Spatially constrained deep semantic segmentation of geospatial imagery for building footprint extraction

Abstract

Recent advances in machine learning for geospatial imagery have facilitated image analysis for tasks such as building footprint extraction and urban land cover classification. The current state of the art semantic segmentation networks (including the many variants of the U-Net architecture) have shown promise for such tasks but have a shortcoming in that the networks utilize a loss function that is only computed per pixel. This precludes spatial context from being leveraged as part of the objective function during the training phase for the models. In this study, we propose a modified loss function for semantic segmentation networks that incorporates the spatial context from the ground truth images in efforts to improve building footprint extraction. Specifically, our approach uses neighborhood pixels to provide an adjustment factor for model training. In this work, we use imagery from the SpaceNet-2 dataset consisting of aerial images of buildings vs. landscape. We demonstrate that by adding spatial context to the loss function of semantic segmentation networks, the semantic features extracted by such networks are better aware of spatial context which can help the underlying segmentation task. Our experiments demonstrate both quantitative (e.g. via DICE scores) and qualitative (e.g. via more effective building footprint extraction) improvement to semantic segmentation networks when the proposed loss function is incorporated compared to when it is not. Using the proposed spatially aware loss function, the resulting U-Net converges faster than when using a standard binary cross entropy loss function. This improvement comes at no additional expense with regards to the amount of training data used, modification of model architecture or an increased number of parameters.