS model an example of this? Clearly, this is not for EO, but more generally for map data.

5. From a technology and innovation perspective, we often focus on numbers in arrays or databases, but how do we incorporate qualitative information? How do we consider Indigenous stories, for instance?

   How can we build any sense of data equity without considering additional, qualitative data sources? Indeed, we could also note that social data is a form of qualitative data; on reflection, there are some interesting examples of social data being put to work (Danti comes to mind).

6. I have spent the last two weeks discussing new sensors and data sources. All these crucial innovations require significant compute, infrastructure, and intellectual capital. There is a serious question of power consumption and technical capacity/capability that needs to be considered.

7. Finally, for the last few years, I have been hearing more and more about eDNA. This is an interesting tool for ground truth in biodiversity data. If this could be pulled into a citizen science, crowd-sourced environment, we could build a “23 and me for the planet”…?

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8. Finally, and perhaps most importantly, data being fit for purpose was mentioned time and time again. This is an interesting comment; on reflection, data usually sits on a spectrum of suitability for different purposes. Different spectral, spatial and temporal characteristics might change where a data product sits on that spectrum for the measurement of a phenomenon. Given it takes a few years to become acquainted with EO and geospatial data, I bet a co-pilot for data suitability would be very handy for certain communities with less access to deep technical experts. Helping new users (and old, for that matter) understand the limitations of different data products within the context of a proposed use case or algorithm could ensure known limitations are considered up front. Perhaps even packaging a sense of provenance.

Each of these topics is worthy of further exploration, and I am sure I will explore each and the connective tissue between them in the coming months.

The Frascati meeting ended with the collation of a series of principles and pathways, the Frascati Principles, which I will talk about in a future post.

PS. If you get the chance to visit ESRIN, the ESA facility outside Rome, it is a great trip, go!

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Every headline now has a latitude and longitude. Wildfires darken skies half a continent away, supply chains reroute in hours, and satellite images expose current events before diplomats can draft statements. In this high-velocity world, whoever understands “where” outpaces the competition, protects communities, and shapes policy. Geospatial is no longer a specialist’s hobby; it is a critical input to decision-making. This document, a “GEOFESTO*,” lays out the principles and emerging trends that let practitioners ride the wave instead of drowning beneath it.

The Calgary Eleven: Kurtis Broda**, Jon Neufeld, Tammy Peterson, Tee Barr, Ben Tuttle, Cade Justad-Sandberg, Kyle Ryan, Sarah Pryor, Andrew House, Will Cadell, Sam Rondeel

By adopting these principles, geospatial practitioners can both enhance their community of practice and drive geospatial technology into a better and bold future.

The GEOFESTO

A call to action for the digital geographer of tomorrow.

I. Educate to Make Spatial Literacy Universal

Geo isn’t a specialty but a core way of reasoning about finance, climate, supply chains, and society. Embed it in curricula, metrics, and boardroom dashboards.

II. Choose Collaboration Over Silos

Walled gardens and traditionally siloed GIS offices waste insight, while teams communicating openly are effective.

III. Open Standards Create Opportunity

Open data and model standards invite idea collisions, turbo‑charge scale, and unleash AI innovation the moment a new sensor comes online.

IV. Geospatial as an Approach, Not a Solution

Jargon‑laden “solutioneering” alienate would‑be users. Lead with plain‑language workflows that hide complexity and spotlight outcomes.

V. Obsess Over Outcomes

We measure our successes by solving problems rather than by building technologies.

Today’s Trends

Why are these important? In 2025, geospatial technology is changing at a rapid pace, and the following trends will define the next era.

1. Spatial information is becoming more important for business intelligence

Geography is not a niche skill but a foundational way of reasoning about the world.

Smartphones, drones, and devices stream location data. Organizations that weave spatial thinking into strategy see patterns rivals miss. Leaders now realize that every KPI hides a “where.” When teams map those hidden relationships, they cut blind spots and find new value. Spatial analysis no longer sits on the fringe; it powers business intelligence.

Humanity’s biggest challenges are spatially located. From climate change to geopolitics, only by understanding where can we understand the critical path forward.

2. From Analysis‑Ready to Decision‑Ready Data

The geospatial community has spent a decade celebrating analysis-ready data, yet end‑users still wrestle with last‑mile analytics.

Decision-ready data will close that gap. That means distilling insights and context so end-users can go straight from data to action. The concept builds on efforts in remote sensing where imagery is pre-processed as Analysis-Ready Data; now, we’re taking it further across the geospatial field. Think of disaster managers getting a flood impact map with population and economic loss estimates instead of just satellite photos. Driving this trend is the need for speed and clarity. In a fast-paced world, few have time to wrangle data or interpret complex charts.

