# Monitoring War Destruction from Space: A Machine Learning Approach

arXiv: https://arxiv.org/abs/2010.05970 · HTML: https://mlsanity.com/paper/2010.05970/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Oct 2020.

## Built on by (most cited first, 5 of 5)

- [S4: Self-Supervised Sensing Across the Spectrum](https://mlsanity.com/paper/2405.01656/) (2024)
- [An Open-Source Tool for Mapping War Destruction at Scale in Ukraine using Sentinel-1 Time Series](https://mlsanity.com/paper/2406.02506/) (2024)
- [Open Access Battle Damage Detection via Pixel-Wise T-Test on Sentinel-1 Imagery](https://mlsanity.com/paper/2405.06323/) (2024)
- [Flood Data Analysis on SpaceNet 8 Using Apache Sedona](https://mlsanity.com/paper/2404.18235/) (2024)
- [Near--Real-Time Conflict-Related Fire Detection in Sudan Using Unsupervised Deep Learning](https://mlsanity.com/paper/2512.07925/) (2025)

## Abstract (arXiv)

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.