Geographic knowledge graph (GeoKG) alignment is important for the integration and knowledge discovery of multisource geographic information and the generation of large-scale and high-quality knowledge graphs (KGs). However, the existing models/technologies face many challenges when dealing with large-scale multisource complex GeoKG alignment tasks, including the inconsistency of attribute and relationship values caused by domain differences, the inability to perceive relationships and entities, and missing geographic domain training data. To address these issues, we propose a GeoKG alignment model based on depth relationships and neighborhood awareness (named DRNA-GCNE). The DRNA-GCNE model adopts a graph neural network as the infrastructure and uses the graph attention technique to evaluate and weight the entity’s relationship attributes dynamically, thus enhancing the ability to perceive structural and semantic information in the GeoKG; concurrently, the relationship information and the multihop neighbor characteristics of the entity are effectively integrated, and the representation of the entity is further enriched. Finally, the training technique of normalized loss mining for multiple negative samples is shown. This approach increases the model’s capacity for generalization. The DRNA-GCNE model, as evaluated on two public datasets and our GeoEA2024 Chinese dataset, significantly outperforms current GeoKG entity alignment methods across key metrics.

Effective building pattern recognition, a complex task that requires the simultaneous consideration of individual building features and spatial relations, is essential for successfully generalizing maps. However, existing deep learning approaches must still be adequately comprehensive in jointly quantifying the individual features and spatial relationships of buildings, suggesting further improvement in the quantitative representation of building spaces. This study presents a novel edge-attention multi-head graph convolutional network (GCN) that concurrently considers the quantitative modeling and representation of individual features and spatial relations, enhancing building pattern recognition. The proposed method captures individual building features and spatial relations, including proximity and arrangement similarity, by using spatial relationship descriptors and attention mechanisms to generate spatial relevance coefficients. These coefficients are then integrated into a weighted multi-head GCN to participate in the quantitative expression of individual features, facilitating the quantitative analysis and modeling of building features, and thus, improving recognition performance. Our experimental analysis confirms the method’s superior capability in recognizing complex spatial features. The method also demonstrates strong generalization across different scales and areas, underscoring its efficacy and potential for enhancing geospatial analyses.

Automatic matching of multisource data is an important technique for achieving change detection, fusion and updating spatial data. However, most current learning methods for building footprint matching require a large number of samples, and labeling these samples is costly in terms of labor and time. Moreover, multisource building footprint data are complex and diverse leading to recognizing the different matching relationships is a hard task. Thus, this study proposes a learning-based method for recognizing multisource building footprints matching relationships by using a one-class support vector machine (OCSVM). The OCSVM was trained using only positive samples. First, a set of geometric indicators was designed to train a model and realize initial matching recognition. Then, a contextual metric was calculated based on the rough matching results, and geometric and contextual metrics were combined to train the model and realize relaxed matching recognition. Relaxed matching is an optimization process implemented after initial matching to recognize more relaxed matching relationships. In relaxed matching, a convex hull is used to recognize matching relationships besides 1:1, such as 1:n, m:1 and m:n. The experimental results showed that the proposed method outperformed indicator-weighted (weighted average) and learning-based matching methods, such as traditional SVMs and decision trees (DTs). The precision scores of the proposed model were 97.1%, 95% and 97.2% for the Wuhan (China), Beijing (China) and Richmond Hill (Canada) datasets, respectively. Furthermore, the proposed model identified the matching relationships of buildings with complex geometric features and high-density spatial distributions.

Semantic segmentation of mobile laser scanning (MLS) point clouds can provide meaningful 3 D semantic information of urban facilities for various applications. However, it still remains a challenge to extract accurate 3 D semantic information from MLS point cloud data due to its irregular 3 D geometric structure in a large-scale outdoor scene. To this end, this study develops a geometry-aware attention point network (GAANet) with geometric properties of the point cloud as a reference. Specifically, the proposed method first builds a graph-like region for each input point to establish the geometric correlation toward its neighbors for robustly descripting local geometry-aware features. Thereafter, the method introduces a novel multi-head attention mechanism to efficiently learn local discriminative features on the constructed graphs and a feature combination operation to capture both local and global geometric dependencies inside fused point features for significantly facilitating the segmentation of small or incomplete 3 D objects at point-level. Finally, an adaptive loss function is appended to handle class imbalance for the overall performance improvement. The validation experiments on two challenging benchmarks demonstrate the effectiveness and powerful generation ability of the proposed method, which achieves state-of-the-art performance with mean IoU of 65.09% and 95.20% in the Toronto-3D and Oakland 3-D MLS dataset, respectively.

