Soil salinization is a significant issue that not only leads to ecological problems like land desertification but also poses a threat to food security by reducing the quantity and quality of arable land. Therefore, it is crucial to rapidly and accurately obtain information about soil salinization for timely and effective soil management. In recent years, the development of microwave remote sensing has provided new methods for large-scale and rapid monitoring of soil salinization, with Synthetic Aperture Radar (SAR) data-based soil salinization monitoring becoming a hotspot in remote sensing research. Recent advancements in SAR remote sensing for soil salinization monitoring can be summarized in four main aspects: (1) Microwave scattering mechanism of saline soil: Research has clarified the correlation between soil salinity and radar backscattering coefficients, providing a basis for estimating soil salinity using SAR data. Understanding the microwave scattering mechanism of saline soil is essential for accurately interpreting SAR data and extracting meaningful information about soil salinization. (2) Construction and extraction of characteristic parameters of saline soil: The construction and extraction of characteristic parameters of saline soil have shown a trend towards diversification and integration. Various parameters, such as backscattering coefficients, polarization ratios, and texture features, are used to characterize the properties of saline soil. By utilizing a combination of these parameters, researchers can obtain a more comprehensive understanding of soil salinization. (3) Classification of saline soil: The classification methods for saline soil have shifted from traditional machine learning to deep learning methods. Deep learning algorithms, such as convolutional neural networks, have shown promising results in accurately classifying saline soil areas. These advanced techniques enable the identification and mapping of different levels of soil salinity, aiding in effective soil management strategies. (4) Inversion of soil salinity: The inversion of soil salinity has transitioned from regression analysis to inversion methods that combine scattering mechanisms. By considering the microwave scattering mechanisms and using multiple data sources, more accurate estimations of soil salinity can be obtained. This approach allows for a better understanding of the spatial distribution and variability of soil salinity, facilitating targeted interventions and management practices. Despite these advancements, there are still challenges and issues in the current research on soil salinization monitoring using SAR remote sensing. Some of these challenges include the influence of multiple factors on the relationship between soil salinity and backscattering coefficients, the need for further analysis of mechanisms, the construction of characteristic parameters, and the fusion of multi-source data for achieving high-precision soil salinization monitoring using SAR remote sensing.

[Objectives] The geographic system is an integrated framework encompassing natural and human phenomena and their interrelationships on the Earth's surface. While Geographic Information Systems (GIS) can digitally process these geographic elements, they face challenges in addressing rapidly changing geographic contexts with complex 3D structures. This is primarily due to the lack of bi-directional interactions between physical and informational spaces, as well as their reliance on predefined rules and historical data. In this paper, we propose the concept of a “Geographic Intelligent Agent” as an advanced form of GIS, which integrates embodied intelligence, self-supervised learning, and multimodal language modeling to improve environmental perception, spatial understanding, and autonomous decision-making. [Methods] The architecture of the geographic intelligent agent consists of three core components: multimodal perception, an intelligent hub, and an action manipulation module. These components collectively acquire comprehensive environmental information through sensor networks, perform complex situatio reasoning using knowledge graphs and generative models, and enable real-time control and multilevel planning of the physical environment. To adapt to differences between virtual and real environments, the geographic intelligent agent is tested using the earth simulator and a test field platform, equipping it with stronger autonomous capabilities in complex and dynamic geographic contexts. [Results] This paper also demonstrates the implementation of geographic intelligent agent in spatial intelligence applications using the virtual digital human “EarthSage” as an example. [Conclusion] As a prototype of the geographic intelligent agent, "EarthSage" integrates modules such as the spatiotemporal Knowledge Ggraph (GeoKG) and a Cognitive Map Generation Model (GeoGPT), assisting users in obtaining intelligent spatial decision-making support in fields such as emergency management, urban planning, and ecological monitoring. This work exemplifies the transformation of GIS from a traditional information processing tool to an autonomous spatial intelligent system, marking a significant advancement in the field.

