Flooding in urban areas causes significant economic and social impacts on populations across the globe. Flood detection plays a pivotal role in disaster management, necessitating advanced methodologies to enhance accuracy and efficiency. Addressing this challenge requires delineating flood extent at a high spatial and temporal resolution. Efforts to fully quantify urban flood distribution utilizing the potential of Synthetic Aperture Radar (SAR) imageries in a cloud-based platform have ample potential but have yet to produce viable results in the urban landscape. Flood detection has been a challenging task in urban areas due to limitations of spatial-temporal resolution and complex back scatter mechanisms in urban settings. However, advancement in big-data and cloud-computing, data acquisition, satellite image processing and predictive analysis are rapidly becoming more accessible. Building on recent advancements, this study presents an analysis of methods exploring and comparing identification of flooded areas in urban and peri-urban locations, which has not been fully described. Using Houston, TX to test these methods, we compare flood maps generated from multiple classification method including constant threshold Change Detection Approach (CDA), Otsu method, and Machine Learning (ML) classification with Random Forest (RF) model using Sentinel-1 SAR images in Google Earth Engine (GEE). An extensive performance evaluation is conducted, including accuracy assessments, precision, recall, F1-score, and confusion matrices. The CDA approach shows the highest accuracy in peri-urban areas, while ML classifier outperforms both CDA and Otsu in urban settings. The analysis in this paper contributes to the development of flood detection methodologies in support of urban flood management.
Mehedi et al. (2025) studied this question.