用分层模型提升干旱城市洪水制图精度，解决水体与植被易混淆问题。
FM-LC: A Hierarchical Framework for Urban Flood Mapping by Land Cover Identification Models
- 分三阶段：先粗分类，再针对混淆类别训练轻量二分类模型
- 在迪拜暴雨事件中，各类别平均F1提升29%，边界更清晰
- 适合需高精度洪水监测的应急响应与城市规划人员
干旱地区城市内涝对基础设施和社区构成严重威胁。精确、细粒度的洪水范围与恢复轨迹制图对于提升应急响应和韧性规划至关重要。然而，干旱环境常存在水体与邻近地表光谱对比度低、水文动态迅速及城市土地覆盖高度异质等问题，挑战传统洪水制图方法。高分辨率、每日更新的PlanetScope影像提供了所需时空细节。本文提出FM-LC（Flood Mapping by Land Cover identification），一种面向此挑战的分层框架。该框架通过三阶段流程实现：首先使用多类U-Net初步将影像分为水体、植被、建筑区和裸地四类；发现水体与植被等光谱相似类别存在混淆。其次，通过早期检测识别出误分类面积最大的类别，并训练轻量级二分类专家模型以区分该类别与其他类别。最后，采用贝叶斯平滑步骤，利用邻近像素信息优化边界并去除噪声。在2024年4月迪拜风暴事件上验证，使用雨前雨后PlanetScope合成影像，实验结果表明，所有土地覆盖类别的平均F1分数提升高达29%，洪水边界显著更锐利，显著优于传统单阶段U-Net基线。
原文摘要 · Abstract (English)
Urban flooding in arid regions poses severe risks to infrastructure and communities. Accurate, fine-scale mapping of flood extents and recovery trajectories is therefore essential for improving emergency response and resilience planning. However, arid environments often exhibit limited spectral contrast between water and adjacent surfaces, rapid hydrological dynamics, and highly heterogeneous urban land covers, which challenge traditional flood-mapping approaches. High-resolution, daily PlanetScope imagery provides the temporal and spatial detail needed. In this work, we introduce FM-LC, a hierarchical framework for Flood Mapping by Land Cover identification, for this challenging task. Through a three-stage process, it first uses an initial multi-class U-Net to segment imagery into water, vegetation, built area, and bare ground classes. We identify that this method has confusion between spectrally similar categories (e.g., water vs. vegetation). Second, by early checking, the class with the major misclassified area is flagged, and a lightweight binary expert segmentation model is trained to distinguish the flagged class from the rest. Third, a Bayesian smoothing step refines boundaries and removes spurious noise by leveraging nearby pixel information. We validate the framework on the April 2024 Dubai storm event, using pre- and post-rainfall PlanetScope composites. Experimental results demonstrate average F1-score improvements of up to 29% across all land-cover classes and notably sharper flood delineations, significantly outperforming conventional single-stage U-Net baselines.
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