2 research outputs found
Monitoring War Destruction from Space: A Machine Learning Approach
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
Battle damage assessment based on self-similarity and contextual modeling of buildings in dense urban areas
Assessment of battle damages is significant both for tactical planning and for after-war relief efforts. In this study damaged buildings are detected using self-similarity descriptor in pre- and post-war satellite images. Detection accuracy is improved by the use of a contextual model that describes the building neighborhoods. Building footprints are utilized for accurate assessment of building-level changes and for the formation of neighborhood context. The Gaza Strip after 2014 Israel-Palestine conflict is analyzed with the suggested method and 84% true positive rate and 19% false positive rate are obtained on the average for detection of damaged buildings with respect to the ground truth data of UNOSAT.This research was supported in part by Republic of Turkey Prime Ministry Disaster and Emergency Management Presidency (AFAD) and TUBITAK BILGEM under Grants B740-G585000Publisher's Versio