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Urban flood mapping is essential for disaster rescue and relief missions, reconstruction efforts, and financial loss evaluation. Much progress has been made to map the extent of flooding with multi-source remote sensing imagery and pattern recognition algorithms. However, urban flood mapping at high spatial resolution remains a major challenge due to three main reasons: (1) the very high resolution (VHR) optical remote sensing imagery often has heterogeneous background involving various ground objects (e.g., vehicles, buildings, roads, and trees), making traditional classification algorithms fail to capture the underlying spatial correlation between neighboring pixels within the flood hazard area; (2) traditional flood mapping methods with handcrafted features as input cannot fully leverage massive available data, which requires robust and scalable algorithms; and (3) due to inconsistent weather conditions at …