TY  - JOUR
AU  - Hannes Mueller
AU  - Andre Groeger
AU  - Jonathan Hersh
AU  - Andrea Matranga
AU  - Joan Serrat
PY  - 2021//
TI  - Monitoring war destruction from space using machine learning
T2  - PNAS
JO  - Proceedings of the National Academy of Sciences of the United States of America
SP  - e2025400118
VL  - 118
IS  - 23
N2  - 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 label augmentation and spatial and temporal smoothing, which exploit the underlying spatial and temporal structure of destruction. As a proof of concept, we apply this method to the Syrian civil war and reconstruct the evolution of damage in major cities across the country. Our approach allows generating destruction data with unprecedented scope, resolution, and frequency—and makes use of the ever-higher frequency at which satellite imagery becomes available.
UR  - https://doi.org/10.1073/pnas.2025400118
L1  - http://refbase.cvc.uab.es/files/MGH2021.pdf
UR  - http://dx.doi.org/10.1073/pnas.2025400118
N1  - ADAS; 600.118
ID  - Hannes Mueller2021
ER  -