发表机构
Microsoft AI for Good Research Lab（微软人工智能造福研究实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
HASTE平台可快速进行灾后建筑物损坏评估。它有两种方法，一种通过用户标记多边形训练语义分割模型，另一种利用预训练视觉模型和逻辑回归。实验表明其能仅用灾后图像区分受损与完好建筑，已支持多次实际灾难应对。
AI 中文摘要
当重大灾难发生时，救援人员需要在数小时内获取受损建筑物的地图。在公共基准测试中表现良好的模型需要匹配的灾前和灾后图像以及来自类似过去事件的训练集，但在新灾难发生的第一天通常都无法获得。我们提出了HASTE（High-speed Assessment and Satellite Tracking for Emergencies），这是一个无代码网络平台，让非机器学习工程师的分析师能够从灾后卫星图像生成每栋建筑物的损坏地图。HASTE实现了两种共享一个接口的方法。第一种方法要求用户在灾后场景上标记多边形，在该单个场景上训练一个小型语义分割模型，在整个图像上运行该模型，并将每个像素的输出与现有的建筑物足迹相结合。第二种方法使用预训练的视觉模型嵌入每个足迹，要求用户标记少数建筑物，并在浏览器中拟合逻辑回归，在几秒钟内对场景的其余部分进行评分。我们描述了该平台、两种方法以及支持它们的工程。我们还报告了在xBD上的初步实验，结果表明仅使用灾后图像，通过在足迹上汇总基础模型嵌入就能将受损建筑物与完好建筑物分开，与使用其五分之一标签的全监督ResNet-50基线相匹配。自2023年以来，HASTE及其前身已经支持了三十多次实际灾难应对，涵盖地震、飓风、气旋、洪水、野火和龙卷风等，在图像可用后的数小时到数天内为人道主义合作伙伴提供结果。我们最后指出了我们认为最有前景的方向，包括视觉语言评估、主动学习以及道路和其他基础设施的损坏模型。HASTE在这个https URL上是开源的。
英文摘要
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 existing building footprints. The second embeds every footprint with a pretrained vision model, requires the user to label a handful of buildings, and fits a logistic regression in the browser that scores the rest of the scene in seconds. We describe the platform, both methods, and the engineering that supports them. We also report preliminary experiments on xBD showing that foundation-model embeddings pooled over footprints separate damaged from intact buildings using post-disaster imagery alone, matching a fully supervised ResNet-50 baseline with a twentieth of its labels. HASTE and its predecessors have supported more than thirty real-world disaster responses since 2023, spanning earthquakes, hurricanes, cyclones, floods, wildfires, and tornadoes, delivering results to humanitarian partners within hours to days of imagery becoming available. We close with the directions we think are most promising, including vision-language assessment, active learning, and damage models for roads and other infrastructure. HASTE is open source at https://github.com/microsoft/haste.
