Unit-level LoD2 Building Reconstruction from Satellite-derived Digital Surface Model and Orthophoto

Abstract. Recent advancements in deep learning have enabled the possibility to identify unit-level building sections from very high resolution satellite images. By learning from the examples, deep models can capture patterns from the low-resolution roof textures to separate building units from duplex buildings. This paper demonstrates that such unit-level segmentation can further advance level of details (LoD)2 modeling. We extend a building boundary regularization method by adapting noisy unit-level segmentation results. Specifically, we propose a novel polygon composition approach to ensure the individually segmented units within a duplex building or dense adjacent buildings are consistent in their shared boundaries. Results of the experiments show that, our unit-level LoD2 modeling has favorably outperformed the state-of-the-art LoD2 modeling results from satellite images.