Fine-resolution spatial analytics are essential for urban planning and policy-making, yet traditional small-area estimation often struggles with sparse, hierarchical, or imbalanced data. This paper introduces a Spatially Regularised Bayesian Heterogeneous Graph Neural Network (SR-BHGNN) that integrates multiple census tract levels within a unified framework. The model builds a heterogeneous graph where nodes represent spatial units at different scales, edges encode adjacency or membership, and Bayesian inference quantifies uncertainty in parameters and predictions. A spatial regularisation term, inspired by Tobler’s First Law of Geography, penalises large discrepancies between neighbouring nodes, reducing errors in imbalanced datasets and ensuring coherent local estimates. We evaluate SR-BHGNN through two London case studies, population estimation and PM 2.5 prediction, comparing it against random forests, single-level GNNs, and spatial hierarchical Bayesian estimation. SR-BHGNN achieves strong performance gains, with classification accuracies of 0.85 for population estimation and 0.81 for PM 2.5 prediction. Its Bayesian design produces posterior distributions that capture uncertainty, enabling policy-relevant insights into vulnerable neighbourhoods or priority intervention zones (e.g. low-emission areas). These results demonstrate that SR-BHGNN advances the state of the art in small-area estimation, offering a flexible, uncertainty-aware framework for diverse urban analytics applications.

Transitions of spatial data infrastructures (SDIs) support applications from 2D landscapes to 3D scenes. The existing methods for describing, managing, and providing services for 3D spatial data often lack coordination and efficiency. Moreover, the added complexity of 3D data structures necessitates novel approaches for component-level management and streaming capabilities. In response, we developed a generic conceptual model suitable for component-level management of diverse 3D spatial data in SDIs and discussed the design rationales and key considerations underlying the model. We formalized the flexible data composition and fine-grained lifecycle management in this model and specified this model at the cloud-optimized encoding level to enable efficient CRUD operations and streaming delivery of massive 3D spatial data. Our approach enabled direct streaming of the managed 3D spatial data without the need for redundant replication. We implemented, evaluated, and discussed the proposed approach in terms of service, accessibility, visualization, analysis cases, and efficiency. The results show that the proposed method is efficient in managing 3D spatial data and enables users to conduct 3D geo-analysis on the basis of specific parts of the data as needed. This work provides a scientific exploration that integrates the management and services of 3D spatial data in SDIs.

Street view imagery (SVI), an emerging geospatial dataset, is useful for evaluating active transportation infrastructure, but it faces potential biases from its vehicle-based capture method, diverging from pedestrians’ and cyclists’ perspectives. Existing literature lacks both an examination of these biases and a solution. This study identifies and quantifies these biases by comparing conventional SVI with views from the road shoulder/sidewalk. To mitigate such perspective biases, we introduce a novel framework with generative adversarial network (GAN)-based image generation models (Pix2Pix and CycleGAN), an image regression model (ResNet-50), and a tabular model (LightGBM). Experiments assessed model effectiveness in translating car-centric views to those from pedestrian and cyclist perspectives. Results show significant differences in semantic indicators (e.g. green view index) between road center and road shoulder/sidewalk SVI, with low Pearson’s correlation coefficients r (0.35–0.55 for road shoulders and 0.45–0.47 for sidewalks) indicating bias. The framework succeeded in creating realistic images and aligning pixel ratios between perspectives, achieving strong correlation coefficients (0.81 for road shoulders and 0.83 for sidewalks), thus reducing bias. This work contributes by providing a scalable and model-agnostic approach to produce accurate SVIs for urban planning and sustainability, setting a foundation for improving bikeability and walkability assessments and promoting active transportation.

Urban morphology is important in a broad range of investigations across the fields of city planning, transportation, climate, energy, and urban data science. Characterising buildings with a set of numerical metrics is fundamental to studying the urban form. Despite the rapid developments in 3D geoinformation science, and the growing 3D data availability, most studies simplify buildings to their 2D footprint, and when taking their height into account, they at most assume one height value per building, i.e. simple 3D. We take the first step in elevating building metrics into full/true 3D, uncovering the use of higher levels of detail, and taking into account the detailed shape of a building. We set the foundation of the new research line on 3D urban morphology by providing a comprehensive set of 3D metrics, implementing them in openly released software, generating an open dataset containing 2D and 3D metrics for 823,000 buildings in the Netherlands, and demonstrating a use case where clusters and architectural patterns are analysed through time. Our experiments suggest the added value of 3D metrics to complement existing counterparts, reducing ambiguity, and providing advanced insights. Furthermore, we provide a comparative analysis using different levels of detail of 3D building models.

