DeepAquaCluster: Using Satellite Images And Self-supervised Machine Learning Networks To Detect Water Hidden Under Vegetation

In recent years the wide availability of high-resolution radar satellite images along with the advancement of computer vision models have enabled the remote monitoring of wetland surface areas. However, these models require large amounts of manually annotated satellite images, which are slow and expensive to produce. To overcome this problem we use self-supervised training methods to train a model called DeepAquaCluster to segment radar satellite images into areas that separate water from land without the use of any manual annotations. Our final model outperforms other radar-based water detection techniques in our test dataset, achieving a 0.08 improvement in the Intersection Over Union metric.