@Article{HannesMueller2021,
author="Hannes Mueller
and Andre Groeger
and Jonathan Hersh
and Andrea Matranga
and Joan Serrat",
title="Monitoring war destruction from space using machine learning",
journal="Proceedings of the National Academy of Sciences of the United States of America",
year="2021",
volume="118",
number="23",
pages="e2025400118",
abstract="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.",
optnote="ADAS; 600.118",
optnote="exported from refbase (http://refbase.cvc.uab.es/show.php?record=3584), last updated on Fri, 28 Jan 2022 10:28:12 +0100",
doi="10.1073/pnas.2025400118",
opturl="https://doi.org/10.1073/pnas.2025400118",
file=":http://refbase.cvc.uab.es/files/MGH2021.pdf:PDF"
}