[Objectives] With the application of knowledge graph techniques in the field of Geographical Information Science (GIS), the Geographical Knowledge Graph (GeoKG) has become a key research direction. GeoKGs often lack sufficient geographic knowledge coverage, which can negatively impact downstream applications. Therefore, reasoning techniques are essential for GeoKG to complete missing knowledge, identify inconsistencies, and predict trends in geographic phenomena. Unlike reasoning techniques applied to general knowledge graphs, reasoning on GeoKGs must handle the unique and complex spatial and temporal characteristics of geographic phenomena. This paper comprehensively introduces and summarizes recent advances in GeoKG reasoning. [Analysis] First, it introduces the relevant concepts and problem definitions of GeoKG reasoning. Second, it analyzes the two core tasks of GeoKG reasoning: knowledge completion and prediction. The reasoning model for knowledge completion primarily fills gaps in the graph to ensure knowledge integrity, while the reasoning model for prediction aims to forecast future trends based on existing geographic data. These two models are optimized for different application scenarios, with different focuses in processing geographic data. [Prospect] Finally, the paper explores future development trends in GeoKG reasoning, highlighting areas such as processing complex relationships in spatiotemporal data, reasoning with multi-scale geographic knowledge, fusing multimodal data, and enhancing the interpretability and intelligence of reasoning models. Additionally, the integration of GeoKGs with large-scale pre-trained models is expected to become a key area of focus.

[Objectives] To systematically review recent advancements in text-to-image generation technology driven by large-scale AI models and explore its potential applications in urban and rural planning. [Discussion] This study provides a comprehensive review of the development of text-to-image generation technology from the perspectives of training datasets, model architectures, and evaluation methods, highlighting the key factors contributing to its success. While this technology has achieved remarkable progress in general computer science, its application in urban and rural planning remains constrained by several critical challenges. These include the lack of high-quality domain-specific data, limited controllability and reliability of generated content, and the absence of constraints informed by geoscience expertise. To address these challenges, this paper proposes several research strategies, including domain-specific data augmentation techniques, text-to-image generation models enhanced with spatial information through instruction-based extensions, and locally editable models guided by induced layouts. Furthermore, through multiple case studies, the paper demonstrates the value and potential of text-to-image generation technology in facilitating innovative practices in urban and rural planning and design. [Prospect] With continued technological advancements and interdisciplinary integration, text-to-image generation technology holds promise as a significant driver of innovation in urban and rural planning and design. It is expected to support more efficient and intelligent design practices, paving the way for groundbreaking applications in this field.

[Significance] As a representative of new-quality productivity, the low-altitude economy is gradually emerging as a new engine for economic growth. This economy is based on the development and utilization of low-altitude airspace resources. While bringing development opportunities to geospatial information technology, it also poses entirely new challenges. [Progress and Analysis] In this paper, we introduce the division of low-altitude airspace resources and highlight typical drone application scenarios in the context of the low-altitude economy. Subsequently, we analyze the broad application prospects of geospatial information technology in key areas of the low-altitude economy, including the refined utilization of airspace resources, the construction of low-altitude environments, the planning, construction, and operation of new air traffic infrastructure, as well as the safe and efficient operation and regulatory oversight of drones. We emphasize that the geospatial information industry will benefit from development opportunities such as the integration and innovation of emerging scientific and technological advancements, growing market demand, policy support, industrial guidance, and industrial upgrading and transformation. [Prospect] Finally, we briefly address the challenges geospatial information technology must overcome to meet the development needs of the low-altitude economy. These include advancements in spatio-temporal dimension elevation, map and location-based services, high-frequency and rapid data acquisition systems, all-time and all-domain capabilities, and ubiquitous intelligent technologies. These areas will also serve as future directions for development and breakthroughs in geospatial information technology.

[Objectives] Forecasting is a key research direction in Geospatial Artificial Intelligence (GeoAI), playing a central role in integrating surveying, mapping, geographic information technologies, and artificial intelligence. It drives intelligent innovation and facilitates the application of spatial intelligence technologies across diverse real-world scenarios. [Progress] This study reviews the historical development of GeoAI-driven spatiotemporal forecasting, providing an overview of prediction models based on statistical learning, deep learning, and generative large models. In addition, it explores the mechanisms of spatiotemporal dependence embedding within these models and decouples general computational operators used for modeling temporal, spatial, and spatiotemporal relationships. [Prospect] The challenges faced by intelligent prediction models include sparse labeled data, lack of explainability, limited generalizability, insufficient model compression and lightweight design, and low model reliability. Furthermore, we discuss and propose four future trends and research directions for advancing geospatial intelligent prediction technologies: a generalized spatial intelligent prediction platform incorporating multiple operators, generative prediction models integrating multimodal knowledge, prior-guided deep learning-based intelligent prediction models, and the expansion of geospatial intelligent prediction models into deep predictive applications for Earth system analysis.

