Existing data on building destruction in conflict zones rely on eyewitness reports or manual detection, which makes it generally scarce, incomplete and potentially biased. This lack of reliable data imposes severe limitations for media reporting, humanitarian relief efforts, human rights monitoring, reconstruction initiatives, and academic studies of violent conflict. This article introduces an automated method of measuring destruction in high-resolution satellite images using deep learning techniques combined with data augmentation to expand training samples. We apply this method to the Syrian civil war and reconstruct the evolution of damage in major cities across the country. The approach allows generating destruction data with unprecedented scope, resolution, and frequency - only limited by the available satellite imagery - which can alleviate data limitations decisively.
This project has been particularly hard to implement. Machine learning from images is it's very own discipline and we benefitted tremendously from the seed funding we received from the La Caixa BGSE project and the servers of the Computer Vision Centre at the UAB. The project would have been impossible to implement without several research assistants who helped us reach several dead-ends and in the implementation of the final methodology. We are grateful for extremely valuable research assistance by Bruno Conte Leite, Jordi Llorens, Parsa Hassani, Dennis Hutschenreiter, Shima Nabiee, and Lavinia Piemontese. We are particularly grateful to Javier Mas for his research assistance which produced the final coding backbone to this project.
The paper is available as an unpublished manuscript here or from ArXiv. Supplementary Information is here.
