论文概要
研究领域: CV 作者: Caleb Robinson, Anthony Ortiz, Simone Fobi Nsutezo, Cameron Birge, Meygha Machado 发布时间: 2026-07-13 arXiv: 2607.11838
中文摘要
大型灾害发生后，响应者需要数小时内获得建筑物损坏地图。在公共基准上表现良好的模型假设有匹配的灾前灾后影像和从类似过去事件抽取的训练集，而新灾害首日通常两者皆无。本文提出HASTE（高速评估与卫星追踪应急平台），一种无代码Web平台，让非机器学习工程师的分析人员从灾后卫星影像生成每栋建筑物的损坏地图。HASTE实现两种共享接口的方法：第一种要求用户在灾后场景上标注多边形，在该单一场景上训练小型语义分割模型，运行覆盖整张图像，并将逐像素输出与现有建筑足迹连接；第二种用预训练视觉模型嵌入每个足迹，要求用户标注少量建筑物，在浏览器中拟合逻辑回归，数秒内为场景其余部分评分。在xBD上的初步实验表明，基于基础模型在足迹上池化的嵌入仅使用灾后影像即可区分损坏与完好建筑，匹配全监督ResNet-50基线但仅需其二十分之一的标签。HASTE及其前身自2023年以来已支持30多次真实灾害响应，涵盖地震、飓风、气旋、洪水、野火和龙卷风，在影像可用后数小时到数天内向人道主义合作伙伴交付结果。
原文摘要
When a large disaster strikes, responders need a map of which buildings are damaged within hours. The models that do well on public benchmarks assume matched before-and-after imagery and a training set drawn from similar past events, and neither is usually available for a new disaster in its first day. We present HASTE (High-speed Assessment and Satellite Tracking for Emergencies), a no-code web platform that lets analysts who are not machine learning engineers produce per-building damage maps from post-disaster satellite imagery. HASTE implements two methods that share one interface. The first requires the user to label polygons over the post-disaster scene, trains a small semantic segmentation model on that single scene, runs it over the whole image, and joins the per-pixel output to exi…
— 自动采集于 2026-07-15
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