Towards Large-Scale Urban Flood Mapping Using Sentinel-1 Data

Within the realm of deep learning techniques, numerous remote sensing applications can be effectively addressed using deep learning algorithms. However, there is a scarcity of studies in synthetic Aperture radar (SAR)-based urban flood mapping involving deep learning techniques, primarily due to two reasons. First, SAR-based urban flood mapping is inherently rooted in change detection, resulting in a complex multi-modality problem within the imbalance data. This complexity arises from the integration of SAR intensity, InSAR coherence, and even SAR phase information acquired from different polarizations (i.e., VV and VH polarization in Sentinel-1 data) both before and after the event. The second challenge is the absence of a benchmark dataset specifically designed for SAR-based urban flood mapping. In an effort to fill this gap, a benchmark dataset for large-scale flood mapping using Sentinel-1 data, which includes not only SAR intensity but also InSAR coherence, should be created. The SAR pre-processing should be carefully checked at the very beginning. With this aim, we tested the specking filter and kernel size selection for the SAR preprocessing for deep learning models. Through this initiative, we found that despeckling SAR intensity and selecting the kernel size in InSAR coherence calculation do not significantly affect the accuracy in deep learning-based urban flood mapping using Sentinel-1 data. The curated benchmark dataset will be presented in the final paper.