Climate changes and coastal development pose growing risks to coastal roadways constructed on flat and low-elevation terrains near retreating shorelines. Although GIS has been widely used for shoreline change analysis and roadway management, significant limitations remain in accurately analyzing shoreline changes relative to roadways and integrating the analysis results into roadway spatial databases in the Geographic Information System (GIS). In this regard, this study proposes a novel geospatial approach that integrates the linear referencing system (LRS) with the vector-offset based analysis method for shoreline change. The LRS, implemented in GIS, defines the specific positions of a roadway using relative distances from predefined referents. Vector offsets, representing the shortest distance and direction from historical shorelines to the roadway, are then employed to analyze shoreline changes. The proposed approach was applied to a coastal roadway experiencing significant shoreline changes driven by climate change and the construction of coastal infrastructure. The results demonstrate the effectiveness of the proposed approach in analyzing shoreline retreat caused by coastal infrastructure development, as well as shoreline accretion following the installation of erosion control structures. These results, which closely reflect the actual erosion pattern, indicate that the proposed approach can effectively support planning for roadway maintenance and reinforcement.

The integration of Building Information Modeling (BIM) and Digital Twin (DT) has emerged as an innovative tool in the architecture, engineering, and construction (AEC) domain. To successfully utilize BIM and DT, it is crucial to update the 3D model in a timely and accurate manner. However, limitations remain when handling massive point clouds to reconstruct complex indoor structures with varying ceiling and floor heights. This study proposes a semi-automatic 3D model reconstruction method. First, point clouds are aligned with 3D Cartesian axes and the spatial extent of the indoor space is measured. Subsequently, the point clouds are projected onto each coordinate plane to hierarchically extract structural elements of a building component, such as boundary lines, rectangles, and cuboids. Boolean operations are then applied to the cuboids to reconstruct a 3D wireframe model. Additionally, wall points are segmented to identify openings like doors and windows. For validation, the method was applied to three typical building components with Manhattan-world structures: an office, a hallway, and a stairway. The reconstructed models were evaluated using reference points, resulting in positional accuracies of 0.033 m, 0.034 m, and 0.030 m, respectively. Finally, the resulting wireframe model served as a reference to build an as-built BIM model.