Report on AI and Land Use

November 14, 2024

Geospatial AI for Land Use, by The Alan Turing Institute. Independent Report from The Alan Turing Institute - URL

How geospatial AI can help inform our land use choices. Blog post from the Geospatial Commission on the report - URL

TODAY, the Geospatial Commission released the report weThe team on this project spanned folks at the Commission and Urban Analytics@Turing. At Turing, Barbara Metzler and Stuart Lynn did most of the heavy lifting, and I was the lucky one to “see early demos and present it around”. 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 ProgrammeGeospatial Commission (2023). Finding common ground: Integrating data, science and innovation for better use of land - URL 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.

Most of the report is a summary of what we learned in two specific exercises and a series of engagement events. I’ll give you the two-sentence version of each here but, if you are interested, you really should grab the report as it covers them much more comprehensively. 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. In this exercise, we explore foundation models for vision (a bit like the GPT in ChatGPT but for satellite data) to see how far we could push them. The result is a series of what I consider extremely exciting results but others might see as largely dry performance scores tables. You can get a summary of those in the report. In addition to these two exercises, we also ran consultation events (at AI UK’24 and online) with experts to get a broader view on the potential, challenges and immediate opportunities of geospatial AI for land use. I could summarise those, but I won’t do a better job than the report, so head over to its “Section 3” for that.

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.

Some are self-explanatory and I suspect few will disagree with them (who is against more knowledge sharing?). Others bring to the front discussions that we think deserve more attention than they’re currently receiving. 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 societyThis is one of the core ideas that made Imago a winning proposition, so watch this space. . So think twice before sending your eyes upwards.

Above all, and ironically given that it summarises past work, we hope this becomes a conversation starter. There is so much exciting stuff that is happening in the world of AI that could have tremendous implications for land use modelling and management. What exactly and, specially, how are debates we have just begun and are far from done. But we think they are worth having and actioning on. To work.