Change detection in the built environment is essential for sustainable development practices including Urban Planning and Development, Environmental Monitoring, and Conservation. Change detection provides valuable insights into dynamic processes, facilitates informed decision making, and supports sustainable development initiatives. Point clouds serve as foundational data sources for change detection in built environments, enabling analysts to detect, quantify, and interpret spatial changes with unparalleled accuracy and granularity. By leveraging the inherent characteristics of point clouds, researchers and practitioners can gain valuable insights into dynamic processes, inform decision making, and foster sustainable development in an ever-evolving built environment. We present the preliminary results of cloud-to-cloud (c2c) distance calculations for further change detection analysis of the entire Netherlands. This study utilises point cloud data from AHN2, 3, and 4 (Actueel Hoogtebestand Nederland1, The Netherlands). A method based on a 3D space-filling curve (SFC) was developed to calculate the c2c distances between AHN2, 3, and 4. This SFC method will allow change detection analysis to be carried out for the entire Netherlands. The change detection analysis outcomes can be accessed for future analysis in Potree2, a web-based point cloud rendered for large point clouds. The final implementation will allow the visualisation of AHN point clouds and their attributes, among which is the change in detection-related information. This research contributes to sustainable development practices by offering enhanced spatial insights and informed decision-making tools for further analysis and monitoring of the (built) environment in the Netherlands. [...]@en

This paper discusses the representation of the scale dimension using tGAP/SSC-DHE, emphasizing its role in creating vario-scale representations. It outlines the process of integrating the scale dimension as the third dimension in a 2D map before transitioning to 3D or 4D maps. DHE is introduced as an effective data structure for this approach, maintaining crucial connections across different levels of detail and offering enhanced topology. Its strength lies in the ability to modify specific parts of the model while retaining a clear structure, thanks to well-defined operators such as Euler operators. This method provides a versatile framework for representing complex spatial data, allowing for detailed analysis while preserving overall coherence and it updates the shape and structure of the model at the same time through local modifications. Through practical implementation, it demonstrates the potential for improved visualization and understanding of multi-dimensional spatial relationships. Also, the DHE data structure supports slicing by either a horizontal plane to produce a map at a specific scale (depending on the height of the horizontal plane) and tilted planes to produce perspective views with mixed scales.@en

Cloud-to-cloud (C2C) distance calculations are frequently performed as an initial stage in change detection and spatiotemporal analysis with point clouds. There are various methods for calculating C2C distance, also called inter-point distance, which refers to the distance between two corresponding point clouds captured at different epochs. These methods can be classified from simple to complex, with more steps and calculations required for the latter. Generally, it is assumed that a more complex method will result in a more precise calculation of inter-point distance, but this assumption is rarely evaluated. This paper compares eight commonly used methods for calculating the inter-point distance. The results indicate that the accuracy of distance calculations depends on the chosen method and a characteristic related to the point density, the intra-point distance, which refers to the distance between points within the same point cloud. The results are helpful for applications that analyze spatiotemporal point clouds for change detection. The findings will be helpful in future applications, including analyzing spatiotemporal point clouds for change detection.@en

Automated map generalization has been a major area of research for decades but has still not reached maturity. Besides the needs for more adaptive algorithms, a fundamental question remains: How can we transfer human generalization knowledge into a computational system more effectively? Previous efforts do not seem capable to fully overcome the “knowledge acquisition bottleneck.” As new theories and technologies emerged in artificial intelligence (particularly deep learning), computers are now able to tackle human-level tasks with superior performance, showing great potential in automated generalization. Meanwhile, crowdsourced geographic information and social sensing is growing at an increasing speed, and the needs for visualizing and analyzing massive geo-referenced data at various scales are numerous. It is therefore necessary to adapt map generalization to these fields. This highlights the potential of applying map generalization in the visual, interactive, and exploratory analysis of abstract (e.g., hierarchical relations) and physical (e.g., movement trajectories) data. This topical collection brings six contributions reporting recent progress and trends in automated generalization in various aspects mentioned above, with which we hope to trigger further discussion and research in our field with new ideas and methodologies.@en