Multilane roads are a set of approximately parallel line segments representing the same road in large-scale vector maps. They must be extracted first in cartographic generalization. There are numerous multilane roads in the easily accessible OpenStreetMap (OSM) dataset. For this dataset, polygon-based methods have achieved state-of-the-art performance. However, traditional polygon-based methods usually rely on manually labeled data, which means they are time-consuming and labor-intensive. To address this problem, an unsupervised framework for extracting multilane roads is proposed in this study. Road segments were first grouped to form the road polygons. A set of shape descriptors was formulated to reduce the dimensions of individual road polygons into conceptual points. Next, dimensional shape descriptors were standardized using logarithmic standardization. The density peaks clustering (DPC) algorithm was employed to classify these points. Then, cluster tags were identified manually to recognize which clusters represent multilane polygons. Finally, post-processing learning from the concept of assimilation is proposed to fill holes and remove islands. Experiments were conducted to extract multilane roads with datasets from three cities: Wuhan, Beijing and Munich. The experimental results show that the proposed framework effectively extracted multilane roads without any labels with accuracy levels comparable to those of supervised methods.

Urban scenes consist of visual and semantic features and exhibit spatial relationships among land-use types (e.g. industrial areas are far away from the residential zones). This study applied a graph convolutional network with neighborhood information (henceforth, named the neighbour supporting graph convolutional neural network), to learn spatial relationships for urban scene classification. Furthermore, a co-occurrence analysis with visual and semantic features proceeded to improve the accuracy of urban scene classification. We tested the proposed method with the fifth ring road of Beijing with an overall classification accuracy of 0.827 and a Kappa coefficient of 0.769. In comparison with other methods, such as support vector machine, random forest, and general graph convolutional network, the case study showed that the proposed method improved about 10% in urban scene classification.

Polygon similarity can play an important role in geographic information retrieval, map matching and updating, and spatial data mining applications. Geographic information science (GIS) represents various spatial objects as polygons, including simple polygons and polygons with holes, as well as multipolygons. Spatial objects of multipolygons possess complex structure which makes it difficult to assess their similarity. This study develops a method based on convex hulls and position graphs to measure the similarity between multipolygons. The proposed method first finds correspondences between subpolygons in the two multipolygons based on a control polygon. Thereafter, the method constructs a position graph to denote the distribution of these subpolygons and applies a turning function to compute the similarity between various graphs. Fourier transformation and moment invariants were combined to characterize the different matching relationships among subpolygons. The experiments involve three different kinds multipolygons to verify the effectiveness and robustness of proposed method. The experiments show that this approach effectively measures similarity between multipolygons. Moreover, the proposed method accounts for the relationships across the entire complex geometrical shape and components of multipolygon during measuring similarity.

Spatiotemporal association pattern mining can discover interesting interdependent relationships among various types of geospatial data. However, existing mining methods for spatiotemporal association patterns usually model geographic phenomena as simple spatiotemporal point events. Therefore, they cannot be applied to complex geographic phenomena, which continuously change their properties, shapes or locations, such as storms and air pollution. The most salient feature of such complex geographic phenomena is the geographic dynamic. To fully reveal dynamic characteristics of complex geographic phenomena and discover their associated factors, this research proposes a novel complex event-based spatiotemporal association pattern mining framework. First, a complex geographic event was hierarchically modeled and represented by a new data structure named directed spatiotemporal routes. Then, sequence mining technique was applied to discover the spatiotemporal spread pattern of the complex geographic events. An adaptive spatiotemporal episode pattern mining algorithm was proposed to discover the candidate driving factors for the occurrence of complex geographic events. Finally, the proposed approach was evaluated by analyzing the air pollution in the region of Beijing-Tianjin-Hebei. The experimental results showed that the proposed approach can well address the geographic dynamic of complex geographic phenomena, such as the spatial spreading pattern and spatiotemporal interaction with candidate driving factors.

