# Open Access Battle Damage Detection via Pixel-Wise T-Test on Sentinel-1 Imagery

arXiv: https://arxiv.org/abs/2405.06323 · HTML: https://mlsanity.com/paper/2405.06323/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 38 references that are in the map.
- First posted on arXiv 10 May 2024.

## Builds on (most cited first, 4 of 4)

- [xBD: A Dataset for Assessing Building Damage from Satellite Imagery](https://mlsanity.com/paper/1911.09296/) (2019)
- [BDANet: Multiscale Convolutional Neural Network with Cross-directional Attention for Building Damage Assessment from Satellite Images](https://mlsanity.com/paper/2105.07364/) (2021)
- [Monitoring War Destruction from Space: A Machine Learning Approach](https://mlsanity.com/paper/2010.05970/) (2020)
- [Self-Supervised Learning for Building Damage Assessment from Large-scale xBD Satellite Imagery Benchmark Datasets](https://mlsanity.com/paper/2205.15688/) (2022)

## Built on by (most cited first, 1 of 1)

- [An Open-Source Tool for Mapping War Destruction at Scale in Ukraine using Sentinel-1 Time Series](https://mlsanity.com/paper/2406.02506/) (2024)

## Abstract (arXiv)

In the context of recent, highly destructive conflicts in Gaza and Ukraine, reliable estimates of building damage are essential for an informed public discourse, human rights monitoring, and humanitarian aid provision. Given the contentious nature of conflict damage assessment, these estimates must be fully reproducible, explainable, and derived from open access data. This paper introduces a new method for building damage detection-- the Pixel-Wise T-Test (PWTT)-- that satisfies these conditions. Using a combination of freely-available synthetic aperture radar imagery and statistical change detection, the PWTT generates accurate conflict damage estimates across a wide area at regular time intervals. Accuracy is assessed using an original dataset of over half a million labeled building footprints spanning 12 cities across Ukraine, Palestine, Syria, and Iraq. Despite being simple and lightweight, the algorithm achieves building-level accuracy statistics (AUC=0.88 across Ukraine, 0.81 in Gaza) rivalling state of the art methods that use deep learning and high resolution imagery. The workflow is open source and deployed entirely within the Google Earth Engine environment, allowing for the generation of interactive Battle Damage Dashboards for Ukraine and Gaza that update in near-real time, allowing the public and humanitarian practitioners to immediately get estimates of damaged buildings in a given area.