The advantages of using point clouds for change detection analysis include comprehensive spatial and temporal representation, as well as high precision and accuracy in the calculations. These benefits make point clouds a powerful data type for spatio-temporal analysis. Nevertheless, most current change detection methods have been specifically designed and utilized for raster data. This research aims to identify the most suitable cloud-to-cloud (c2c) distance calculation algorithm for further implementation in change detection for spatio-temporal point clouds. Eight different methods, varying in complexity and execution time, are compared without converting the point cloud data into rasters. Hourly point cloud data from monitoring a beach-dune system's dynamics is used to carry out the comparison. The c2c distance methods are (1) the nearest neighbor, (2) least squares plane, (3) linear interpolation, (4) quadratic (height function), (5) 2.5D triangulation, (6) natural neighbor interpolation (NNI), (7) inverse distance weight (IDW) and (8) multiscale model to model cloud comparison (M3C2). We evaluate these algorithms, considering both the accuracy of the calculated distance and the execution time. The results can be valuable for analyzing and monitoring the (build) environment with spatio-temporal point cloud data.@en

When users zoom in or out on a digital map, the map should change correspondingly to present geographical information at proper levels. A way to help map users better keep track of their interested objects is to change the map smoothly instead of discretely switching between several levels of detail. This paper focuses on the problem of providing smooth merging of area objects. We propose to merge multiple areas simultaneously to share their animation durations. In this way, each merging operation can be prolonged, and it is visually smoother. We present a greedy algorithm to decide which areas should be merged at each step. The merging process is pre-computed and is recorded into a space-scale cube (SSC). When a user accesses our web map, the SSC is sent to the client side so that the map can be generated by slicing the SSC in the graphics processing unit (GPU). We also explain how to snap the zooming to valid states so that the zooming will not stop halfway of the merging operations. Our case study shows that it is visually smoother to merge simultaneously than to sequentially merge each pair of areas.

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Point cloud is made up of a multitude of three-dimensional (3D) points with one or more attributes attached. Point cloud is the third data paradigm in addition to the well-established object (vector) and gridded (raster) representations, since point cloud data can be directly collected, computed, stored, and analyzed without converting to other types. Modern ways of data acquisition, including laser scanning from airborne, mobile, or static platforms, multi-beam echo-sounding, and dense image matching from photos, generate millions to trillions of 3D points with attached attributes. If the collection is carried out in different periods, one of the essential attributes is precisely time, allowing spatiotemporal analysis to be performed. Its use is widespread in some fields such as metrology and quality inspection, virtual reality, indoor/outdoor navigation, object detection, vegetation monitoring, building modeling, cultural heritage, and diverse visualization applications. There are some examples in fields related to hydroinformatics, mainly related to terrain modeling. Due to its nature of big data, over the past decades, a series of developments have been carried out in the different processing chains for the optimal use of point cloud. This research seeks to introduce the various point cloud developments from which the hydroinformatics community and research could benefit. A review of recent advances is made, mainly including the analysis and visualization of point cloud for dealing with water-related problems. Potential areas of application and development in hydroinformatics are identified. These include, for example, the topics of coastal monitoring, coastal erosion, shallow water assessment, ice sheet change analysis, sea-level rise assessment, monitoring of levels in water bodies, crop and vegetation monitoring, analysis of the effects of groundwater depletion, detail tracing of basins and channels, analysis of floods with detailed terrain models, and drought monitoring in crops and forests. The challenges to overcome and ongoing developments regarding point cloud application in hydroinformatics are also discussed.@en

We investigate how PCServe, a web service for disseminating massive point clouds, performs for read-only access (i.e. a visualization application). PCServe is backed by a database model based on Space Filling Curves. By adding a virtual hierarchy of blocks to the database, we can support different visualization applications for retrieval of point cloud data over the web without having to store the data multiple times. This makes expressive access to point clouds over the web possible. We investigate the amount of processing that is needed to create the database model and how well PCServe handles requests from the visualization application. Some suggestions are provided how the current approach can be improved.

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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.