In the context of the rapid development of urbanization, the reasonable selection of locations for public service facilities is critical for delivering efficient services and enhancing the quality of urban residents' lives. However, prevailing approaches for allocation of public service facilities often fall short of meeting the demands on their performance and efficiency in complex and large-scale real-world scenarios. To address these issues, this article proposed a novel Graph-Deep-Reinforcement-Learning Facility Location Allocation Model (GDRL-FLAM), coupling a Facility Location Allocation Graph Attention Network (FLA-GAT) with a Deep Reinforcement Learning (DRL) algorithm. This proposed model tackled the location allocation problem for public service facilities based on graph representation and the REINFORCE algorithm. To assess the performance and efficiency of the proposed model, this study conducted experiments based on randomly generated datasets with 20, 50, and 100 points. The experimental results indicated that: (1) For the tests with 20, 50, and 100 points, the GDRL-FLAM model exhibited a significant improvement ranging from 11.79% to 14.49% compared to the Genetic Algorithm (GA) which is one of the commonly used heuristic algorithms for addressing location allocation problems. For the tests with 150 and 200 points, the improvement ranged from 1.52% to 9.35%. Moreover, with the increase in the size of the training set, the model also demonstrated enhanced generalizability on large-scale datasets; (2) The GDRL-FLAM model showed strong transfer learning ability to obtain the location allocation strategies in simple scenarios and adapt them to more complex scenarios; (3) In the case study of Singapore, the GDRL-FLAM model outperformed GA significantly, achieving obvious improvements ranging from 1.01% to 10.75%; (4) In all these abovementioned tests and experiments, the GDRL-FLAM model showed substantial improvement in efficiency compared to GA. In short, this study demonstrated the potential of the proposed GDRL-FLAM model in addressing the location allocation issues for public service facilities, due to its generalization and transfer learning abilities. The proposed GDRL-FLAM could also be adapted to solve other spatial optimization problems. Finally, the article discussed the limitations of the model and outlined potential directions for future research.

Image super-resolution technology enhances image clarity and enriches image detail by improving image spatial resolution, enabling quality enhancement without changing hardware conditions. Given the large size, complex target features, and abundant details of remote sensing images, along with the need for efficient information acquisition, we propose a Diffusion Super-Resolution (DSR) algorithm based on a conditional diffusion model. This approach uses low-resolution remote sensing images from the same region as conditioning inputs to the diffusion model, while high-resolution images with added noise are concatenated as inputs. A deep noise training network was constructed with U-Net as the backbone, incorporating residual connections and self-attention mechanisms. The loss function was also improved for better super-resolution results. The DSR method was tested using high-resolution remote sensing images from multiple periods of the domestic Gaofen and SuperView satellite series. The super-resolution results demonstrated pixel dimension expansion from 32 to 128. Comparative experiments with Bicubic, SRGAN, Real-ESRGAN, and SwinIR super-resolution algorithms showed that the DSR method outperforms these algorithms in both PSNR and SSIM metrics. Additionally, the DSR method significantly improves the quality of multispectral remote sensing images. By leveraging the conditional diffusion model, it successfully preserves rich detail and enhances spatial resolution without compromising image clarity. This method offers an efficient solution for super-resolution reconstruction, ensuring effective information acquisition in remote sensing applications and fulfilling the requirements of various domains such as land use classification, environmental monitoring, and urban planning. Moreover, the DSR method also opens new avenues for future research by demonstrating the potential of diffusion models in remote sensing image processing. It overcomes the limitations of simple convolutional networks, which extract only shallow features, and avoids the convergence issues commonly seen in adversarial neural networks during training, ultimately improving the restoration of rich details in remote sensing images.

