Changfeng Jing

Understanding urban functional regions (UFRs) is essential for spatial planning and governance. Existing approaches typically rely on static points of interest (POI) or mobility data to delineate functional zones, but they often fail to capture the dynamic coupling between physical space and human activities and overlook population attributes, which are a fundamental parameter underpinning both spatial units in urban planning and human activities. To address this limitation, we propose a Dual‐Information Entropy Model (DIEM) that explicitly synthesizes the structural signals of POI distribution and the dynamic usage intensity of Wi‐Fi mobility records. By further incorporating population portrait data, the proposed framework moves beyond static functional classification toward comprehensive, human‐centric profiling of urban regions. Applied to Beijing's Fourth Ring Road, DIEM significantly outperforms baseline zoning strategies, achieving an Overall Accuracy of 0.896 and a Kappa coefficient of 0.885. More importantly, the results reveal two key patterns: UFRs display temporal elasticity, shifting from concentrated production‐oriented structures on workdays to fragmented consumption and life‐oriented patterns on holidays; and population dynamics exhibit systematic restructuring, with clear variations in age, gender, and educational attainment across functional regions. These findings highlight the value of incorporating demographic dimensions into urban functional analysis and provide new insights for adaptive, human‐centered urban planning.

The spatial attention mechanism has been widely employed in the semantic segmentation of remote sensing images due to its exceptional capacity for modeling long-range dependencies. However, the analysis performance of remote sensing images can be reduced owing to their large intra-class variance and complex spatial structures. The vanilla spatial attention mechanism relies on the dense affine operations and a fixed scanning mechanism, which often introduces a large amount of redundant contextual semantic information and lacks consideration of cross-directional semantic connections. This paper proposes a new Cross-scan Semantic Cluster Network (CSCN) with integrated Semantic Filtering Contextual Cluster (SFCC) and Cross-scan Scene Coupling Attention (CSCA) modules to address these limitations. Specifically, the SFCC is designed to filter redundant information; feature tokens are clustered into semantically related regions, effectively identifying local features and reducing the impact of intra-class variance. CSCA effectively addresses the challenges of complex spatial geographic backgrounds by decomposing scene information into object distributions and global representations, using scene coupling and cross-scanning mechanisms and computing attention from different directions. Combining SFCC and CSCA, CSCN not only effectively segments various geographic spatial objects in complex scenes but also has low model complexity. The experimental results on three benchmark datasets demonstrate the outstanding performance of the attention model generated using this approach.

Discovering spatially associated objects involves measuring objects’ similarities and retrieving associated objects. The integration of spatial topology and network models for discovering associated objects remains largely unexplored. Here, the concept of a maximum topological accessibility path was developed to quantify objects’ similarity attenuation. Considering the topological accessibility and spatial feature similarity of network nodes, an approach named the Weighted Similarity measure method considering Topological Accessibility (WSTA) is proposed to measure object association. The WSTA can capture both spatial interaction patterns and topological relationships in complex urban environments, thereby improving the accuracy of spatially associated object discovery. The proposed approach is validated using real-world point-of-interest (POI) datasets from Beijing city. The results suggest that integrating topological relationship approaches yields significant accuracy improvements in existing baseline methods, thereby enriching geospatial data retrieval in the era of big geospatial data.

Long-tailed distribution is a common issue in remote sensing image classification (RSIC), and many datasets suffer from severe class imbalance. This imbalance often causes the classifier to focus on the head classes with more samples, neglecting the tail classes. As a result, the precision of the tail classes is reduced, which in turn affects the generalization ability of the classifier. To address this problem, a hybrid network based on Cross-Space Learning and Perception-Driven Mechanism (CLPM) is proposed to improve the classification accuracy of samples from the tail classes. The CLPM network consists of three components. The Cross-Space Representation Learning Branch (CS-RLB) is designed to enhance the representation capability of tail-class samples by integrating multi-scale and multi-region spatial features. In parallel, the Adaptive Perception Classification Branch (AP-CB) dynamically adjusts the receptive fields to improve generalization across different resolutions and challenging scenarios. Additionally, the CLPM innovatively applies the von Mises-Fisher (vMF) distribution to remote sensing images for high-dimensional inter-class feature modeling. Building on this, a vMF-based Contrastive Loss (vMF-CL) function is proposed. This approach effectively coordinates the learning processes of head and tail classes while enhancing the precision of feature representation. The effectiveness of CLPM is validated on datasets with varying balance ratios, including SIRI-WHU, CLRS, and NWPU-RESISC45. Results show that CLPM significantly improves tail classes accuracy while maintaining high recognition rates for head and middle classes. Compared to existing methods, CLPM has significant advantages in overall recognition accuracy, the long-tailed problem, and diversity adaptation.

The promotion and expansion of Geographic Information System (GIS) applications have increased the complexity of geocomputation. The concept of a geographic scene, which encompasses both geographic entities and complex spatial interaction relationships, serves as a means of organizing and expressing geographic information. Existing methods of organizing and computing geographic scenes often focus on single geometric elements or single-category objects, neglecting the complexity of the geocomputation process and leading to biased results. To address this issue, this study designs and implements a geographic scenario computation system based on WebGIS to overcome the lack of integration of knowledge elements in traditional geographic scenario computation methods. The system consists of a data management module, a geographic scenario visualization module, and a geographic scenario computation module. It not only realizes the visualization of geographic scenarios but also introduces a knowledge base into the geographic scenario computation process, enabling accurate management of geographic scenarios. This paper uses a farmland scenario in Shandong Province, China, as a case study to verify the effectiveness and feasibility of the system. The application of this system provides new technical support for the computation process of geographic scenarios, promoting the refinement and intelligent development of geographic scenario management. This method can also be applied to similar applications of intelligent sensors and the Internet of Things in the organization and management of sensing information in the future.