TODAY, the Geospatial Commission released the report we prepared for a project we have been working over the last year in collaboration with them. The work builds on our earlier contribution to the National Land Data Programme last year, and the document puts in writing much of what we presented to the minister for AI and intellectual property last March. There is also an accompanying blog post Mehul and his team at the Commission put out. (View Highlight)
In one, we took our initial DemoLand app, a tool that helps users explore different land use scenarios and their effect on areas such as air quality or house prices, and embedded a chat interface powered by a large language model. The original tool provided access to a lot of data and modelling that would be hard to access for non-technical audiences otherwise, but it still required the user to “know their way around”. With the new chat-based interface, exploring the results is a much more conversational experience that can reach larger even audiences. In the second exercise, we digged into the guts of the models that power land use applications such as those in DemoLand. Typically, many of these require data that is collected and released more slowly than ideally required for decision making (e.g., Census or building cadasters). We explored complementing, or even replacing, these by widely available satellite imagery. Satellite data is definitely not a new thing, we’ve had metal boxes orbitting the Earth since at least the 1950s, but there are a few recent things that make them more appealing. The revolution in computer vision (and what is now also termed AI) we have seen in the last 15 years has changed what we are able to do with imagery, even that of limited spatial resolution. (View Highlight)
What will probably (I hope!) catch most attention is the “Recommendations” section. Here is where we brought together everything we learnt in these exercises to propose concrete steps forward. In particular, we mention five:
1/ Identify additional areas of opportunity for satellite data to build the value case for geospatial AI.
2/ Develop a Geospatial AI Toolkit for LLMs.
3/ Expand the conversation on national foundation models to land use and geospatial.
4/ Improve access to key computational and data resources.
5/ Promote knowledge sharing and cross-discipline collaboratio. (View Highlight)
New highlights added January 9, 2025 at 2:33 PM
LLMs, for example, are not very good at geography (there is a reason why the second L is not a G!). Before we jump in and take them off-the-self, we think there is work to do to develop the “Geography curriculum” we’d like these models to know when they help folks on spatial domains. And others seem more obvious than they actually are. Suggesting in 2024 that satellite data be used for land use change may cause unreparable eye-rolls among land use experts who’ve been doing this in an academic context for several decades. Yet there is still very little of it that has made it into “production” at scale, particularly in non environmental and physical domains such as cities and society. So think twice before sending your eyes upwards. (View Highlight)
