Point Cloud for 3D Land Administration System (LAS)

Resumo

As cities grow denser and more and more in vertical directions, Land Administration Systems (LAS) must evolve to represent complex, multi-level property ownership, particularly in apartment buildings. While Building Information Models (BIM) are commonly used for 3D representation, their availability remains limited for mainly new buildings. This research explores the use of point clouds as an alternative means to represent 3D spatial units in LAS, focusing on the integration of cadastral floor plans and the airborne Lidar point cloud datasets (in our case the national Actual Height Netherlands or AHN data sets). Three apartment cadastral drawings from different years in Rotterdam serve as case studies. The proposed methodology involves five main steps: (1) parsing the scanned image of the floor plans using image processing to extract cadastral room boundary polygons; (2) segmenting AHN point cloud (3); generating synthetic point clouds by extruding floor plan polygons and aligning them with AHN; (4) storing these 3D spatial units in a PostgreSQL-based database following the ISO 19152-1:2024 Land Administration Domain Model (LADM); and (5) developing a web-based 3D LAS portal using Vue.js, CesiumJS, and FastAPI for visualization and interaction. Results show that by combining AHN and cadastral drawings, the cadastral unit boundaries can be extracted and converted into 3D point clouds for integration into a cadastral database. The synthetic point clouds include room-level attributes and spatial identifiers, enabling interactive visualization and data management through a web interface. However, challenges such as misalignment due to occlusion in AHN data and inconsistent quality in older floor plan drawings affect the accuracy and the level of full automation of the process. This research demonstrates that point clouds can effectively serve as final 3D representations in land administration, providing a scalable solution in the absence of BIM models and minimizing the need for additional field surveys.