3. Collaboration Beats Silos

Walled gardens hinder fusion; open standards catalyze idea collisions.

The geospatial field has long suffered from data silos caused by agencies and companies guarding data or working in isolation. That’s changing fast, out of both necessity and opportunity. Complex problems like pandemics and climate risks don’t respect organizational boundaries, and solving them requires pooling expertise and datasets. When GIS teams operate in silos, their impact is limited. Open collaboration multiplies the value of spatial data open standards like STAC for sharing imagery, and consortia where government, academia, and industry jointly build mapping solutions. This trend is gaining momentum now thanks to cloud infrastructure and APIs that make data exchange easier, plus a generational change in mindset toward openness. The payoff is huge: better decision-making, less duplication, and the ability to tackle big challenges together.

4. Designing for an AI‑First Era

Do we retrofit AI into legacy toolchains or invent fresh, prompt‑driven experiences that bypass traditional GIS GUIs?

The traditional GIS (Geographic Information System) has been a powerhouse, but it’s historically tied to complex software interfaces and expert operators. In 2025, we are looking at a radically new paradigm of AI-first geospatial workflows. Large language models and other AI can now interpret natural language and generate maps or analyses on the fly. This means a researcher or city official might simply describe a problem or ask a question, and the AI-powered system will handle the GIS tasks under the hood such as data retrieval, analysis, and visualization without the user clicking through menus or writing code.

There’s also a democratizing impulse: to make spatial analysis accessible to anyone who needs it, not just those with specialized training. An AI-first workflow lowers the barrier so a hydrologist, epidemiologist, or journalist can get spatial insights without being a GIS guru. This doesn’t spell the end of the GIS professional; rather, it augments experts’ capabilities and frees them from rote tasks. It’s GIS “after the GUI,” where AI agents handle the heavy lifting (and even some expert reasoning). Imagine vastly shorter analysis cycles and increased innovation as more people can play with geospatial questions.

5. Geopolitics of Pixels: Sovereignty, Security, and the New Spatial Order

Platform sovereignty, export controls, and the slide toward nationalized constellations. This trend is a direct threat to open collaboration and breaking down of silos.

Geospatial technology is deeply entwined with questions of national sovereignty and security.

During Russia’s 2022 invasion of Ukraine, ubiquitous satellite imagery empowered a smaller nation and the global public to hold a superpower to account in what has become the most “documented” conflict in history. Nations are taking note, and are asserting control over mapping within their borders. Many countries worry that unfettered open data could expose critical infrastructure or strategic vulnerabilities, prompting debates about limiting certain datasets.

We are entering a new spatial order where owning your satellites, your mapping platforms, and your spatial data repositories is seen as vital as owning your land. Even private companies like map providers find themselves navigating international sensitivities, such as differing border representations and data localization laws. In this landscape, “pixels” (satellite images, map tiles) carry diplomatic weight. Control over them means control over narratives and strategic insights.

The Beginning

Geospatial’s moment is now: location data sits at the heart of decision and AI rips away old interface barriers. And sovereign pixels redraw power maps. The GEOFESTO calls us to meet that moment. To think spatially by default, share before siloing, codify open standards, speak outcomes not acronyms, and measure impact in lives improved, risks reduced, forests saved. If practitioners, vendors, academics, and policymakers embrace these principles, we don’t merely keep pace with change; we shape it and help the world move forward.

These are not rules by any means but more considerations. A philosophy of modern geospatial.

*Paying homage to our community’s insistence on adding the prefix “geo” to almost any word. Note that Sparkgeo cleverly uses a suffix, which is quite different.

**Special thanks to Kurtis Broda for assembling our notes.

Is this a case of AI taking our jobs? Yeah it is, and I’m all for it

This week, I was lucky enough to be in Calgary for a small meeting of geospatial minds. Because N51 has been postponed due to “complicated international circumstances,” a small number decided to still meet up, as a consolation prize, so to speak. This has resulted in a short white paper which is still being edited and will be published soon. While I don’t want to steal our “geo-festo’s” thunder, three components of that discussion were:

1. The role of AI in the future of geospatial activities,

2. The notion of the horseless carriage as it pertains to product development, and

3. What an AI-first GIS or geospatial platform might look like.

Firstly, I am being careful to avoid the term “geospatial industry,” because I don’t think that exists, anymore. Perhaps it once did, but now, I see a community of practice. A community of individuals from a variety of backgrounds that apply the key characteristic or attribute of spatial thinking to business, human, or organizational problems. Forgive me, if I sometimes lapse and use the phrase geospatial industry, that’s usually out of laziness, or a need to help describe a concept to an insider. Probably laziness, though.

