CASCADE-3D: A GUI-Driven Framework for Automated 3D Building Model Reconstruction

Abstract

The generation of rapid and accurate geospatial data and three-dimensional (3D) features is essential for supporting multipurpose land management services. This study presents CAdastre and Spatial map adjustment with spatial Computation for Automatic builDing dEtection and 3D generation (CASCADE-3D), a graphical user interface (GUI) developed for the automated reconstruction of 3D models at Levels of Detail (LOD) 1 and 2. CASCADE-3D integrates advanced deep-learning frameworks to perform building outline detection and point cloud classification. Building outlines are extracted using SAM, a promptable segmentation system capable of zero-shot generalization to unfamiliar objects and images without requiring additional training. The CASCADE-3D GUI enables interactive digitization, automatic regularization, and refinement of the segmentation mask based on its primary orientation. Each building height model (BHM) is generated by classifying raw point clouds with the DGCNN algorithm to extract ground and building classes. Accurate reconstruction of complex LOD2 models requires precise extraction of roof structures that captures the geometric configuration and orientation of roofs in intricate architectural forms. To achieve this, roof structure detection techniques were applied using each building’s aspect. The study utilized point clouds and orthophotos of 1,215 buildings, encompassing diverse architectural forms and land cover types, across several provinces in Indonesia. The CASCADE-3D GUI was evaluated for its accuracy in detecting building outlines and roof structures, and performing LOD1/2 reconstruction. The results indicate that the reconstructed 3D building geometries yielded an RMSE of 0.36 m. Subsequently, CASCADE-3D reconstructs LOD1 and LOD2 building models and exports them in CityJSON format.