Pedestrian networks play an important role in various applications, such as pedestrian navigation services and mobility modeling. This paper presents a novel method to extract pedestrian networks from crowdsourced tracking data based on a two-layer framework. This framework includes a walking pattern classification layer and a pedestrian network generation layer. In the first layer, we propose a multi-scale fractal dimension (MFD) algorithm in order to recognize the two different types of walking patterns: walking with a clear destination (WCD) or walking without a clear destination (WOCD). In the second layer, we generate the pedestrian network by combining the pedestrian regions and pedestrian paths. The pedestrian regions are extracted based on a modified connected component analysis (CCA) algorithm from the WOCD traces. We generate the pedestrian paths using a kernel density estimation (KDE)-based point clustering algorithm from the WCD traces. The pedestrian network generation results using two actual crowdsourced datasets show that the proposed method has good performance in both geometrical correctness and topological correctness.

Volunteered geographic information (VGI), OpenStreetMap (OSM), has been used in many applications, especially when official spatial data are unavailable or outdated. However, the quality of VGI remains a valid concern. In this paper, we use the matched results between OSM building footprints and official data as the samples for training an autoencoder network, which encodes and reconstructs the sample populations according to unknown complex multivariate probability distributions. Then, the OSM data are assessed based on the theory that small probability samples contribute little to the autoencoder network and that they can be recognized by the higher reconstructed errors during training. In the method described here, the selected measures, including data completeness, positional accuracy, shape accuracy, semantic accuracy and orientation consistency between OSM and official data, are used as the inputs for a deep autoencoder network. Finally, building footprint data from Toronto, Canada, are evaluated, and experiments show that the proposed method can assess the OSM data comprehensively, objectively and accurately.

In geographic information retrieval and spatial data mining, similarity is used to resolve shape matching and clustering. Many approaches have been developed to calculate similarity between simple geometric shapes. However, complex spatial objects are common in spatial database systems, spatial query languages and Geographic Information Science (GIS) applications. With holed polygons, many similarity measurement approaches are restricted to address the relationships between holes or between the holes and the entire complex geometric shape. A successful method should remove the restrictions due to these complex relations and retain invariant during geometric translation (rotation, moving and scaling). To overcome these deficiencies, we utilize position graphs to describe the distribution of holes in complex geometric shapes by storing invariants, such as angles and distances. In addition, Fourier descriptors and the position graph-based method are used to measure the similarity between holed polygons. Experiments show that the proposed method takes into account the relationships in an entire complex geometric shape. It can effectively calculate the similarity of holed polygons, even if they contain different numbers of holes.

With the wide adoption of big spatial data and the emergence of CyberGIS, the nontrivial computational intensity introduced by massive amount of data poses great challenges to the performance of vector map visualization. The parallel computing technologies provide promising solutions to such problems. Evenly decomposing the visualization task into multiple subtasks is one of the key issues in parallel visualization of vector data. This study focuses on the decomposition of polyline and polygon data for parallel visualization. Two key factors impacting the computational intensity were identified: the number of features and the number of vertices of each feature. The computational intensity transform functions (CITFs) were constructed based on the linear relationships between the factors and the computing time. The computational intensity grid (CIG) can then be constructed using the CITFs to represent the spatial distribution of computational intensity. A noninterlaced continuous space-filling curve is used to group the lattices of CIG into multiple sub-domains such that each sub-domain entails the same amount of computational intensity as others. The experiments demonstrated that the approach proposed in this paper was able to effectively estimate and spatially represent the computational intensity of visualizing polylines and polygons. Compared with the regular domain decomposition methods, the new approach generated much more balanced decomposition of computational intensity for parallel visualization and achieved near-linear speedups, especially when the data is greatly heterogeneously distributed in space.