Automatic Reconstruction of High-Accuracy 3D Roof Models from Orthophotos and Digital Surface Models

Abstract. In recent years, the demand for 3D city model development has grown, as demonstrated by initiatives such as Project PLATEAU in Japan. In the construction of LoD2 building models, which are an essential component of 3D city models, the reconstruction of 3D roof models still heavily depends on manual work. To enhance productivity through automation, this study proposes a novel method for automatically reconstructing high-accuracy 3D roof models using orthophotos and Digital Surface Models (DSMs) derived from aerial imagery. In the proposed method, a deep-learning-based model is first applied to orthophotos and DSMs to extract 2D rooflines. Then, the extracted 2D rooflines are refined and polygonised to assemble 2D roof models. Finally, planar fitting was performed on the point cloud generated from the DSM within each 2D roof plane to reconstruct 3D roof models. In this process, the horizontal alignment of rooflines and the continuity between adjacent roof planes were preserved. In the experiments, 3D roof models manually digitized by stereoscopic measurement were used as the ground truth, and the automatically reconstructed 3D roof models were evaluated by comparison with this reference. As a result, the recall values for 2D and 3D roof planes were 0.686 and 0.430, respectively, and increased to 0.723 and 0.455 for roof planes larger than 4 m².