Tag: deep learning

Review: GeoAI

Published on by Jonathan Crowe
Book cover: GeoAI

Save some room on the AI bandwagon for ArcGIS. This seems to be the central message of GeoAI: Artificial Intelligence in GIS, a slim (only 120-page) volume of articles and posts that previously appeared, for the most part, in Esri blogs and publications. They highlight examples and “real-life stories” of how Esri’s machine- and deep-learning tools have been successfully applied in the public, private and non-profit sectors. At a moment when “AI” is invariably a synecdoche for the awfulness that is generative AI, which I will not litigate here, it can be a challenge to remember that machine and deep learning tools, which have been included in ArcGIS since 2008, have all kinds of applications and benefits. (See Esri’s pretrained deep learning models for examples like feature detection, land-cover classification, and object tracking; see also their GeoAI landing page.) Calling these tools “GeoAI” strikes me as a way to package them to appeal to decision makers who are speedrunning their AI rollout, for better or worse. It’s those decision makers that this book is targeted to. Esri has something to sell them: this is the pitch.

I received an electronic review copy from the publisher.

GeoAI: Artificial Intelligence in GIS
ed. by Ismael Chivite, Nicholas Giner and Matt Artz
Esri, 2 Sep 2025, $40
Amazon (Canada, UK), Bookshop

Deep Learning Applied to Satellite Imagery Reveals Untracked Ships

Published on by Jonathan Crowe

Maps showing registered and unregistered fishing vessels near Spain, Morocco, Sicily and Tunisia.
Excerpt from Fig. 2 of Paolo et al., “Satellite mapping reveals extensive industrial activity at sea,” Nature 625 (2024).

Speaking of AI-assisted global monitoring: researchers affiliated with Global Fishing Watch have revealed that the global fishing, transport and energy fleets are a lot bigger than expected. They were able to compare the locations of ships carrying AIS transponders with satellite imagery, to which deep learning was applied to classify ships. They conclude that something like three-quarters of industrial fishing vessels, and thirty percent of transport and energy vessels, go untracked. This isn’t necessarily so much about clandestine activity—in many regions ships, especially fishing boats, simply aren’t required to be tracked—but it can, among other things, reveal illegal fishing in protected areas. Results of the study were published in Nature last month. Global Fishing Watch also has an interactive map. [The Verge]

Google, EDF Partner to Build Map of Global Methane Emissions

Published on by Jonathan Crowe

Methane is a greenhouse gas, more powerful than CO2 but shorter-lived. Google is partnering with the Environmental Defense Fund to map global methane emissions, much of which result from leaks from fossil fuel infrastructure and are undercounted. The EDF’s MethaneSAT satellite (itself a partnership between the EDF and New Zealand’s space agency) launches next month: it’ll measure methane emissions at high resolution. Google’s bringing to the party algorithms and AI, the latter to build a global map of oil and gas infrastructure.

Once we have this complete infrastructure map, we can overlay the MethaneSAT data that shows where methane is coming from. When the two maps are lined up, we can see how emissions correspond to specific infrastructure and obtain a far better understanding of the types of sources that generally contribute most to methane leaks. This information is incredibly valuable to anticipate and mitigate emissions in oil and gas infrastructure that is generally most susceptible to leaks.

More at The Verge.

Previously: Mapping Methane Emissions.