This project is joint work by Clement Gorin, Andre Groeger, Arogya Koyrala and Hannes Mueller. It builds on previous work of the authors using satellite images and Deep Learning for automated detection of war-related building destruction in Syria. Satellite imagery is becoming ubiquitous and is released with ever higher frequency. Research has demonstrated that Deep Learning applied to satellite imagery holds promise for automated detection of war-related building destruction. While these results are promising, monitoring in real-world applications requires consistently high precision, especially when destruction is sparse and detecting destroyed buildings is equivalent to looking for a needle in a haystack. The previous work of the team has been published in the Proceedings of the National Academy of Sciences (PNAS) and has been awarded with the 2023 Spanish Society of Statistics and Operations Research – BBVA Foundation Award in the category "Best contribution in Statistics and Operations Research applied to Data Science and Big Data".
The team will generalize this established destruction monitoring method to be able to measure destruction in more civil war contexts outside the Syrian civil war. An important possible application is the systematic measurement of destruction of cities in the Ukrainian war for the rebuilding effort. But the application of the trained mechanism can also be used for humanitarian relief purposes in other contexts.