Building extraction is one of the important research directions that has attracted great attention in the field of remote sensing image processing. It refers to the process of accurately extracting building information such as the location and shape of buildings by analyzing and processing remote sensing images. This technology plays an irreplaceable and important role in urban planning, disaster management, map production, smart city construction, and other fields. In recent years, with the advancement of science and technology, especially the continuous evolution of earth observation technology and the rapid development of deep learning algorithms, Convolutional Neural Networks (CNNs) have become an emerging solution for extracting buildings from remote sensing images because of their powerful feature extraction capability. The aim of this paper is to provide a comprehensive and systematic overview and analysis of building extraction methods based on convolutional neural networks. We conduct a comprehensive literature review to summarize the building extraction methods from perspectives of model structure, multi-scale feature differences, lack of boundary information, and model complexity. This will help researchers to better understand the advantages and disadvantages of different methods and the applicable scenarios. In addition, several typical building datasets in this field are described in detail, as well as the potential issues associated with these datasets. Subsequently, by collecting experimental results of relevant algorithms on these typical datasets, a detailed discussion on the accuracy and parameter quantities of various methods is conducted, aiming to provide a comprehensive assessment of performance and applicability of these methods. Finally, based on the current research status of this field and looking forward to the new era of high-quality development in artificial intelligence, the future directions for building extraction are prospected. Specifically, this paper discusses the combination of Transformers and CNNs, the combination of deep learning and reinforcement learning, multi-modal data fusion, unsupervised or semi-supervised learning methods, real-time extraction based on large-scale remote sensing model, building instance segmentation, and building contour vector extraction. In conclusion, our review can provide some valuable references and inspirations for future related research, so as to promote the practical application and innovation of building extraction from remote sensing images. This will fulfill the demand for efficient and precise map information in remote sensing technology and other related fields, contributing to the sustainable and high-quality development of human society.

Urban green spaces are critical components of urban ecosystems, playing an irreplaceable role in improving the ecological environment and enhancing quality of life. High-precision identification of urban green spaces is fundamental for urban renewal and optimizing green infrastructure. However, research on the identification and spatial heterogeneity of green spaces in megacities remains relatively limited. This study, taking Xi'an as an example, integrates urban street view images and GF-2 (Gaofen-2) satellite imagery, employing methods such as ISODATA classification, K-Means classification, and convolutional neural networks to achieve multi-dimensional, downscaled, and high-precision identification and analysis of green spaces. The results indicate the following: (1) The K-Means classification method demonstrates significantly higher accuracy (84.5%) compared to the ISODATA classification method (62.4%) and more accurately maps the spatial characteristics and heterogeneity patterns of green spaces. The green space coverage identified by the K-Means method is 0.277 0, which is lower than the 0.360 7 identified by ISODATA. (2) The average Green View Index (GVI) of streets in Xi'an's main urban area is 0.156 0, indicating a generally good level of street greening. However, there is notable polarization across different roads, with 30% of sampling points having a GVI below 0.080 0. Overall, the GVI of higher-grade roads is greater than that of lower-grade roads, following the trend: primary roads > secondary roads > trunk roads > tertiary roads. (3) There is a positive correlation between the GVI of streets and the vegetation coverage in their surrounding areas in Xi'an's main urban area. However, this correlation weakens in certain road sections, reflecting differences between vertical cross-sections and overhead views of the streets. Combining these perspectives provides a more accurate assessment and quantification of urban green spaces. This study provides a reference for green space planning, green infrastructure construction, and smart management in Xi'an, as well as technical guidance for high-precision identification and spatial analysis of urban green spaces in other cities.

The measure of similarity of the building shape is crucial to the cartographic generalization process. Its research provides information on the contour of the building as a foundation for map analysis and the identification of spatial elements. Moreover, it is applied in many aspects, such as shape matching, shape retrieval, building simplification and building selection. With the development of neural networks, graph contrastive learning learns more discriminative representations by comparing positive samples from the same graph with negative samples from different graphs. Based on the advantages of the graph contrastive learning model,the study proposes a building shape similarity measurement model with the support of graph contrastive learning model, which aims to train a graph encoder to narrow the difference between positive samples and increase the gap between negative samples.The contrastive loss function and graph augmentation strategy are used to implement this operation. The following is the model's implementation process. Firstly, the vector building shapes are converted to the graph data structure and the point and edge features of the shapes are extracted.Secondly, two distinct views are generated as input to the encoder by applying various augmentation means, such as node dropping, edge removing, edge adding, and feature masking, to each graph. After that, the augmented graphs are then given to the graph encoder, which establishes each graph's feature encoding through the training process. Finally, the shape classification is achieved by a nonlinear classifier, and the extracted shape coding can be used to study shape similarities. The results indicated the shape classification accuracy of 96.7% using OSM shape data as training and testing samples. Furthermore, feature and node direction analysis, graph augmentation analysis, and parameter sensitivity analysis were carried out.The experimental results show that the classification accuracy rates of the HU moment method, Fourier method, and GCAE method are 22.9%, 44.4%, and 92.5%, respectively. Therefore, the method proposed in this paper outperforms traditional methods and deep learning in shape recognition capability.With a 95.7% shape classification accuracy, three areas of Hong Kong were chosen for shape matching and shape classification. And conducted shape matching tests on 9 typical shapes, finding that the similarity values of similar shapes were much greater than those of dissimilar shapes, consistent with visual perception.The graph contrastive learning model has effectively enhanced the recognition capability of complex shapes, providing technical support for applications such as cartographic generalization, spatial queries, shape matching, and shape retrieval.