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A web-based application has been developed, exploiting the integrated model of LADM andIndoorGML to provide indoor navigation based on the user's access rights in an educationalbuilding. Different types of users (students, teachers, visitors, etc.) have different access rights,which also depend on the exact time (e.g. inside or outside office hours). A 3D BIM IFC fileof a building has been geo-referenced and converted into a LADM complaint database inPostgreSQL/PostGIS and is enriched with information about access rights based on therelationship between users, time and indoor spaces. The PostgreSQL extension pgRouting hasbeen used for the actual routing. To support the access rights-based routing, the databasecontains several tables to represent nodes, edges, parties (users), and rights. There is one overallnetwork for the whole building, and database views are used to dynamically select the relevantnodes and edges based on the time and the user’s rights. The Dijkstra algorithm is used tocompute the shortest path. Finally, the 3D geospatial web-platform Cesium JS is used to createa client GUI allowing to specify start and destination, the user and time, and to visualize thenavigation routes. As this GUI is web-based it can run on different platforms, such as desktops,laptops, tablets and mobile phones. This paper provides a complete description of all the stepsto design, develop and test the integrated model of LADM and IndoorGML.@en

Governments use flood maps for city planning and disaster management to protect people and assets. Flood risk mapping projects carried out for these purposes generate a huge amount of modelling results. Previously, data submitted are highly condensed products such as typical flood inundation maps and tables for loss analysis. Original modelling results recording critical flood evolution processes are overlooked due to cumbersome management and analysis. This certainly has drawbacks: the ĝ€ static' maps impart few details about the flood; also, the data fails to address new requirements. This significantly confines the use of flood maps. Recent development of point cloud databases provides an opportunity to manage the whole set of modelling results. The databases can efficiently support all kinds of flood risk queries at finer scales. Using a case study from China, this paper demonstrates how a novel nD-PointCloud structure, HistSFC, improves flood risk querying. The result indicates that compared with conventional database solutions, HistSFC holds superior performance and better scalability. Besides, the specific optimizations made on HistSFC can facilitate the process further. All these indicate a promising solution for the next generation of flood maps.

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Efficient spatial queries are frequently needed to extract useful information from massive nD point clouds. Most previous studies focus on developing solutions for orthogonal window queries, while rarely considering the polytope query. The latter query, which includes the widely adopted polygonal query in 2D, also plays a critical role in many nD spatial applications such as the perspective view selection. Aiming for an nD solution, this paper first formulates a convex nD-polytope for querying. Then, the paper integrates three approximate geometric algorithms – SWEEP, SPHERE, VERTEX, and a linear programming method CPLEX, developing a solution based on an Index-Organized Table (IOT) approach. IOT is applied with space filling curve based clustering and advanced querying mechanism which recursively refines hypercubic nD spaces to approach the query geometry for primary filtering. Results from experiments based on both synthetic and real data have confirmed the superior performance of SWEEP. However, the algorithm may lag behind CPLEX due to pessimistic intersection computation in high dimensional spaces. In a real application, by properly transforming a perspective view selection into a polytope query, the solution achieves a sub-second querying performance using SWEEP. In another flood risk query, SWEEP also leads the others. In general, the robust and efficient solution can be immediately used to address different polytope queries, including those abstract ones whose constraints on combinations of different dimensions are formed into a polytope model. Besides, the knowledge of high-dimensional computations acquired also provides significant guidance for handling more nD GIS issues.

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As an extension to 2D polygonal queries, the nD-polytope queries on point clouds also play a crucial rolein nD GIS applications such as the perspective view selection. This report rst denes the nD-polytopemathematically, and then develops an ecient nD-polytope querying solution by extending an index-organized table (IOT) approach. The solution integrates four novel intersection algorithms includingCPLEX, SWEEP, SPHERE and VERTEX, each of which can be used to realize the primary lteringfor polytope querying. The performance of these algorithms is then measured and compared using anrepresentative nD-simplex and an nD-prism query region, respectively. It turns out that SWEEP performsthe best over all, but it may degrade signicantly as dimensionality goes up. On the other hand, thelinear programming algorithm CPLEX although takes more time on intersection computation, performsmore stable. Besides, the experiments also reveal that the properties of a same geometry can changesignicantly across dierent dimensionality, and thus optimal strategies developed in 2D/3D may not beapplicable in high dimensional spaces.@en

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.

