In this study, a novel self-supervised contrastive learning framework that integrates geographic knowledge into the analysis of street view imagery for representing urban space is proposed. Traditional methods that rely solely on deep learning often struggle to capture the complex spatial characteristics of urban environments. To address this issue, in the proposed framework, we first extracted visual knowledge (VK) from street view imagery using semantic segmentation and then constructed contrastive samples through VK–imagery pairs. Finally, we introduced distance-weighted temperatures into the contrastive loss function to adjust the similarities of urban features encoded in the representations on the basis of geographical proximity, thereby integrating semantic knowledge among geographical locations (GSK). The method was validated through two case studies: urban village classification in Guangzhou and Foshan and urban-mobility pattern prediction in Shenzhen. The results showed significant improvements in classification accuracy (OA: 0.967) and reduced prediction error (MAE: 28.069) compared with conventional approaches. This research demonstrates the effectiveness of integrating geographic knowledge (including VK and GSK) into contrastive learning. The approach enhances model interpretability and generalizability for urban studies and provides a tool for analyzing urban development patterns and mobility needs that will be useful for urban planning and policymaking.

Land use and land cover change (LUCC) simulation aids the interpretation of the causes and consequences of future landscape dynamics under various scenarios, which in turn supports policy decisions. The essence of LUCC simulation lies in representing complex spatiotemporal associations among land types, including competitions and interactions. Currently, analyses of complex spatiotemporal LUCC associations mainly focus on the spatial configuration of land use while ignoring the intricate spatiotemporal co-evolution patterns of land types. Therefore, by integrating spatiotemporal co-evolution pattern mining (STC) in a future land use simulation (FLUS) model, a land use change simulation model named STC-FLUS was developed in this study. The proposed model is innovative because it can accurately quantify the spatiotemporal co-evolution patterns of land types, which can be effectively incorporated into LUCC simulations. A set of simulations indicate that the STC-FLUS model is more accurate than the classical FLUS model, with a figure of merit score of 0.135 compared with 0.114. Simulation results under five localized shared socioeconomic pathway scenarios from 2020 to 2040 demonstrate that the proposed model is effective for future LUCC simulation under a set of development scenarios. We conclude that spatiotemporal co-evolution patterns of land types can enhance the reliability of land use projections. Moreover, the STC-FLUS model can serve as a useful tool to understand future land use dynamics.

While most state-of-the-art building extraction methods can generate precise binary segmentation masks, geographic and cartographic applications typically require vectorized footprints of the extracted building instead of the rasterized output. Current vectorized footprint extraction methods for multiple buildings have yet to address the confusion brought about by the broken line formed at the slope connection of the building roof. In addition, these methods have some shape errors, such as incomplete and irregular boundaries. Given the above issues, this study proposed a new building outline extraction method, SANET, which uses a transformer block to capture refined boundaries and footprint features. Moreover, a shape-aware loss function was designed to constrain the building shapes and optimize boundary feature generation. Incomplete and irregular boundaries and intersecting outlines aggravate the shape errors for vectorized building objects. Thus, this study computed a Fourier descriptor for a footprint generation model to provide prior shape knowledge for the shape constraint module. Experiments were conducted on the WHU and SpaceNet datasets, and the proposed method could achieve state-of-the-art performance with a higher average precision (AP) and recall compared with other contour-based methods. The proposed shape constraint methods obtained complete and shape-correct building boundaries. Moreover, the incomplete outlines and smooth corners were remarkably improved.

Investigating the relationship between built environment factors and roadway safety is crucial for preventing road traffic accidents. Although studies have analyzed traffic-related built environment factors based on pre-determined zonal units, conclusive evidence regarding the relationship between streetscape features and traffic accidents at a fine-grained road segment level is still lacking. With the widespread availability of large-scale street view images, automatically analyzing urban built environments on a large scale is possible. Therefore, the aim of this study was to investigate the relationship between streetscape features and traffic accidents at a fine-grained road segment level using street view images. Specifically, we employed semantic image segmentation to extract streetscape elements from urban street view images, and then created traffic crash-related variables, including the street-level built environment variables, traffic variables, land-use indices, and proximity characteristics, at the road-segment level. Finally, we adopted a classification-then-regression strategy to model the number of traffic crashes while considering the zero-inflated and spatial heterogeneity issues. Our findings suggest that streetscape features can effectively reflect built-environment characteristics at the road-segment level. Moreover, a comparison of our proposed modeling method with existing models demonstrates its superior performance. The results provide insight into the development of effective planning strategies to improve traffic safety.

The building is a critical urban element for developing smart cities. Building extraction research has experienced rapid development due to abundant remote sensing image data availability. Building extraction methods have recently focused on enhancing regional accuracy without obtaining refined building boundaries, and do not fit the concave-convex shape of the building and have significant sawtooth noise. This paper proposes a building shape-preserving framework to solve the imperfect boundary problem. To delineate the concave and convex building boundaries from remote sensing images, we combine coarse and fine-grained information with the instance segmentation method, Mask region-based convolutional neural network (R-CNN), which can further refine the indeterminate boundary points, and perform inward fitting of the building boundary. A regular boundary network is designed to learn the features of the edges and footprints to calculate the boundary loss that can smoothen the boundary noise, considering the imperfect boundaries and sawtooth noise in the extracted building boundary. Furthermore, to enhance the ability to evaluate the extracted building shape, a building footprint information evaluation algorithm, the footprint distance metric, is proposed to calculate the footprint distance between each building boundary. Comparing the building extraction results indicate that the proposed method achieves excellent quantitative evaluation scores. Experiments performed using Spacenet and WHU datasets to verify the effectiveness of the proposed method are detailed. The experimental results indicate that the algorithm utilizes the powerful edge extraction capability of the method to exhibit excellent performance in terms of extracting building edges from complex remote sensing scenes. Code is available at https://github.com/AnnaCUG/Boundary-shape-preserving-model.

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.

The ever-increasing of the large geospatial datasets and the widely application of the complex geocomputation make the parallel processing of GIS an important component of high-performance computing. The paper introduces a two-phase load-balancing scheme for the parallel GIS operations in distributed environment. The paper focus on the parallel framework design and parallel strategy implement of the spatial operations in GIS. Two major aspects of the spatial data partitioning and dynamic load schedule are discussed in detail, declustering the massive data sets into two parts hierarchically: the dynamic share data and the static local data. In the experimental test, we build up the analytical cost model and evaluate the utilize rate of computational power and I/O resource, and analyze the efficiency of the proposed parallel prototype.