3D city models are omnipresent in urban management and simulations. However, instruments for their evaluation have been limited. Furthermore, current instances are scattered worldwide and developed independently, hampering their comparison and understanding practices. While there are developed assessment frameworks in open data, such efforts are generic and not applied to geospatial data. We establish a holistic and comprehensive four-category framework ‘3D City Index’, encompassing 47 criteria to identify key properties of 3D city models, enabling their assessment and benchmarking, and suggesting usability. We evaluate 40 authoritative 3D city models and derive quantitative and qualitative insights. The framework implementation enables a comprehensive and structured understanding of the landscape of semantic 3D geospatial data, as well as doubles as an evaluated collection of open 3D city models. For example, datasets differ substantially in their characteristics, having heterogeneous properties influenced by their different purposes. There are further applications of this first endeavour to standardise the characterisation of 3D data: monitoring developments and trends in 3D city modelling, and enabling researchers and practitioners to find the most appropriate datasets for their needs. The work is designed to measure datasets continuously and can also be applied to other instances in spatial data infrastructures.

The accurate prediction of travel demand by bus is crucial for effective urban mobility demand management. However, most models of travel demand prediction by bus tend to focus on the bus’s spatiotemporal dependencies, while ignoring the interactions between buses and other transportation modes, such as metros and taxis. We propose a Multiview Spatiotemporal Graph Neural Network (MSTGNN) model to predict short-term travel demand by bus. It emphasizes the ability to capture the interaction dependencies among the travel demand of buses, metros, and taxis. Firstly, a multiview graph consisting of bus, metro, and taxi views is constructed, with each view containing both a local and global graph. Secondly, a multiview attention-based temporal graph convolution module is developed to capture spatiotemporal and cross-view interaction dependencies among different transport modes. Especially, to address the uneven spatial distributions of features in multiview learning, the cross-view spatial feature consistency loss is introduced as an auxiliary loss. Finally, we conduct intensive experiments using a real-world dataset from Shenzhen, China. The results demonstrate that our proposed MSTGNN model performs better than the existing models. Ablation experiments validate the contributions of various modes of transportation to the improvement of the model’s performance.

We present a new method to create spatial data using a generative adversarial network (GAN). Our contribution uses coarse and widely available geospatial data to create maps of less available features at the finer scale in the built environment, bypassing their traditional acquisition techniques (e.g. satellite imagery or land surveying). In the work, we employ land use data and road networks as input to generate building footprints and conduct experiments in 9 cities around the world. The method, which we implement in a tool we release openly, enables the translation of one geospatial dataset to another with high fidelity and morphological accuracy. It may be especially useful in locations missing detailed and high-resolution data and those that are mapped with uncertain or heterogeneous quality, such as much of OpenStreetMap. The quality of the results is influenced by the urban form and scale. In most cases, the experiments suggest promising performance as the method tends to truthfully indicate the locations, amount, and shape of buildings. The work has the potential to support several applications, such as energy, climate, and urban morphology studies in areas previously lacking required data or inpainting geospatial data in regions with incomplete data.

The First International Workshop on "Intelligence Cross-Data Analytics and Retrieval" (ICDAR'20) welcomes any theoretical and practical works on intelligence cross-data analytics and retrieval to bring the smart-sustainable society to human beings. We have witnessed the era of big data where almost any event that happens is recorded and stored either distributedly or centrally. The utmost requirement here is that data came from different sources, and various domains must be harmonically analyzed to get their insights immediately towards giving the ability to be retrieved thoroughly. These emerging requirements lead to the need for interdisciplinary and multidisciplinary contributions that address different aspects of the problem, such as data collection, storage, protection, processing, and transmission, as well as knowledge discovery, retrieval, and security and privacy. Hence, the goal of the workshop is to attract researchers and experts in the areas of multimedia information retrieval, machine learning, AI, data science, event-based processing and analysis, multimodal multimedia content analysis, lifelog data analysis, urban computing, environmental science, atmospheric science, and security and privacy to tackle the issues as mentioned earlier.

