ADVANCED APPROACH FOR AUTOMATIC RECONSTRUCTION OF 3D BUILDINGS FROM AERIAL IMAGES
In this work, a novel automatic 3D building reconstruction approach is proposed to extract accurate LoD1 building models from multi-view aerial images. The proposed approach consists of three main parts. The first step is to generate digital surface models (DSMs) from aerial images, which is implemented with the Smart3D software and can be replaced by other open-source multi-view stereo (MVS) algorithms as well. The second step is to produce structured 2D building footprints using combined deep learning and regularization. The initial building segmentation maps are obtained by the multi-scale aggregation fully convolutional network (MA-FCN), which takes both the images and DSM as input, through supervised learning. The initial segmentation maps are further refined with another segmentation maps that are derived from the DSM. After that, the contour extraction and regularization technology are applied to produce structured building footprints. In the last step, the elevations of the top and base of each individual building are reliably estimated by adopting an adaptive terrain generation approach and the neighbourhood buffer analysis. The georeferenced building footprint polygons and the elevations of building top and base form the watertight LoD1 building models. The qualitative and quantitative evaluations in Jinghai District, Tianjin, China demonstrate the robustness and effectiveness of the proposed approach.