So, when we think about the geospatial community of practice, the suite of tools in use are wide and interesting. Many of those tools are introducing an AI chat-bot of some sort. We should also highlight that many of us have been using various AI practices in geospatial activities for decades. But, that does not diminish the huge and, thus far somewhat nascent, power that language and now geospatial based models can provide.

But, the addition of a geo-buddy, or a carto-copilot is exactly the problem of the horseless carriage. If a language bot is just added to a pre-existing workflow, then I would argue that AI is not being sensibly used. The horseless carriages as described here, refer to the design of the first cars. They initially looked like “horseless carriages,” meaning that instead of a new vehicle being built around the combustion engine, the combustion engine was just added to the vehicle that was already best understood by the market - the horse drawn carriage. If AI is the new geospatial compute engine, then I am curious to see the new breed of AI-first geospatial tools will look like, probably not like the geospatial platforms of today (or, yesterday, I guess.)

Would an AI-first geospatial platform ever have to think about reprojections, or would it just understand the difference between flat surface and ellipsoids? Like my phone crossing timezones, would it just know? A better question, why wouldn’t it just know? If an AI-first geospatial platform “just knows” where data is, would it ever have to worry about ETL (Extract, Transform, Load)? By that, I mean, couldn’t it just look at a data set and see what format it is then ingest it to some known common storage unit? If we can assume relatively open standards, couldn’t an AI-first geospatial platform just collide datasets together and know if/when/why a union is better than an intersection for disparate data? Indeed, could those traditional analysis techniques just happen in memory per user request? Will layer_UTM13_tmp1_final_final1.shp ever be necessary again?

Quick aside: there are already some able, modern and AI-powered geospatial tools. I would point the reader at Felt, Atlas, Ellipsis Drive, which have all come up in conversations recently, and are worth a look.

If an AI-first geospatial platform could reproject and run ETL routines, then what impact does that have on the geospatial community of practice? Specifically, what will the GIS (Geographic Information System) technician do? Experienced geospatial and GIS people respond to me that data will always need to be cleaned on some level, but will it? Then they respond that LLMs (Large Language Models) hallucinate, but humans make mistakes too, sometimes on purpose*. I can still see the need for some bespoke analysis, but we ask language models to do more and more complex tasks, so why couldn’t ChatGPT find my closest Starbucks, count cars in a parking lot, or track a snow plough? In fact, I bet with a combination of Lovable, and ChatGPT I could build that app today. But why, except to create an amusing collage of geospatial’s biggest cliches via a text prompt?

The point here is not to flip the table and say we’re done. We are most assuredly not. Firstly, we still need to build all these AI-first tools, for the next decade we’ll all be deeply involved in that activity. But more broadly, it’s to say we should look further out in time and zoom further out in approach. For our community, the concept of spatial thinking has always been the central value proposition. That has not, and will not, change. Mr Marshall is right, there is tremendous Power in Geography and Mr Dangermond’s Science of Where is also a telling message. That said, I do wonder if our community should be less focused on the how of geospatial and more on the why of geospatial.

I like to say that “Investing in data is like buying a house while investing in technology is like buying a car.” This is as true today with the AI-economy as it ever was with the web-economy. Data will always be critical; it has a cost and should be cared for. Technology, however, will (and should) evolve through innovation. You must invest in both, but know where your money is going.

So, yes AI might take our jobs. But it probably won’t take the core benefit of spatial thinking, and in that we can celebrate and thrive.

*I was once working for a forest inventory company in Northern British Columbia. One of the human photo interpreters once told me he would add in “exotic species” to his species classification work, because they were allowed to add up to 5% exotic, and he was bored. So if you look at BC’s Vegetation Resource Inventory and see Monkey Puzzles trees or Bamboo, now you know.

Does AI get bored?

We have a changing climate, with seemingly more extreme events every day. We are seeing increasing international tensions break out into kinetic conflict. The pillars of entrenched economic systems are creaking under the weight of fragile supply chains and nationalistic political agendas—all painted on a canvas of technology with the brushes of AI and a palette of algorithms.

The one continuity in this changing landscape is geography itself. Geography is the framework of space; the scale that we measure change. The geographic industry has always been hidden in plain sight, with mapmakers creating the more prized economic treasures of both ancient and modern Nations.

