Flood damage data are needed for various applications. Structural damage of buildings can reflect not only the economic damage but also the life‐threatening condition of a building, which provide crucial information for disaster response and recovery. Since traditional on‐site data collection shortly after a disaster is challenging, remote sensing data can be of great help, cover a wider area and be deployed earlier in time than on‐site surveys. However, this has its challenges and limitations. We elucidate on that by presenting two case studies from flash floods in Germany. First, we assessed the reliability of an existing flood damage schema, which differentiates from minor (structural) damage to complete building collapse. We compared two on‐site raters of the 2016 Braunsbach flood, reaching an excellent level of reliability. Second, we mapped structural building damage after the flood in the Ahr valley in 2021 using a textured 3D mesh and orthophotos. Here, we evaluated the remote sense‐based damage mapping done by three raters. Although the heterogeneity of ratings using remote sensing data is larger than among on‐site ratings, we consider it fit‐for‐purpose when compared with on‐site mapping, especially for event documentation and as basis for financial damage estimation and less complex numerical modelling.

中文翻译：

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基于遥感的阿尔河谷结构建筑损坏测绘

各种应用都需要洪水损害数据。建筑物的结构损坏不仅可以反映经济损失，还可以反映建筑物的生命威胁状况​​，为灾害应对和恢复提供重要信息。由于传统的灾后现场数据收集具有挑战性，因此遥感数据可以提供很大帮助，覆盖范围更广，部署时间比现场调查更早。然而，这也有其挑战和局限性。我们通过介绍德国山洪暴发的两个案例研究来阐明这一点。首先，我们评估了现有洪水损坏模式的可靠性，该模式区分了轻微（结构）损坏和建筑物完全倒塌。我们对 2016 年布劳恩斯巴赫洪水的两名现场评估人员进行了比较，结果达到了极好的可靠性水平。其次，我们使用纹理 3D 网格和正射影像绘制了 2021 年阿尔河谷洪水后的结构建筑损坏情况。在这里，我们评估了三位评估者完成的基于遥感的损伤测绘。尽管使用遥感数据的评级的异质性大于现场评级，但我们认为与现场测绘相比，它更适合目的，特别是对于事件记录以及作为财务损失估计和不太复杂的数值建模的基础。