Due to its cloud-penetrating capability and independence from solar
illumination, satellite Synthetic Aperture Radar (SAR) is the preferred data
source for large-scale flood mapping, providing global coverage and including
various land cover classes. However, most studies on large-scale SAR-derived
flood mapping using deep learning algorithms have primarily focused on flooded
open areas, utilizing available open-access datasets (e.g., Sen1Floods11) and
with limited attention to urban floods. To address this gap, we introduce
\textbf{UrbanSARFloods}, a floodwater dataset featuring pre-processed
Sentinel-1 intensity data and interferometric coherence imagery acquired before
and during flood events. It contains 8,879 $512\times 512$ chips covering
807,500 $km^2$ across 20 land cover classes and 5 continents, spanning 18 flood
events. We used UrbanSARFloods to benchmark existing state-of-the-art
convolutional neural networks (CNNs) for segmenting open and urban flood areas.
Our findings indicate that prevalent approaches, including the Weighted
Cross-Entropy (WCE) loss and the application of transfer learning with
pretrained models, fall short in overcoming the obstacles posed by imbalanced
data and the constraints of a small training dataset. Urban flood detection
remains challenging. Future research should explore strategies for addressing
imbalanced data challenges and investigate transfer learning's potential for
SAR-based large-scale flood mapping. Besides, expanding this dataset to include
additional flood events holds promise for enhancing its utility and
contributing to advancements in flood mapping techniques.

通过介绍 UrbanSARFloods 数据集，研究考察了卫星合成孔径雷达在大规模洪水测绘方面的局限性，发现目前的方法无法克服数据不平衡和小型训练数据集的限制，探索了解决不平衡数据挑战和利用迁移学习进行基于合成孔径雷达的大规模洪水测绘的潜力，为深化洪水测绘技术的应用做出贡献。