用单期高分雷达影像+地理数据，快速评估地震后建筑损毁，无需灾前图像。
A Deep Learning framework for building damage assessment using VHR SAR and geospatial data: demonstration on the 2023 Turkiye Earthquake
- 融合雷达图像与地图、地形、建筑属性等多源数据进行损伤检测
- 在2023年土耳其地震数据上准确识别损毁建筑，性能优于仅用雷达的模型
- 无需灾前数据，适合紧急救援场景，可推广至不同城市区域
灾后迅速识别建筑损毁对应急响应至关重要。尽管光学遥感常用于灾害制图，但云层遮挡或缺乏灾前影像常限制其应用。为此，本文提出一种新型多模态深度学习框架，利用意大利航天局COSMO SkyMed（CSK）星座提供的单日期甚高分辨率（VHR）合成孔径雷达（SAR）影像，结合开放街道地图（OSM）建筑轮廓、数字地表模型（DSM）及全球地震模型（GEM）的结构与暴露属性数据，提升损伤检测精度与上下文理解能力。该方法仅依赖灾后数据，避免了对灾前影像的依赖，适用于紧急情况下的快速部署。框架在2023年土耳其地震覆盖多个城市的全新数据集上验证，结果表明引入地理空间特征显著提升模型性能与跨区域泛化能力。通过融合雷达影像与详细脆弱性及暴露信息，本方法实现无需灾前数据的可靠、快速建筑损毁评估。自动化可扩展的数据生成流程确保其在多样化灾后区域的应用潜力，为灾害管理与恢复提供有力支持。代码与数据将在论文接受后公开。
原文摘要 · Abstract (English)
Building damage identification shortly after a disaster is crucial for guiding emergency response and recovery efforts. Although optical satellite imagery is commonly used for disaster mapping, its effectiveness is often hampered by cloud cover or the absence of pre-event acquisitions. To overcome these challenges, we introduce a novel multimodal deep learning (DL) framework for detecting building damage using single-date very high resolution (VHR) Synthetic Aperture Radar (SAR) imagery from the Italian Space Agency (ASI) COSMO SkyMed (CSK) constellation, complemented by auxiliary geospatial data. Our method integrates SAR image patches, OpenStreetMap (OSM) building footprints, digital surface model (DSM) data, and structural and exposure attributes from the Global Earthquake Model (GEM) to improve detection accuracy and contextual interpretation. Unlike existing approaches that depend on pre and post event imagery, our model utilizes only post event data, facilitating rapid deployment in critical scenarios. The framework effectiveness is demonstrated using a new dataset from the 2023 earthquake in Turkey, covering multiple cities with diverse urban settings. Results highlight that incorporating geospatial features significantly enhances detection performance and generalizability to previously unseen areas. By combining SAR imagery with detailed vulnerability and exposure information, our approach provides reliable and rapid building damage assessments without the dependency from available pre-event data. Moreover, the automated and scalable data generation process ensures the framework's applicability across diverse disaster-affected regions, underscoring its potential to support effective disaster management and recovery efforts. Code and data will be made available upon acceptance of the paper.
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