Zooming into Uncertainties: Towards Fusing Multi Zoom Level Imagery for Urban Land Use Segmentation

Abstract

Urban land use prediction is an ill-posed problem from a remote sensing perspective. Some areas are easy to predict with aerial images, e.g. residential areas or industrial areas, whereas it is nearly impossible to predict land use in dense urban centers with highly mixed land use. In this study, we use a fully convolutional, Bayesian neural network for urban land use segmentation that yields predictions and pixel-wise uncertainty values side-by-side. By adding aleatoric uncertainty to the output of our model, we can assess how much the model benefits from the provided data. We train our network using a dataset from four metropolitan areas in the U.S. on two different zoom levels. Our results show that adding aleatoric uncertainty can improve the IoU scores if a sufficient amount of informative data is provided.