“Geospatial technology” is the modern expression of geography, encompassing geographic information systems, remote sensing, and surveying technology. It’s a convenient catch-all term for anything with a location component. The problem is that geography is quite literally everywhere, and modern geospatial technology is, in fact, changing everything.

At Sparkgeo, we first used that phase four years ago, and it’s more true now than it was even then. The complementary assets of cloud computing, new space, AI, openness and smart devices have created a new set of capabilities that we have not yet scratched the surface of.

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Yet, so many of our geospatial brethren are siloed into small teams offering limited insights. While geospatial is changing everything, the thing that has not changed is our industry’s ability to describe ourselves - and our value. While geography is changing in front of our eyes, both physically and politically, the very practitioners of the art have found themselves sidelined.

How could this have happened? More importantly, how can we flip the script to take a leading role?

In a Monopoly, Innovation is Outsourced

It’s no surprise to anyone that the Geographic Information Systems (GIS) sector is a practical monopoly. In monopolistic structures, innovation is outsourced to the single dominant vendor. The market receives what is provided by that vendor. This is not quite true for GIS because the open-source community has always tried to keep up with community versions of GIS technology. But in a marketplace where the options are either open source or a single proprietary software vendor, enterprise technical leadership is abdicated to that dominant vendor. To a large extent, this is reasonable, as that vendor is collecting most of the revenue. But, this becomes a problem because innovation functions best with diverse thought and action within the presence of competition. However, in a monopoly, diversity is bad for business, and there is no meaningful competition. Monopolies are a margin play.

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Unfortunately, this leaves the GIS sector in a position where it has difficulty demonstrating value precisely when it needs to be absolutely fighting for its life. Don’t forget that the US federal government is the single biggest buyer of geospatial technology, and I would estimate that most geospatial businesses are at most two degrees of freedom from national (federal) Government work, whether it be the US government or another nation.

GIS is a Silo

At this rate, given the innovation practices we can all see in other sectors adjacent to GIS, I see the entire practice of 2-dimensional planimetric GIS being disrupted. How many drone teams are now in municipal governments? Those teams have natively 4-dimensional web-centric toolkits that are advancing at a lightning pace when compared to the traditional toolkits of the GIS analyst. Those drone teams are creating astonishing amounts of overtly geographic data, which could be stored in a cloud-native manner for instant retrieval. Are those teams interfacing with GIS teams? Frequently, no. They emerge out of infrastructure maintenance and asset management, and because GIS is typically siloed, integration becomes a technical and personnel challenge (nightmare).

Of course, geospatial is not (just) GIS.

I agree; geospatial is broader than GIS, but GIS is a key component. I care deeply about extracting information from geographic systems, but I’ve been lucky enough to have a career that has challenged me to work in multiple operational environments, interfacing with both proprietary and open technologies. Proprietary tools are absolutely necessary for numerous business reasons, but an over-reliance on a single vendor is a tremendous risk for our sector, as is any industrial monoculture.

Create Comfort in Innovation

I am constantly told that enterprises can’t take risks. Having seen many take innovation seriously, I am inclined to believe that it’s people, not enterprises, that are the deciding factor. It is easy to blame a structure, and some structures are certainly more resistant to change than others, but our industry is at an inflection point - an existential crisis, if you will. The geospatial sector must learn to innovate or risk being siloed into obscurity.

Because, if you have difficulty describing your job at a dinner party, you’ll have a much harder time facing the inevitable departments of government efficiency that will permeate most major organizations and companies in the coming months.

Team, I don’t want to be the harbinger of ill tidings but be ready with your five bullets. While innovation comes with risk, it shows the motivation to improve and build.

Beyond the Analyst

While GIS is important to the geospatial landscape, it is an increasingly limiting concept. In many ways, GIS is a single-purpose data exploration and visualization tool. Modern geospatial use cases are moving towards monitoring because data is being served as streams, and time is becoming a first-class citizen. This doesn’t mean your master's degree in GIS is useless, quite the opposite! But you will be challenged to think about additional dimensions. With those dimensions comes interesting problems of visualization, which will change depending on the phenomena being mapped, but might easily not be a map. One could argue that the lack of a map in complex scenarios is why the dominant vendor hasn’t engaged effectively in non-map-based geospatial technology.

Pixel Problems, Analytic Solutions

I’ve long suggested that every pixel or point cloud captured is a problem to solve. The primary use case of EO is still likely situational awareness, for which human eyes are an excellent solution. However, for more scalable applications, algorithms are critical, and increasingly, those are powered by AI. We are now seeing Large Language Models (LLMs) drive specific geospatial foundation models, a workflow that is both deeply modern and fearsome to those used to interpret imagery manually.

