Understanding and explaining complex geographic phenomena—ranging from climate change to socioeconomic disparities—is a central focus in both geography and the broader scientific community. Various methods have been developed to elucidate relationships between variables, from coefficient estimates in linear regression models to the increasingly dominant use of feature attribution scores in Explainable AI (XAI) techniques. However, explanations generated by XAI methods often carry uncertainty, stemming from the model itself and the data used to train the model. Despite the critical importance of accounting for such uncertainty, this issue remains largely overlooked in the geospatial domain. In this study, we developed an uncertainty quantification framework for XAI explanations based on conformal prediction, termed Geospatial eXplanation Conformal Prediction (GeoXCP). By incorporating spatial dependence into the modeling process, GeoXCP produced spatially adaptive explanations with calibrated uncertainty estimates. We validated the effectiveness of GeoXCP through extensive simulation experiments and real-world datasets. The results demonstrated that GeoXCP provided reliable explanations while effectively quantifying uncertainty across diverse geospatial scenarios. Our approach represented a significant advancement in explainable geospatial machine learning, enabling decision-makers to better assess the trustworthiness of model-driven insights. The proposed framework was implemented in a python package, named GeoXCP.

Understanding the relationships between geographical variables is a fundamental task in spatial analysis. However, existing spatial methods often underperform in scenarios involving nonlinear relationships and complex interactions among geographical variables. Identifying the relationships between individual variables (i.e. univariate effect) within multiple interacting variables remains challenging and long-lasting. In this study, we propose a novel model—Geographical Pattern Interaction (GPI)—based on the premise that the spatial pattern of a response variable emerges from the interaction of spatial patterns in explanatory variables. GPI leverages decision trees and Shapley value explanations to quantify both global and local univariate effects by measuring the alignment between the spatial distribution of the target variable and those of the predictors. Through simulation experiments, we demonstrate GPI’s superior performance compared to traditional regression-based spatial explanation methods. Notably, the GPI framework is stable across varying spatial scales and sample sizes, making it particularly suitable for spatial explanation tasks under small data and multi-scale conditions. A case study on homelessness risk in Australia demonstrates GPI’s ability to reveal nonlinear spatial associations and interaction effects. By capturing overlooked pattern similarities and interactions, GPI offers an interpretable and transferable tool for analyzing complex spatial relationships.

OpenStreetMap (OSM) has transitioned from a pioneering volunteered geographic information project into a global, multi-disciplinary research nexus. This study presents a bibliometric and systematic analysis of the OSM research landscape, examining its development trajectory and key driving forces. By evaluating 1926 publications from the Web of Science (WoS) Core Collection and 782 State of the Map (SotM) presentations up to June 2024, we quantify publication growth, collaboration patterns, and thematic evolution. Results demonstrate simultaneous consolidation and diversification within the field. While a stable core of contributors continues to anchor OSM research, themes have shifted from initial concerns over data production and quality toward advanced analytical and applied uses. Comparative analysis of OSM-related research in WoS and SotM reveals distinct but complementary agendas between scholars and the OSM community. Building on these findings, we identify six emerging research directions and discuss how evolving partnerships among academia, the OSM community, and industry are poised to shape the future of OSM research. This study establishes a structured reference for understanding the state of OSM studies and offers strategic pathways for navigating its future trajectory.

Understanding the intricacies of fine-grained population distribution, including both predictability and uncertainty, is crucial for urban planning, social equity, and environmental sustainability. The spatial processes associated with the distribution of populations are complex, and enhancing their predictability involves revealing nonlinear interactions among various explanatory variables. Additionally, population distribution is influenced by various factors that are often challenging to quantify, thereby introducing uncertainty into predictive models. Although the development of explainable artificial intelligence (XAI) helps identify underlying factors, the complex geographical processes and the special nature of spatial data present challenges for purely statistical-based explanation methods, leading to incomplete or incorrect explanations. To address these challenges, we introduce GeoVisX, a geospatial visual analytics framework integrated with XAI. GeoVisX integrates XAI with visual analytics to dissect the spatial processes. Through a case study of Munich, GeoVisX demonstrates its utility in analyzing spatial distribution and identifying key factors impacting population distribution at the 100 m grid level. Our findings highlight the GeoVisX’s capability to enhance understanding of geographical phenomena, contributing to more informed urban policy and planning strategies. This study not only validates the effectiveness of GeoVisX but also emphasizes the importance of incorporating visual analytics and explainable methodologies for addressing complex geographical issues.

Excessive exposure to the sun and the resulting heat poses health risks to pedestrians in hot weather. To mitigate these risks, we propose a shadow-oriented navigation system that offers cooler and more convenient walking routes by simulating shadows. Our system integrates a manually corrected pedestrian network from OpenStreetMap with LoD2 3D city models, using a ray tracing module for real-time shadow simulation. It optimizes routes to be either cooler or shorter based on user preferences, with easy verification through 3D scene visualization. Our navigation system has been implemented in a study area in Munich, Germany, with further discussions on the technical feasibility and challenges of extending it to larger areas.

This research introduces a novel Road Network Embeddings Matching (RNEM) method for road network matching in map conflation tasks, addressing key challenges in integrating diverse map datasets. Traditional methods like Delimited Stroke Oriented (DSO) and Hootenanny face difficulties with disparities in geometric, semantic, and topological information. RNEM leverages embeddings derived from these features, significantly improving accuracy, precision, recall, and F1 score. Using pre-trained models like Bidirectional Encoder Representations from Transformers (BERT), RNEM captures semantic and topological information, while geometric embeddings are generated through resampling and normalization of polylines. Experiments on Munich datasets show that RNEM outperforms existing methods by 3.2% in accuracy. This method represents the irst approach to incorporate semantic and topological information using NLP techniques, offering a comprehensive solution for map conflation, benefiting initiatives such as the Overture Maps Foundation.