A set of 12,436 papers published in 20 GIScience journals in the period 2000–2014 were analysed to extract publication patterns and trends. This comprehensive scientometric study focuses on multiple aspects: output volume, citations, national output and efficiency (output adjusted with econometric indicators), collaboration, altmetrics (Altmetric score, Twitter mentions, and Mendeley bookmarking), authorship, and length. Examples of notable observations are that 5% countries account for 76% of global GIScience output; a paper published 15 years ago received a median of 12 citations; and the share of international collaborations in GIScience has more than tripled since 2000 (31% papers had authors from multiple countries in 2014, an increase from 10% in 2000).

While error propagation in GIS is a topic that has received a lot of attention, it has not been researched with 3D GIS data. We extend error propagation to 3D city models using a Monte Carlo simulation on a use case of annual solar irradiation estimation of building rooftops for assessing the efficiency of installing solar panels. Besides investigating the extension of the theory of error propagation in GIS from 2D to 3D, this paper presents the following contributions. We (1) introduce varying XY/Z accuracy levels of the geometry to reflect actual acquisition outcomes; (2) run experiments on multiple accuracy classes (121 in total); (3) implement an uncertainty engine for simulating acquisition positional errors to procedurally modelled (synthetic) buildings; (4) perform the uncertainty propagation analysis on multiple levels of detail (LODs); and (5) implement Solar3Dcity – a CityGML-compliant software for estimating the solar irradiation of roofs, which we use in our experiments. The results show that in the case of the city of Delft in the Netherlands, a 0.3/0.6 m positional uncertainty yields an error of 68 kWh/m2/year (10%) in solar irradiation estimation. Furthermore, the results indicate that the planar and vertical uncertainties have a different influence on the estimations, and that the results are comparable between LODs. In the experiments we use procedural models, implying that analyses are carried out in a controlled environment where results can be validated. Our uncertainty propagation method and the framework are applicable to other 3D GIS operations and/or use cases. We released Solar3Dcity as open-source software to support related research efforts in the future.

The international standard CityGML defines five levels of detail (LODs) for 3D city models, but only the highest of these (LOD4) supports modelling the indoor geometry of a building, which must be acquired in correspondingly high detail and therefore at a high cost. Whereas simple 3D city models of the exterior of buildings (e.g. CityGML LOD2) can be generated largely automatically, and are thus now widely available and have a great variety of applications, similarly simple models containing their indoor geometries are rare.In this paper we present two contributions: (i) the definition of a level of detail LOD2+, which extends the CityGML LOD2 specification with indoor building geometries of comparable complexity to their exterior geometries in LOD2; and more importantly (ii) a method for automatically generating such indoor geometries based on existing CityGML LOD2 exterior geometries. We validate our method by generating LOD2+ models for a subset of the Rotterdam 3D data set and visually comparing these models to their real counterparts in building blueprints and imagery from Google Street View and Bing Maps. Furthermore, we use the LOD2+ models to compute the net internal area of each dwelling and validate our results by comparing these values to the ones registered in official government data sets.

The knowledge of the transportation mode used by humans (e.g. bicycle, on foot, car and train) is critical for travel behaviour research, transport planning and traffic management. Nowadays, new technologies such as the Global Positioning System have replaced traditional survey methods (paper diaries, telephone) because they are more accurate and problems such as under reporting are avoided. However, although the movement data collected (timestamped positions in digital form) have generally high accuracy, they do not contain the transportation mode. We present in this article a new method for segmenting movement data into single-mode segments and for classifying them according to the transportation mode used. Our fully automatic method differs from previous attempts for five reasons: (1) it relies on fuzzy concepts found in expert systems, that is membership functions and certainty factors; (2) it uses OpenStreetMap data to help the segmentation and classification process; (3) we can distinguish between 10 transportation modes (including between tram, bus and car) and propose a hierarchy; (4) it handles data with signal shortages and noise, and other real-life situations; (5) in our implementation, there is a separation between the reasoning and the knowledge, so that users can easily modify the parameters used and add new transportation modes. We have implemented the method and tested it with a 17-million point data set collected in the Netherlands and elsewhere in Europe. The accuracy of the classification with the developed prototype, determined with the comparison of the classified results with the reference data derived from manual classification, is 91.6%.