This New Scientist article (unfortunately paywalled) reports on a remarkable application of geospatial AI, which is predicting the locations of landmines in conflict areas (or areas that once experienced conflict) from satellite images. The original paper is here.
Landmines are barbaric weapons that linger for years after a conflict finishes and primarily harm civilians . New Scientist states that of the nearly 5000 people injured or killed by landmines in 2022, 85% were civilians, and half of those civilians were children. Removing mines is extremely time-consuming and of course very dangerous for the teams involved, and tragically mines continue to be placed in ongoing conflicts such as the war in Ukraine. Anything that helps remove landmines as quickly and safely as possible is a blessing.
The AI tool, called Desk-AId, is trained using a map of confirmed landmine locations across a particular country, with the aim of identifying regions that are likely to be mined down to an accuracy of about 50 metres (rather than the location of specific mines). Locations such as buildings, waterways, and areas previously surveyed by mine removal teams are also included. Across the various models and conditions trialled there are promising signs, with a highest reported accuracy of 92%.
Regardless of any doubts about the purported benefits of generative AI models like ChatGPT across various industries, there's no doubt that if AI can assist with a vital humanitarian cause like landmine removal, that's extremely welcome and worthy of celebration.
Martin Jebens at the International Committee of the Red Cross and his colleagues have created an AI tool they call DeskAId that can use satellite images to pinpoint the likely locations of landmines based on knowledge of where they have been found in previous conflicts, as well as the locations of roads, buildings and medical facilities.
The AI learns patterns in the placement of mines in relation to these sites, which it uses to predict where they may have been laid when shown maps of new areas.
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