Spatial heterogeneity refers to uneven distributions of geographical variables. Spatial interpolation methods that utilize spatial heterogeneity are sensitive to the way in which spatial heterogeneity is characterized. This study developed a Generalized Heterogeneity Model (GHM) for characterizing local and stratified heterogeneity within variables and to improve interpolation accuracy. GHM first divides a study area into multiple spatial strata according to the sample values and locations of a variable. Then, GHM estimates simultaneously the spatial variations of the variable within and between the spatial strata. Finally, GHM interpolates unbiased estimates and uncertainty at unsampled locations. We demonstrated the GHM by predicting the spatial distributions of marine chlorophyll in Townsville, Queensland, Australia. Results show that GHM improved both the overall interpolation accuracy across the study area and along strata boundaries compared with previous interpolation models. GHM also avoided bull’s eye patterns and abrupt changes along strata boundaries. In future studies, GHM has the potential to be integrated with machine learning and advanced algorithms to improve spatial prediction accuracy for studies in broader fields.

CityGML (City Geography Markup Language), the OGC (Open Open Geospatial Consortium) standard on three-dimensional (3D) city modeling, is widely used in an increasing number of applications, because it models a city with rich geometrical and semantic information. The underlying building model differentiates four consecutive levels of detail (LoDs). Nowadays, most city buildings are reconstructed in LoD3, while few landmarks in LoD4. For visualization or other purposes, buildings in LoD2 or LoD1 need to be derived from LoD3 models. But CityGML does not indicate methods for the automatic derivation of the different LoDs. This article presents an approach for deriving LoD2 buildings from LoD3 models which are essentially the exterior shells of buildings without opening objects. This approach treats different semantic components of a building separately with the aim to preserve the characteristics of ground plan, roof, and wall structures as far as possible. The process is composed of three steps: simplifying wall elements, generalizing roof structures, and then reconstructing the 3D building by intersecting the wall and roof polygons. The first step simplifies ground plan with wall elements projected onto the ground. A new algorithm is developed to handle not only simple structures like parallel and rectangle shapes but also complicated structures such as non-parallel, non-rectangular shapes and long narrow angles. The algorithm for generalizing roof structure is based on the same principles; however, the calculation has to be conducted in 3D space. Moreover, the simplified polygons of roof structure are further merged and typified depending on the spatial relations between two neighboring polygons. In the third step, generalized 3D buildings are reconstructed by increasing walls in height and intersecting with roof structures. The approach has been implemented and tested on a number of 3D buildings. The experiments have verified that the 3D building can be efficiently generalized, while the characteristics of wall and roof structure can be well preserved after the simplification.

This paper presents an automatic approach to rectify misalignments between a geo-referenced Very High Resolution (VHR) optical image (raster) and a road database (vector). Due to inconsistent representations of road objects in different data sources, the extraction and validation of the homologous road features are complicated. The proposed Sparse Matching (SM) approach is able to smoothly snap the road features from the vector database to their corresponding road features in the VHR image.

This paper presents an adapted algorithm to derive road network from land parcels based on skeleton operator. The spaces among the neighboring parcels are assumed to be occupied by roads. These potential roads are decomposed and approximated to generate road centerlines. The proposed algorithm minimizes unwanted artifacts by computing the negative minima curvature of the boundary. The algorithm includes three parts: 1) shape decomposition; 2) skeleton approximation; and 3) topology reconstruction. A pruning procedure is followed by the decomposition results, which can yield shared edges for neighboring sub-regions, so the direction of the centerline has been smoothed when it passes the shared edges. The straight skeleton (SS) algorithm can generate straight line, and the result is most reasonable for road network. Our proposed algorithm keeps the time complexity of straight skeleton algorithm, however, it partitions the target region into subregions where the skeletons have been computed in individual subregion instead of the whole region, and crossing patterns have been found and utilized to prune the results, hence it is not only much faster in practical computation, but also it is more suited to human perceptions. It can generate centerlines with correct topology in our cadastral test datasets from part of Barcelona. 97% of the decomposed region can get reasonable centerlines compared to a reference dataset, whereas 2% reveals incorrect reconstruction, and only 1% keep the original results from straight skeleton algorithm.

With the growing demand on multi-purpose or multi-modal navigation, the route calculation becomes more and more complex. The currently operational route planning algorithms reveal rather limited performances due to unavailable or insufficient interoperation among the underlying datasets that are separately maintained in different spatial databases. This paper introduces an operational approach to integrate routing-relevant information from different data sources. It involves three processes: (a) automatic matching to identify the corresponding road objects between different datasets; (b) interaction to refine the result of automatic matching; and (c) transferring the routing-relevant information from one dataset to another. In process (a), our Delimited Stroke Oriented (DSO) algorithm is employed to achieve the automatic data matching. It has revealed a high matching rate and certainty. However uncertain matching problems occur in areas where topological conditions are too complicated or inconsistent. The remaining unmatched or wrongly matched objects are treated in process (b) with the help of a series of our interaction tools plugged in Arc GIS 9.0. On the basis of the refined matching results, process (c) is dedicated to automatic integration of the routing-relevant information from different data sources. In large test areas from a number of federal states in Germany, the proposed automatic approach has been successfully applied to transfer the routing-relevant information from the dataset of Tele Atlas to DLM De. The enriched DLM De thus gains added value for route calculations.