As my team will tell you, I am an AI curmudgeon. So, while I see numerous flaws in the above workflow, I am also acutely aware that the speed of innovation in AI is uncomfortably fast. EO today is a problem of software; EO tomorrow will be a problem of AI. But those AIs will need to be trained by increasingly complex models, and that training will be done by experts - geographic experts who are drawing boxes around landscape phenomena. GIS people, I guess? Don’t feel that this is some kind of diminishment, robots are good at doing repeatable things, and we should teach them to do those well. this is just about a sensible division of labour.

Risky Business

While the GIS sector may need an injection of adrenaline, I still contend that geospatial technology is changing everything. Location is being extracted and exploited in almost every software, web property, and smart device. Geography is intrinsically valuable, and geospatial tools measure and report on that value.

• When you pull out your phone and can navigate like a local around almost every location on Earth - that’s geospatial, giving you intrinsic knowledge.

• When you’re delivered with a more competitive insurance quote because your house is further from weather risks or your roof is in good condition - that’s geospatial saving you money.

• When you measure the distance and speed of your run or bike ride - that’s geospatial helping you measure your gains.

These are just personal benefits. The biggest impacts are the ones that you may never see but will most certainly feel: food on our shelves from logistics and agricultural support technology, safer journeys from better weather forecasting, and resilient city planning from climate monitoring.

Geospatial touches almost every industry because almost everything is somewhere!

My advice to anyone in GIS who may be concerned about the vortex of complexity we are working in is to stop and take a look at how geography is in everything we do. Focus on first principles. Take a fearless step out of your echo chamber and engage with other professionals because geography and geographic knowledge are, and always have been, one of the most prized and valuable commodities. How we measure and describe that value may change, but the core of the message remains the same:

Where landscapes change, and people move, there is value - we can measure it.

So, while everything is changing geospatial; geospatial is also changing everything.

Before we get into this article. I wanted to highlight NORTH51, the independent geospatial thought leadership event. N51 is about big ideas, and we have just published our program for this year’s event in April.

With sessions on Space, People, and Oceans, this year at North51 we are exploring ideas around mapping what we cannot or choose not to see. We welcome challenging, respectful discussions in the beautiful Canadian Rockies. Want to take the temperature of the geospatial industry, come and join us.

Time for a change.

I’ve been discussing the sheer amount of data flowing out of Low Earth Orbit (LEO) for years, and that torrent of terabytes has only grown in magnitude. This firehose of data has prompted me to note that the biggest problems in Earth observation (EO), presently*, are in software, not sensors.

What I mean here is that we are amassing an enormous collection of surface reflectance readings organized into arrays that can be projected to model the surface of our planet. These we call images. From these arrays, we deduce either the status of a location or events that may have occurred.

While the status of a location may imply an absolute measurement. The notion of events implies a measurement of change. Given we are recording the surface of our planet at a furious rate, change has become the currency of the EO sector. This brings up three questions.

1. Why are we so poor at visualizing change?

2. Why is time treated so haphazardly?

3. Who is actually selling the product “change”?

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Time must be a first-class citizen in geospatial.

Future geospatial platforms will be four-dimensional by nature. Already, we see reimagined platforms developed in geo-aligned sectors like drones, for instance, outside the more traditional GIS sector, consider the nature of both time and space equally. These sectors are unshackled from decades of planimetric technical and product debt. Free to approach the measurement of our landscapes with an untarnished eye.

But can we do better still? Returning to the two questions posed above, what are the main tools for interrogating change? Well, we have time sliders, and we have animated GIFs. This is a seemingly underwhelming selection of tools. But the central premise here is an assumption of the product. I have assumed that those who want to measure change also want to see that change geographically. This is one of the greatest assumptions in geospatial and GIS. This assumption I believe to be false. But a false assumption that drives a stake into the hearts of most geographers, EO scientists, and satellite builders. What if people don’t want to buy the visually appealing thing I’ve built? What if, instead, they just want to buy the informational product? Remember the Venn of geospatial people?

Gordon Logie, from the Data Science team in Sparkgeo, demonstrates this conundrum in his work on drought in Southern Alberta (part 1, part 2).

He ably moves from video-based visualization (animated GIFs) to graphs. Ultimately, a financial analyst would use the graph for quantitative feedback. However, the initial video was necessary to build the algorithmic trust they needed to make that cognitive commitment.

An interesting and notable approach is evident from Hydrosat’s acquisition of Irriwatch. It uses a simple dashboard to indicate to a farmer which field needs to be watered. This is not a map—most farmers know where their fields are—just a dashboard fed by data from Hydrosat’s constellation. This is a simple and elegant EO-derived product.

