AN END-TO-END DEEP LEARNING WORKFLOW FOR BUILDING SEGMENTATION, BOUNDARY REGULARIZATION AND VECTORIZATION OF BUILDING FOOTPRINTS

Abstract. Automatic building footprint extraction from remote sensing imagery is a widely used method, with deep learning techniques being particularly effective. However, deep learning approaches still require additional post-processing steps due to pixel-wise predictions, that contribute to occluded and geometrically incorrectly segmented buildings. To address this issue, we propose an end-to-end workflow that utilizes binary semantic segmentation, regularization, and vectorization. We implement and assess the performance of four convolutional neural network architectures including U-Net, U-NetFormer, FT-UnetFormer, and DCSwin on the MapAI Precision in Building Segmentation competition. To additionally improve the shape of the predicted buildings we apply regularization on the predictions to assess whether regularization further improves the geometrical shape and improve the prediction accuracy. We aim to produce accurate predictions with regularized boundaries that can prove useful in many cartographic and engineering applications. The regularization and vectorization workflow is further developed into a working QGIS-plugin that can be used to extend the functionality of QGIS. Our aim is to provide an end-to-end workflow for building segmentation, regularization and vectorization.