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.
Yongheng Li, Masaya Shimasaki, M. Sakamoto et al.· The International Archives o...· 0 citations
Abstract. Pavement crack segmentation is important for road infrastructure inspection, but practical deployment remains challenging because many high-performance deep learning models require substantial computational resources. This issue is particularly critical in large-scale Mobile Mapping System (MMS)-based workflows, where large volumes of road surface images must be processed efficiently. To address this problem, this study proposes a lightweight CNN-Mamba hybrid architecture for crack segmentation as a deployment-oriented redesign of CT-CrackSeg. The proposed method replaces the original MobileViT-based global modelling modules with EfficientViM-inspired blocks based on hidden-state mixer-based state space duality (HSM-SSD), while preserving the overall encoder-decoder structure and refining the boundary enhancement branch with DCNv2-based deformable convolution. Experiments on the publicly available GAPs384 and CamCrack789 datasets show that the proposed model maintains competitive topology-aware segmentation performance while substantially improving computational efficiency. Compared with CT-CrackSeg, the proposed model increases inference speed from 1.49 to 4.44 FPS on GAPs384 and from 1.32 to 3.92 FPS on CamCrack789, while reducing peak memory consumption from 2827 MB to 355 MB on both datasets. At the same time, the clDice score remains comparable, changing from 0.760 to 0.758 on GAPs384 and from 0.921 to 0.922 on CamCrack789. These results indicate that the proposed architecture provides a favourable balance between crack segmentation quality and deployment efficiency, making it a practical option for large-scale pavement inspection and photogrammetric infrastructure monitoring.
Masaya Shimasaki, M. Sakamoto, Toshiaki Satoh· The International Archives o...· 0 citations