Analysis Ready Time.

Analysis Ready Data (ARD) is a hot topic in EO. In essence, the dirty little secret is that different sensors are hard to compare. Unfortunately, this is true in almost every dimension, from geography to radiometry to time. Our industry must continue to build consensus on these dimensions. Thankfully, CEOS’s pragmatic approach to ARD is gaining some traction.

Timed to perfection.

We need that consensus because time in geospatial has every bit as much potential granularity as the XY and Z dimensions. This is as true of the applications as it is of the data collection.

Consider the basic EO product, “situational awareness.” As a snapshot of a location, this product is instantly out of date. When we consider the time an image might take to be delivered to the screen of an analyst or end-user, critical time might have passed. For military operations, this could mean lives; in the financial sector, this could mean profits; and in natural resources, this could mean pollutants. Time in all these applications is critical.

Then, we have the notions of change; change depends on context. Changes can happen slowly “over time” or quickly in “no time.” In both these cases, “time” is not defined, and whether it is slow or quick is dependent on the context of the subject in question. Urban development alone could be fast in Riyadh yet slow in Regina. Not to pick on our friends in Saskatchewan; I just like the alliteration, but things are built very quickly in Riyadh.

The central point here isn’t that “change is a cool product.” It’s that change is a multitude of different products, each with different characteristics determined by time. Additionally, the availability of suitable data is directly affected by our definitions of time. Finally, time is an unstructured dimension, one which we would be wise to consider as deeply as we consider geography. While we think about geographic alignment, do we consider temporal alignment with equal scrutiny?

To be clear, though, change is a cool product. In fact, I believe change is the EO product. The problem is that we so rarely sell it.

Change is time; time is change.

When we have a massive corpus of data, like EO data, a great way to synthesize it is to find useful dimensions along which to organize it. Time allows for that organization, but it also allows for the intermediate inferences to be made. Those inferences are the interpretations we ask our imagery analysts to make for us.

Typically, EO companies have avoided offering overtly change-oriented products. While some have ventured up the value chain, Planet being an excellent example with its planetary variables, few have offered an unabashed change-focused product. Even though, with high repeat imagery, change has always been the product’s manifest destiny. When something is being measured regularly, each absolute measurement becomes irrelevant against the richer understanding of the phenomena’s temporal behaviour.

Consider.

1. Time should always be a first-class citizen in geospatial analysis.

2. We need to be better at building 3 and 4-dimensional visualization products.

3. Measuring geography through time often means we lose the geographical representation, but that’s ok!

4. Change has always been EO’s best product, but we so rarely sell it.

*independent of finding paying customers!

As I have suggested before, this change has been happening incrementally for the last decade*. But on a recent trip to London, I feel that change has now reached something of a tipping point towards geospatial technology being more generally consumed by the finance sector. From what I see, there are a number of drivers.

Caveat: As an entrepreneur, I am notoriously optimistic; perhaps I am early. So, feel free to disagree on timing. If I have a blind spot, that's it.

Also, Don't, for a second, think that I am diminishing the magnitude of activity in the Defence and Intelligence (D&I) sector (that of the US specifically, but the rest of the world more generally, too.) D&I will continue to be the dominant funder of geospatial data and software companies. But what I want to illustrate is the rise of the commercial sector, which modern Earth Observation (EO) companies have been so desperately seeking and upon which their 2020/21 Special Purpose Acquisition Company (SPAC) valuations were based.

Is this a story of rampant optimism or one of better late than never? Perhaps we are still in for a false start, and there are numerous market complications which could accelerate or stunt the growth of this delicate market flower. Nevertheless, some of the appropriate market conditions are now in place.

To understand the signals I am seeing, I will try to weave a broader market tapestry from a number of situational threads. This note is an effort to help me draw those threads together into what is hopefully a holistic and understandable story.

Part One: Schumpeter wins again

The modern insurance sector is massive, but much like the banking sector, it is built on a series of acquisitions, mergers and compliance fear. This means every major insurance company is presently babysitting an on-premise Jenga tower of teetering and malformed technology blocks which no individual wants to touch for fear of their job and reputation. Thus, the motivation to "change" in a major insurance company is low.

Wait, this doesn't sound promising for geospatial.

