Rescue requests during large-scale urban flood disasters can be difficult to validate and prioritise. High-resolution aerial imagery is often unavailable or lacks the necessary geographic extent, making it difficult to obtain real-time information about where flooding is occurring. In this paper, we present a novel approach to map the extent of urban flooding in Harris County, Texas during Hurricane Harvey (August 25–30, 2017) and identify where people were most likely to need immediate emergency assistance. Using Maximum Entropy, we predict the probability of flooding based on several spatially-distributed physical and socio-economic characteristics coupled with crowdsourced data. We compare the results against two alternative flood datasets available after Hurricane Harvey (i.e., Copernicus satellite imagery and riverine flood depths estimated by FEMA), and we validate the performance of the model using a 15% subset of the rescue requests, Houston 311 flood calls, and inundated roadways. We find that the model predicts a much larger area of flooding than was shown by either Copernicus or FEMA when compared against the locations of rescue requests, and that it performs well using both a subset of rescue requests (AUC 0.917) and 311 calls (AUC 0.929) but is less sensitive to inundated roads (AUC 0.721).