基于Sentinel-1影像逐像素T检验的开放获取战斗损伤检测
Open Access Battle Damage Detection via Pixel-Wise T-Test on Sentinel-1 Imagery
AI总结：
本文提出PWTT方法，利用免费Sentinel-1雷达影像和统计变化检测实现可复现、可解释的建筑物战斗损伤评估，并在跨四国12城数据集上达到接近深度学习方法的AUC。
摘要：
在加沙和乌克兰近期极具破坏性的冲突背景下，可靠的建筑物损伤估计对于知情的公共讨论、人权监测和人道主义援助供给至关重要。鉴于冲突损伤评估具有争议性，这些估计必须完全可复现、可解释，并来源于开放获取数据。本文介绍了一种满足这些条件的建筑物损伤检测新方法——逐像素T检验（Pixel-Wise T-Test，PWTT）。PWTT结合免费可用的合成孔径雷达影像与统计变化检测，能够按固定时间间隔在广阔区域内生成准确的冲突损伤估计。研究使用一个原创数据集评估准确性，该数据集包含超过50万个带标注的建筑物足迹，覆盖乌克兰、巴勒斯坦、叙利亚和伊拉克的12座城市。尽管该算法简单且轻量，但其建筑物级准确性统计（乌克兰AUC=0.88，加沙AUC=0.81）可与使用深度学习和高分辨率影像的最先进方法相媲美。该工作流是开源的，并完全部署在Google Earth Engine环境中，能够生成近乎实时更新的乌克兰和加沙交互式战斗损伤仪表板，使公众和人道主义从业者能够立即获得给定区域内受损建筑物的估计。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