But where we see a problem, there is a cadre of mid-career insurance people who have been living in this situation. For twenty years, they have been told to deal with it because that's how it’s done. The real reason is that some internal process seems to be working, and no matter how square the wheels are, those wheels are the wheels we have and we understand how they work. So, through their careers, this cadre has seen no change and little willingness to risk trying. Rightfully, they are identifying this inertia as nonsense and are starting their own new breeds of insurance companies: mid-career entrepreneurism, born of frustration. Built on new technology stacks and new data products, these companies want to be innovative and actively intend to have a massive impact with a surprisingly small headcount. There is little more fearsome to the establishment than this group of domain experts with a clean slate and a willingness to build.

So, in the short term, this group are accessing legacy capital, while building new capabilities. In the slightly longer term, they will be able to access their own capital. And, the Schumpeterian snake will once again eat its own tail to ultimately renew itself. Shedding the skins of legacy workflows. This creative destruction is one signal of the tipping point for geospatial, and the key phrase here is "low headcount."

Part Two: All bets will be off.

Yesterday be damned, all bets will be off. The expectation of non-traditional GIS users is already higher than can be met by traditional planimetric GIS tools. I see this already with drone teams that use better geospatial tools than GIS teams. With companies like Google, Niantic and DJI introducing geospatial technology to the masses, the expectations of digital geography have been raised. This new breed of insurance and risk companies will not be hiring traditional GIS teams. Indeed, with a desire to keep costs and headcount managed I would expect a range of managed services with discrete and actionable tools will be much more attractive.

Systems which natively communicate with each other via sensible, published, but secure APIs will allow risk-management systems to support the needs of different small to mid-sized insurance companies and banks. This will allow them to move very fast and have access to post-event tools to inform the prompt movement of capital. These refined workflows can potentially save tens of millions of dollars/pounds and several stomach ulcers per event**. But remember, geospatial can inform users about risk in various phases of the asset management process, this is not just about post-event analysis.

With this new approach and the possibility of a cloud-native structure, suddenly many new opportunities around the use of data can be realized. This is where our friends in Low Earth Orbit (LEO) might again prick up their ears. One of the biggest barriers to the use of EO has been technology infrastructure. With a modern infrastructure and less need to support ancient Fortran workflows, suddenly, there is funding and technologies to support new data paradigms.

So, all bets are off, what we have been doing will be identified as superfluous, manual and slow.

Part Three: Some biggies are innovating.

While some companies are harbouring the teetering technology stacks, some have been more circumspect and careful with integrating. These organizations are also seeing the benefits of a changing landscape. For this reason, the Mergers and Acquisition (M&A) landscape for risk-oriented geospatial analytics is very fertile (Fathom -> Swiss Re, Geosite -> Descartes.) A modern pattern here, however, is to hold these acquisitions at arm’s length and pull in relevant data via API, thus avoiding the worst of the Jenga effect.

Part Four: Dominos of AI.

Before any AIs, there are people. We have always trained machines, and now these machines are supporting our decisions. This process is being borne out in real-time in the insurance sector. Parametric insurance depends on robust and agreed-upon data sources. However, the granularity of these data sources is becoming increasingly sensitive. The best data, and the best models are being developed and then disputed (Again, a Schumpeterian cycle we should celebrate.) In time, though, as these dominos fall, confidence in the process rises. In traditionally manual processes, a change in the model would be hugely problematic and might incur compliance issues. In the future, we will see more granular models providing more revenue opportunities. By this, I mean that without geographic sophistication, insurance companies had no data with which to parse major climatic landscape changes like increasing wildfires in California, without "good data," all that could be done was to decide to avoid entire political areas, like Counties or even States. With better data, more granularity can be applied to this decision-making, ultimately increasing revenue opportunities. This is particularly true with flood modelling, where higher resolution can indicate various houses that might be far more resilient than others to rising rivers or pouring ponds.

So, these are my central observations on risk. This note does not touch on the financial regulatory environment for ESG or Biodiversity Net Gain, both of which are additional and significant opportunities for the geospatial sector. In no uncertain terms, spatial finance is emerging from the mists of unstructured markets to become a reality.

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* I was deeply disappointed this year at SatCamp when I asked if a speaker felt that the customers of the EO sector had changed in the last decade, only to be challenged with them not knowing who the customer was last decade, as they were still in school. I was disappointed not in them but in me, thinking that a decade seems short, when in fact I am just old!

** What is the noun of assemblage for a stomach ulcer? A blister of ulcers?

When I think of geographic information, I think of these digital mirror worlds. When I think about smartphones, I think about portals into these alternative universes. A phone is a subtle knife that allows movement between realms of information.

As we navigate our lives we are constantly interacting with these alternative realities. When Gelertner first talked about Mirror Worlds in the 90s, his observations were prescient:

There are software models of some chunk of reality, some piece of the real world going on outside your window.

