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On the morality of omnipresence.
If we can see everything, do we then have a moral duty to act on what we see?
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Remote sensing is, by its very nature, a scalable technology. For instance, the Landsat program has been orbiting our planet for thirty years, recording landscape changes and human activities. Landsat’s product wasn’t limited in some way to a particular location, so our planet has been somewhat* evenly monitored for the duration of the program. The primary value of satellite remote sensing has always been the ability to look at far-off places relatively easily. In addition, the Landsat program’s imagery has been released under an open license. In this manner, Landsat is an equitable data source; there are no barriers to its use.
That said, there are barriers to using Landsat or any sensor for that matter.
As we move to a society witnessing greater complexity in landscape changes and climate-related events, what is our moral requirement as digital geographers to deliver information to communities that need it?
There are two themes to this discussion. Firstly, there is a general gap in the exploitation of imagery. The downstream component of the Earth Observation (EO) value chain is still largely nascent. Secondly, there is a capacity gap between the delivery of enormous amounts of sensor data and the ability of those communities most in need to put that data to use. To some extent, these are the same concern. Outside the defence sector, remote sensing is not well understood. But this question is deeper than an exploration of market forces.
When communities are in peril, is there a societal responsibility to monitor and report?
As I see it, there are two issues:
Equitable access to data
Equitable access to insight
With the rise of “smart space” and the enormous growth in commercial space, the amount of sensory data flowing out of the sky has increased enormously. As I have often suggested, this data flow comes just in time to help our global community understand the risks around more frequently occurring climate-related events such as fires or floods. However, some communities are better equipped to consume data than others. Moreso, some communities are better equipped to buy data products that could provide critical insight into the survivability of a community in the face of an event. This raises the moral question: if a private organization discerns a danger to a community, what is that organization’s moral duty to inform the community of the threat? While likely saving lives, becoming a global climate alerting service will not necessarily drive shareholder value for a typical satellite company.
Indeed, suppose an organization is designed to provide alerts for a catastrophic event, such as fire. What is that organization’s responsibility to provide those alerts even if a community has not “signed up for the service”?
The pathway through this moral minefield is not clear, but we can start from a few principles.
Firstly, most remote sensing companies have an open data** program for disaster relief. These programs are hugely beneficial; all companies and institutions involved should be applauded for this work. So, we can observe that the moral case is satisfied for the monitoring of post-catastrophic events. Data is accessible, and in terms of post-event rescue and clean-up, the image is a genuinely helpful product in and of itself: situational awareness.
However, some catastrophes emerge over a long time: reservoirs dry up, crops fail, and wildfire fuel builds up. These measurable landscape features are not being measured at this point, yet their social and economic effects are significant. Open data programs, while laudable, are a reaction to events; they are not preventative.
Preventative products are more complex. Typically these products involve consistent monitoring and complex algorithms.
So, whereas open data programs typically provide imagery, which as a situational awareness tool, is excellent. To give some notion of prescience, imagery must be interpreted. This is the second issue; the gap between situational awareness and insight remains wide.
So secondly, with today’s data flow, there are not enough trained eyes to look at every image captured daily. This means that if we genuinely consider an equitable approach, algorithms must consider the planet somewhat equally and be able to parse the vast amounts of data captured with an analytic intent. Presently, this is somewhat aspirational. One could argue that this is not yet a problem, so why even consider it? Except that the business models that will define the next decade of remote sensing are being designed now, and data equity should be built in.
When we consider equity, we need to think about access to data but also access to education, computing resources, time and capital. We must consider the heritage of built environments and the geographic context in which people find themselves. We need to consider planetary solutions and how different products can be designed to meet the needs of different communities.
Clearly, our community is awash in data. However, I suspect that while the data flow seems significant now, it will continue growing at a rate approaching exponential. With this kind of sensory opportunity, we should remember that while some of us see the world from the cutting edge of geospatial, most live in an entirely different context.
Yet again, William Gibson’s quote comes to mind:
The future is already here, its just very unevenly distributed
We are still early, so while we can build equity into our business models, we should.
*Forgiving the vagaries of orbital mechanics, mission priorities, and cloud cover.
**Some will suggest that open data programs are “free data” programs. Data is often released with non-commercial and restricted licenses. Open is a word often used and less often meant.