Conference Agenda

As a fundamental map covering the whole land of Japan, GSI (Geospatial Information Authority of Japan) maintains the map called the Digital Japan Basic Map. The Digital Japan Basic Map has been prepared as 2D map information. Due to Cabinet Decisions made on 2023, GSI is required to implement 3D mapping across the whole country by 2028 and to start publishing the 3D maps sequentially in public in FY2025. The purpose of this presentation is to introduce the improvement of three-dimensionalization method for roads and railroads in 3D Digital Japan Basic Map conducted in FY2024.

Urban digital twins are becoming essential for transportation applications, demanding precise geometric, semantic, and topological data. However, existing transportation infrastructure information is typically available in 2D formats, while many applications require accurate 3D representations. Existing 3D representations, such as point cloud data, often lack integrated semantic information. This paper addresses this gap by presenting a novel method for the automatic transformation of semantic 2D lane models into 3D CityGML representations. The transformation process comprises three main phases: (1) Point cloud data processing: Noise and irrelevant structures are removed, retaining essential 3D lane features, and elevation information is derived by converting the point cloud data into digital elevation models (DEMs); (2) Segmentation and smoothing: Extracted DEMs undergo segmentation, noise removal, and refinement to ensure geometric continuity; and (3) Transformation and postprocessing: The semantic 2D lane models are integrated with the processed DEMs through elevation interpolation, followed by refinement and transformation into 3D CityGML representations. Compared to existing methods, the proposed method delivers more realistic and comprehensive 3D lane models while maintaining efficiency. A case study in City X demonstrates the algorithm’s effectiveness in addressing challenges in complex scenarios including tunnels and bridges. The paper concludes by discussing encountered challenges and proposing future research directions to advance the integration of 2D and 3D transportation infrastructure information.

Accurate semantic modelling of urban road infrastructure is critical for digital twins, traffic simulations, and smart city planning. This study presents a structured methodology to transform road elements segmented from urban point clouds into CityGML 3.0-compliant representations. Leveraging CityGML’s hierarchical Transportation module, the approach introduces a multi-level granularity framework—area, way, and lane—for representing road components like sidewalks, driving lanes, and parking areas. Following geometric pre-processing, segmented surfaces are semantically mapped into appropriate CityGML classes using a rule-based mapping strategy, enriched with descriptive attributes and hierarchical identifiers. The resulting XML-based datasets were validated and visualized using industry-standard tools such as FME, QGIS, and 3DCityDB, demonstrating successful integration into city-scale digital environments.