In the 90s, those universes were navigated only by experts; now, we have created systems and technology to make that navigation almost invisible. As I argued in Enable Simplicity, building innovative products which are practically invisible but just “a lot better” is a great strategy to reduce barriers to adoption.

The phrase digital twin has been co-opted by several industrial groups. From building designers to agronomists, I have seen this phase provide the mental visualization to help broader populations understand a designed digital connectivity between features within a constrained environment. In many ways, it’s a great term because it conjures up the image of a digital equivalency to a process or thing. But this notion of a digital mirror world is more ephemeral, more esoteric.

It could be argued that traditional Geographic Information Systems (GIS) serve a similar purpose: creating geographic data that may or may not be directly represented in physical space. Interestingly, in Gelentner’s initial explorations, he was reflecting simply on the existence of digital data, not that the entire world would somehow interact with that data through a mobile device.

So, what separates a GIS from a mirror world, from a digital twin?

I use this image a lot.

Mirror Worlds

Somewhere, on some computer, there is a geographic line that this signpost attempts to inform visitors about. That line was derived through some authoritative process by a government body, but it remains invisible to people in the physical world. In the line’s digital mirror world, it holds a legal status and that status holds physical import to people who behave inappropriately on either side of that invisible line. Yet, one could wander through this area, miss a sign and remain oblivious to some geographic status change.

In our physical activities we are clumsily barging through digital mirror worlds every day with minimal repercussions. But that will change as our digital and physical realities are increasingly hard to separate

Mirror worlds depend on data, and geospatial data, especially points of interest with their associated building outlines are notoriously inconsistent. When the presence or absence of our smart devices within various poorly digitized and under-classified polygons being monitored, unbeknownst to our over-caffeinated and hyper-stimulated brains, we can rightly be concerned that data errors could end up having significant legislative impacts on parts of our society. In cases like these, the looking glass through which we can see our mirror world could also be reporting our activities right back to the Queen of Hearts.

The idea of a mirror world is really just what we do with geographic data. Only recently, with the advent of connected geographic devices, are we seeing the collision between pervasive location tracking and privacy. The duel plains of our physical and digital identities are being inferred from each other.

In this inference, a digital and policy rubicon has been crossed. Having lost our digital privacy, it seems unlikely that can easily find it again. Of course, not all use cases are nefarious, but there is something sadly complicated and human in the idea that while we can never again easily be private, the machines may be looking on while people still suffer from loneliness.

That may seem needlessly dark. In reality, we can always achieve digital privacy by abandoning our devices. Go for a walk and leave your phone behind. We don’t need to share everything. The concepts of digital mirror worlds and digital twins as extensions to our traditional ideas around GIS are tremendously powerful. Indeed, companies like Niantic have created digital mirror worlds for us to play and delight in. Not all futures must be dystopian; I believe that, ultimately, our society tends to make good decisions, albeit we can take time to settle on “good'.”

Digital Twins

Digital twins, tend to exhibit at least one more critical feature: the ability to simulate. While mirror worlds are, digital twins also allow for what could be. This ability to project scenarios through time means a digital twin becomes a playground for experimentation. This is a hugely valuable toolkit for understanding different economic, climate and policy choices. A modern and increasingly detailed Sim City, but for anything, is a compelling idea that, for expert communities, provides deep insight into future phenomena.

Digital Twins have been used within the industrial community for some time for the design and management of large machinery. The simulation and tracking of machine maintenance and gentle experimentation based on the likely future lifespan of the machine within the context of past maintenance are great safety mechanisms. This idea is bleeding into building and geographic developments. Why can we not think about our geographic spaces as systems and components for simulation and monitoring?

If we indulge in some “inevitability thinking,” we know that cities will eventually be able to manage their infrastructure and assets with digital twins. To some extent, this is already being done, albeit with understandably limited expectations.

Portals to the future?

A digital mirror world can be interacted with via a smart device, meaning we can build services to tap into those worlds through the phone. The phone is then a portal through which to see these invisible places. The digital twin takes this idea further to build simulation capability giving our community the ability to project scenarios forward.

What is more compelling than seeing into the future?

All universes are flawed in the same way that all models are just that, a model of reality. We should celebrate this because most digital twins are designed to abstract phenomena for experimentation. This, of course, begets the question of chaos and complex systems (thinking about transitions is useful here.) Nevertheless, if we endeavour to build new digital universes, then data quality should always be considered.

If you are compelled to build your own mirror world, at least add an appropriate travel advisory!