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Many of the point cloud processing techniques have their origin in image processing. But mathematical morphology, despite being one of the most used image processing techniques, has not yet been clearly adapted to point clouds. The aim of this work is to design the basic operations of mathematical morphology applicable to 3D point cloud data, without the need to transform point clouds to 2D or 3D images and avoiding the associated problems of resolution loss and orientation restrictions. The object shapes in images, based on pixel values, are assumed to be the existence or absence of points, therefore, morphological dilation and erosion operations are focused on the addition and removal of points according to the structuring element. The structuring element, in turn, is defined as a point cloud with characteristics of shape, size, orientation, point density, and one reference point. The designed method has been tested on point clouds artificially generated, acquired from real case studies, and the Stanford bunny model. The results show a robust behaviour against point density variations and consistent with image processing equivalent. The proposed method is easy and fast to implement, although the selection of a correct structuring element requires previous knowledge about the problem and the input point cloud. Besides, the proposed method solves well-known point cloud processing problems such as object detection, segmentation, and gap filling.@en

This paper presents a web map juxtaposition comparer. Using the comparer, we can place two maps side by side for comparison. The maps of the comparer can contain multiple layers, and each of them can be a multi-scale layer or a vario-scale layer. We enhance the comparer’s visual analytical ability by developing more functionalities, including toggling on/off the layers, tuning the opacities, and swiping to change the maps’ widths. The aim of developing the web map juxtaposition comparer is to carry out usability studies to see if a vario-scale map is better then a multi-scale map in helping map users to keep their context during zooming. This paper presents two versions of the comparer. The first one uses respectively a multi-scale and a vario-scale vector layer of area objects in the left map and in the right map. Both maps use the same multi-scale raster layer as the background. The second comparer also uses the raster layer as the background, and it uses three thematic layers as the foreground. The change between the thematic layers is realized by switching on/off according to scales. In future, we will implement continuous changes between the three thematic layers for the right map.@en

Navigational charts play a vital role in a ship's safety while navigating the seas, rivers or lakes. With most of the features and obstructions being out of sight -below sea-level - these charts are more critical than e.g. topographic maps. For routing but also positioning, depth information is a key aspect on these charts. This depth information is available in either depth contours, coloured depth areas or individual spot soundings. However with the data originating from accurate but usually erratic survey data, a visualisation of raw data is not sufficient for use in a navigational chart directly. It would not clearly convey the information to a human operator in one sight, and thus this visualisation is in need of generalisation: a simplified and hopefully more useful representation of the same data. In navigational isobath generalisation four main generalisation constraints can be distinguished: legibility, morphology, functional and topological. Isobaths should always be topological valid and safe (hard constraints). Generalised isobaths may never indicate an area being deeper than it was actually measured in the first place, and thus can only be moved to their deeper side. The objective of yielding a legible and useful chart concretely means having smooth and distinguishable lines. Irrelevant details (isolated pits) should be omitted and dangerous obstacles (isolated shoals) can be emphasised. Yielding a legible isobath representation is in constant compromise with the constraint of representing morphology as good as possible. Every operation increasing legibility of the chart lead to a reduced representation of the actual morphology (and with it a reduction of navigable space). This ever going compromise is what makes navigational isobath generalisation such a complex task. Choices are different for different chart scales, chart purposes and even different areas within each chart (e.g. fairways, anchorages, nature reserves require different amounts and types of generalisation).@en

Space Filling Curve (SFC) mapping-based clustering and indexing works effectively for point clouds management and querying. It maps both points and queries into a one-dimensional SFC space so that B+- tree could be utilized. Based on the basic structure, this paper develops a generic HistSFC approach which utilizes a histogram tree recording point distribution for efficient querying. The goal is to resolve the issue of skewed data querying. Besides, the paper proposes an agile method to compute a continuous Level of Detail (cLoD), and integrates it into HistSFC to support smooth rendering of massive points. Results indicate that for range queries, HistSFC decreases the False Positive Rate (FPR) of selection by maximally 80%, compared to previous approaches. It also performs significantly faster than the state-of- the-art Oracle SDO_PC solution. With improved performance on visualization and k Nearest Neighbour (kNN) search, HistSFC can therefore be used as a new standard solution. @en