Conference Agenda

The Virtual Campus Project at the University of Cologne (UoC) has as a main objective the creation of a highly detailed 3D model of the university campus and its publication and distribution through OGC 3D Tiles. Further objectives include the development of integrated applications leveraging this 3D model, such as a web-based 3D viewer, game engine-driven geospatial augmented reality (GeoAR) and virtual reality (VR) experiences, and an indoor positioning system utilizing 3D building models indoor geometries. This paper focuses on and details the methodology for developing and implementing the georeferenced 3D model and establishes an Open Geospatial Consortium (OGC) 3D Tiles-compliant Spatial Data Infrastructure (SDI). The main result is a Tool Suite or Software Framework and the description of the tool pipeline or workflows for collecting, creating and modelling the 3D geospatial data and publishing it as OGC 3D Tiles data. This framework ensures campus-wide 3D data accessibility through 3D Tiles standard clients, including Desktop GIS like ArcGIS or QGIS, game engines like Unity, Unreal or O3DE and Webmapping libraries like MapLibre, three.js or CesiumJS.

The assessment of sidewalk infrastructure for accessibility compliance is an important task in urban planning; however, traditional methods are often manual, subjective, and resource-intensive. This paper introduces BirdCV-LiDAR, a multimodal data fusion framework for an automated assessment of sidewalk infrastructure. The proposed approach integrates high-resolution bird's-eye-view (HR-BEV) imagery with aerial LiDAR point cloud (ALPC) data to automatically detect, measure, and assess sidewalk features for compliance with accessibility standards. By combining YOLO-oriented bounding box (OBB) models with precise LiDAR-based elevation data, the framework enables accurate dimensional and slope evaluations of sidewalk features, such as crosswalks and truncated domes. Validation with a 12-inch inclinometer shows that LiDAR-based slope measurements achieve 84.7% accuracy, with a root-mean-square error (RMSE) of 0.1152 meters for crosswalk width measurements. The framework achieves 81.0% accuracy in determining ADA-PROWAG compliance, providing an adaptable, expandable solution for improved urban accessibility assessments.

This paper proposes a micro-scale walkability metric based on harmonised indicators that supports pedestrian route planning, which prioritises pleasant environments alongside distance efficiency. The employed method quantifies street segments and crossings using geospatial indicators, including pavement width, slope, shade, adjacency to traffic, park context, and crossing type and width. Indicator values are transformed to percentile ranks to harmonise heterogeneous inputs and are aggregated into a single edge-level walkability score on a 0 to 1 scale. The score is integrated into a routing cost function that reduces edge cost with higher walkability, which favours calmer, greener, and wider links while bounding detours relative to the shortest path. The method also accommodates the incorporation of street-level perceptions through a structured survey instrument and a confidence-weighted fusion scheme.

The results show various spatial patterns. Central areas and park-adjacent segments exhibit higher scores, while steep, narrow, and traffic-exposed links score lower, and several suburban and foothill districts display reduced walkability. The comparison with a distance-only baseline shows selection of quieter alignments with modest length increases, indicating potential gains in perceived pleasantness.

Urban areas are continuously expanding outward with economic development and demographic growth, while simultaneously growing higher vertically. However, few efforts have been made to evaluate the impact of development priorities in different regions on urban sustainability, which limited our understanding of how urbanization has been affected by imbalanced evolution rhythm. Here we developed a 3D structure-based approach to assess volumetric urban expansion, as well as a refined evaluation system for assessing urban imbalanced growth trends. Results show that the 3D expansion patterns of urban areas exhibited significant heterogeneity globally. As urbanization accelerates, urban areas in the Global South are showing a trend of faster expansion accompanied by faster vertical growth. In addition, imbalanced growth types across different dimensions are significantly more complicated in the Global South than in the Global North, indicating the variance of development priorities is greater in the Global South. Furthermore, the imbalances are intensifying over time, as indicated by the temporal indices. Our study enhances the understanding of urban 3D patterns and imbalanced urban evolution, providing crucial insights for more balanced urbanization especially in the Global South.

Urban green spaces are important for residents’ physical and mental health, but green visibility is difficult to quantify in high-rise, high-density cities, especially across different height levels. To address this problem, this study proposes a stratified green visibility framework based on airborne LiDAR point clouds and a voxel model. Using the Dutch AHN5 dataset, the study area was converted into a unified 3D voxel space and classified into trees, grass, buildings, ground, and empty space. A voxel-level penetration probability model based on the Beer–Lambert law was introduced to represent the semi-transparent blocking effect of tree canopies, improving upon conventional binary visibility models. Multi-directional line-of-sight (LOS) tracing was then applied to calculate green visibility (GVI) and spatial openness (SOP) at different height layers. The results show that GVI is generally high around parks, large green spaces, and some enclosed courtyards, but its contribution from street trees is limited. Vertically, GVI decreases with height, while SOP tends to increase. Combining the two indicators helps identify different spatial types with distinct visual characteristics. The study demonstrates that airborne LiDAR, combined with voxelization and probabilistic 3D simulation, can effectively capture the vertical variation of urban GVI and support large-area assessment in high-density residential environments.

Accurate building-level mobile device positioning is critical for fine-grained location-based services and human activity analysis, as people spend 80–90% of time indoors. Existing techniques rely on dedicated infrastructure or dense fingerprinting, limiting scalability. This study proposes a lightweight, infrastructure-free framework integrating two core modules: 1) Indoor/outdoor classification via a random forest model trained on a multi-scene sample library, using satellite, Wi-Fi, Bluetooth, and cellular sensor features with similarity-guided training selection; 2) Building matching through a Bayesian inference model leveraging three-scale spatial features (device, building, area) and prior knowledge from anonymous crowdsourced data. Validated in Beijing, Nanjing, and Xi’an, the framework achieves over 90% overall precision for indoor/outdoor classification and ≥70% precision for building matching with satellite or Wi-Fi features alone. It requires no extra infrastructure or extensive labeled data, offering a scalable solution for smart city applications like population analytics, emergency response, and context-aware services across heterogeneous urban regions.

In Japan, the combined effects of declining birth and marriage rates have accelerated population decline, leading to spatial porosity and fragmentation in urbanised areas: a phenomenon known as “Urban spongification”. This study analyses local population distributions in order to identify localised low-population areas embedded within densely populated urban environments, with the aim of understanding spatial porosity and fragmentation in Osaka Prefecture. A multi-scale spatial autocorrelation approach was applied to detect the spatial extent of localised low-population areas, and results were compared between 1995 and 2020. The analysis further examined how the formation and change of localised low-population areas differ across Use Districts and according to long-term land-use transition histories. The findings reveal pronounced spatial variability within districts that cannot be captured by conventional population density metrics alone. The study demonstrates that the emergence, persistence, and transformation of localised low-population areas are closely related to zoning regulations and historical land-use processes. These results provide insights into the spatial processes contributing to urban porosity and fragmentation and offer a basis for future evaluations of residential inducement areas designated under Location Optimisation Plans.