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 many 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 Actueel Hoogtebestand Nederland (AHN)). 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 PostgreSQLbased database following the ISO 19152:2024 Land Administration Domain Model (LADM); and (5) developing a web-based 3D LAS using Vue.js, Cesium, and FastAPI for visualization and interaction. Results show that unit boundaries can be extracted from cadastral drawings 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 LADM information through a web interface that can be accessed by the public and stakeholders. However, challenges such as misalignment due to occlusion in AHN data and inconsistent quality in older floor plan drawings affect the accuracy and 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. It also enables a seamless integration with AHN, offering a representation of real-world features such as building facades, walls, and fences, which often delineate cadastral boundaries. The code for this project is available in GitHub, while the website can be accessed in gist.bk.tudelft.nl/apps/LADMPointCloud/.

By providing shade for residents in urban areas, cool spaces have been shown to be essential for mitigating the effects of heat stress. In response, the Municipality of Amsterdam developed a map showing walking distances to these spaces. However, the map lacks key information on capacity, accessibility, and precise distance measurements. This project addresses these gaps by identifying quality indicators for cool places and mapping their locations and quality scores across Amsterdam. Additionally, it establishes methods for computing the shortest and shadiest pedestrian routes to these spaces, enabling efficient routing to and from any given location.

To address the research questions, the following procedures were conducted. First, shade maps of Amsterdam were created for each warm month using the Daily Shadow Pattern tool of the Urban Multi-scale Environmental Predictor (UMEP). Second, cool spaces were identified and evaluated based on accessibility, shading, usability, capacity, heat risk, and Physiological Equivalent Temperature (PET) indicators. Lastly, after obtaining and processing the pedestrian network from the Open Street Map database, shade weight was calculated for each street segment, and cool spaces were incorporated into the network, allowing users to generate datasets of the shortest and shadiest distances to cool spaces, and an algorithm that performs four different routing options: the shortest, the shadiest, and two combinations of the shortest and shadiest paths with different weighting ratios either between two locations or from a starting point to its nearest cool space.

The project produced datasets which provide insights into Amsterdam’s cool spaces, their quality, and the shadiest and shortest routes to these locations. Additionally, the code to make these datasets has been made available on GitHub.