As urban environments become increasingly vertical, Land Administration Systems (LAS) must support complex 3D spatial representations. While Building Information Models (BIM) offer such capabilities, they are not always available. This paper investigates an alternative approach using point clouds for 3D LAS, focusing on the integration of scanned cadastral floor plans and airborne LiDAR from the Actueel Hoogtebestand Nederland (AHN). We present a semi-automated pipeline that extracts floorplan geometries, segments and enhances AHN data, and synthesizes room-level point clouds. Results from a case study in Rotterdam demonstrate the potential of this approach in the absence of BIM, supporting legal space definition and public visualization. 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. The synthetic point clouds include room-level attributes, enabling a seamless integration with AHN, offering a representation of real-world features such as building facades, walls, and fences, which often delineate cadastral boundaries.

Point clouds contain high detail and high accuracy geometry representation of the scanned Earth surface parts. To manage the huge amount of data, the point clouds are traditionally organized on location and map-scale; e.g. in an octree structure, where top-levels of the tree contain few points suitable for small scale overviews and lower levels of the tree contain more points suitable for large scale detailed views. The drawback of this solution is that it is based on discrete levels, causing visual artifacts in the form of data density shocks when creating the commonly used perspective views. This paper presents a method based on an optimized distribution of points over continuous levels, avoiding the visualization shocks. The traditional distribution ratio's of data amounts over discrete levels of raster or vector data is considered the reference. How to convert this to point clouds with continuous levels (still benefiting from the proven advantages of the data distribution in discrete levels for efficient access at a wide range of scales)? In our solution, for each point a cLoD (continuous Level of Detail) value is computed and added as dimension to the point. A SFC (Space Filling Curve)-based nD data clustering technique can be used to organize the points, so that they can be efficiently queried. It should be noted that also other multi-dimensional indexing and clustering techniques could be applied to realize continuous levels based on the cLoD value. Besides the mathematical foundation of the approach also several implementations are described, varying from a 3D web-browser based solution to an augmented reality point cloud app in a mobile phone. The cLoD enables interactive real-time visualization using perspective views without data density shocks, while supporting continuous zoom-in/out and progressive data streaming between server and client. The described cLoD based approach is generic and supports different types of point clouds: from airborne, terrestrial, mobile and indoor laser scanning, but also from dense matching optical imagery or multi-beam echo soundings.

Point cloud data have rich semantic representations and can benefit various applications towards a digital twin. However, they are unordered and anisotropically distributed, thus being unsuitable for a typical Convolutional Neural Networks (CNN) to handle. With the advance of deep learning, several neural networks claim to have solved the point cloud semantic segmentation problem. This paper evaluates three different neural networks for semantic segmentation of point clouds, namely PointNet++, PointCNN and DGCNN. A public indoor scene of the Amersfoort railway station is used as the study area. Unlike the typical indoor scenes and even more from the ubiquitous outdoor ones in currently available datasets, the station consists of objects such as the entrance gates, ticket machines, couches, and garbage cans. For the experiment, we use subsets from the data, remove the noise, evaluate the performance of the selected neural networks. The results indicate an overall accuracy of more than 90% for all the networks but vary in terms of mean class accuracy and mean Intersection over Union (IoU). The misclassification mainly occurs in the classes of couch and garbage can. Several factors that may contribute to the errors are analyzed, such as the quality of the data and the proportion of the number of points per class. The adaptability of the networks is also heavily dependent on the training location: the overall characteristics of the train station make a trained network for one location less suitable for another.

In guiding the energy transition efforts towards renewable energy sources, 3D city models were shown to be useful tools when assessing the annual solar energy generation potential of urban landscapes. However, the simplified roof geometry included in these 3D city models and the lack of additional semantic information about the buildings' roof often yield less accurate solar potential evaluations than desirable. In this paper we propose three different methods to infer and store additional information into 3D city models, namely on physical obstacles present on the roof and existing solar panels. Both can be used to increase the accuracy of roof solar panel retrofit potential. These methods are developed and tested on the open datasets available in the Netherlands, specifically AHN3 lidar point-cloud and PDOK aerial photography. However, we believe they can be adapted to different environments as well, based on the available datasets and their precision locally available.

Emergency operations are a key example for the need of digital twins in the way it is complex, urgent and uncertain. First, the process is complex, as many organizations are involved. Second, it is urgent, as most damage is done in the first moments of an emergency. Third, it is uncertain, as situational conditions tend to change quickly. For outdoor operations, spatial information systems help in creating an overview of the situation, for example by displaying positions of first responder units involved with the incident. However, spatial data of indoor environments is scarce. Static information of the building, such as floor plans, are often outdated or non-existent. Dynamic operational data such as positions of first responders within the building are only available in a very limited way as well, and often without visual representation. To create situation awareness of indoor first responder operation environments, this paper successfully proposes a proof of concept with two objectives. First, the proof of concept will collect spatial environment data in the form of mapping and tracking data by using a Microsoft HoloLens. This means the geometry of the building will be collected, together with traversed routes within the building. Second, the data will be streamed and displayed to a remote first responder coordinator in real-time to create a common operational picture. This enables the coordinator to quickly build situation awareness of the operation environment, enabling the coordinator to improve the quality of decisions, thereby improving first responder performance. The proof of concept showed that situation awareness on all three levels increases with the real-time (live) availability (visualisations) of 3D indoor environments. This concept needs to be tested further on usability and performance.

Occlusions accompany serious problems that reduce the applicability of numerous algorithms. The aim of this work is to detect and characterize urban ground gaps based on occluding object. The point clouds for input have been acquired with Mobile Laser Scanning and have been previously segmented into ground, buildings and objects, which have been classified. The method generates various raster images according to segmented point cloud elements, and detects gaps within the ground based on their connectivity and the application of the hit-or-miss transform. The method has been tested in four real case studies in the cities of Vigo and Paris, and an accuracy of 99.6% has been obtained in occlusion detection and labelling. Cars caused 80.6% of the occlusions. Each car occluded an average ground area of 11.9 m2. The proposed method facilitates knowing the percentage of occluded ground, and if this would be reduced in successive multi-temporal acquisitions based on mobility characteristics of each object class.