Flood inundation simulations can help in planning preventive measures against flood damage, however conventional simulation methods can be computationally expensive and time-consuming. An alternative to speed up the calculations is to use data-driven emulators. In this work, we present two contributions: (1) the development of a large-scale, high-resolution flood dataset and (2) the development of deep learning (DL)-based emulators for flood prediction trained using our dataset. We show that in comparison to previous works, our emulators are able to generalize to previously unseen test locations and achieve comparable performance metrics in terms of RMSE. In comparison to a GPU-accelerated simulator, an inference time speed-up of approximately 1000 times is achieved using these emulators. The dataset and code is available at https://github.com/dinesh-k-natarajan/urban-flood-emulator/ .