Seeing Through the Clouds With DeepWaterMap

We present our next-generation surface water mapping model, DeepWaterMapV2, which uses improved model architecture, data set, and a training setup to create surface water maps at lower cost, with higher precision and recall. We designed DeepWaterMapV2 to be memory efficient for large inputs. Unlike earlier models, our new model is able to process a full Landsat scene in one-shot and without dividing the input into tiles. DeepWaterMapV2 is robust against a variety of natural and artificial perturbations in the input, such as noise, different sensor characteristics, and small clouds. Our model can even “see” through the clouds without relying on any active sensor data, in cases where the clouds do not fully obstruct the scene. Although we trained the model on Landsat-8 images only, it also supports data from a variety of other Earth observing satellites, including Landsat-5, Landsat-7, and Sentinel-2, without any further training or calibration. Our code and trained model are available at https://github.com/isikdogan/deepwatermap.