Monitoring War Destruction from Space: A Machine Learning Approach

Building destruction during war is a specific form of violence which is particularly harmful to civilians, commonly used to displace populations, and therefore warrants special attention. Yet, data from war-ridden areas are typically scarce, often incomplete and highly contested, when available. The lack of such data from conflict zones severely limits media reporting, humanitarian relief efforts, human rights monitoring, reconstruction initiatives, as well as the study of violent conflict in academic research. One approach has been to use remote sensing to identify destruction in satellite images[1]. This approach is gaining momentum as high-resolution imagery is becoming readily available and is updated ever quicker yielding weekly or even daily frequency. At the same time recent methodological advances related to deep learning have provided sophisticated tools to extract data from these images [2, 3, 4, 5].