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In May-June 2022, northeastern Bangladesh suffered devastating flooding affecting over 7 million people, to which various public agencies responded by producing maps of flooded areas derived from satellite images. In the wake of this and the growing availability of satellite flood algorithms and end-user-products, we compared surface water maps for the Sylhet District generated from various remote sensing approaches, focusing on (a) "local" versus "global" and (b) machine learning (ML) versus "traditional" (non-ML) methods. Specifically, we assessed (1) a "local" Sentinel-1 change detection algorithm calibrated on four recent floods in Bangladesh, (2) a pre-trained "globally applicable" ML Sentinel-1 algorithm, (3) the Copernicus Global Flood Monitoring (GFM) tool, (4) a deep learning (DL) "fusion" of MODIS and Sentinel-1 and (5) a MODIS algorithm used to produce the Global Flood Database (GFD …