China is one of the countries most severely affected by geological disasters. Researching high-precision and highly reliable methods for monitoring and predicting landslide deformation holds practical significance for disaster prevention and mitigation efforts. Using the massive Outang landslide in the Three Gorges Reservoir Area as a case study, this paper addresses the issue of the atmospheric interference in extracting landslide deformation using time-series InSAR technology. To correct for atmospheric effects, the GACOS model is introduced and validated against GNSS observation data. To address the often-overlooked temporal-spatial analysis before landslide deformation prediction, the Moran index and Hurst index are calculated to analyze the spatiotemporal characteristics of landslide deformation. Recognizing that landslide deformation is influenced not only by historical deformation but also by various external factors, this paper proposes coupling landslide influencing factors with deformation data for prediction. A Long Short-Term Memory (LSTM) model, optimized by Variational Mode Decomposition (VMD) and the Sparrow Search Algorithm (SSA), is employed for the prediction. By decomposing landslide displacement data into trend, periodic, and random components using VMD, the LSTM network structure is constructed. SSA is used to determine the optimal number of hidden units, maximum training periods, and the initial learning rate of the LSTM model. Additionally, methods such as data normalization, regularization, and model evaluation are employed to enhance the performance and stability of the LSTM model. Finally, the model is trained using the influencing factors and decomposed displacement data to predict landslide deformation. The results indicate that: (1) From January 2021 to June 2023, the maximum and minimum deformation rates of the Outang landslide were -72.75 mm/a and 50.74 mm/a, respectively; (2) The deformation in the study area exhibits positive spatial autocorrelation, with the landslide in the settlement area showing a persistent trend; (3) The prediction error of the LSTM model optimized by VMD and SSA is only 0.37 mm, representing an 11.004% accuracy improvement compared to the standard LSTM model. Based on time-series InSAR technology and spatiotemporal analysis results, this paper constructs a high-precision prediction model for landslide deformation, incorporating multiple influencing factors. This model provides a valuable reference for the prevention and control of landslide disasters.

[Significance] As a comprehensive observation of natural resource development and utilization, spatio-temporal big data on natural resources contains valuable knowledge about resource distribution, spatio-temporal process evolution, and interrelationships. [Progress] This paper examines spatio-temporal big data mining and knowledge services for natural resources, highlighting key data mining techniques and their critical applications in knowledge services. First, it introduces the core concepts, technical frameworks, and methodological processes of spatio-temporal clustering analysis, association mining, anomaly detection, predictive modeling, and geographic risk assessment, along with their applications in natural resource management and land-use decision-making. Second, a four-tier natural resource spatio-temporal knowledge service system is proposed, encompassing descriptive, diagnostic, predictive, and decision-making knowledge services, which provide essential support for applications such as resource status monitoring, land-use regulation, and disaster prevention and mitigation. Finally, the paper indicates that current natural resource management is transitioning from data aggregation and analysis to knowledge-driven intelligent services, forming an emerging research and application paradigm of big data, big analysis, big knowledge, and big services. [Prospect] Future efforts will focus on advancing collaborative data and knowledge mining technologies, addressing the standardization challenges in spatio-temporal knowledge bases and services, and exploring the potential of cutting-edge technologies such as generative large models in the natural resource domain to drive the information and intelligent transformation of natural resource management.

[Significance] Data resources have become pivotal in modern production, evolving in close synergy with advancements in artificial intelligence (AI) technologies, which continuously cultivate new, high-quality productive forces. Remote sensing data intelligence has naturally emerged as a result of the rapid expansion of remote sensing big data and AI. This integration significantly enhances the efficiency and accuracy of remote sensing data processing while bolstering the ability to address emergencies and adapt to complex environmental changes. Remote sensing data intelligence represents a transformative approach, leveraging state-of-the-art technological advancements and redefining traditional paradigms of remote sensing information engineering and its applications. [Analysis] This paper delves into the technological background and foundations that have facilitated the emergence of remote sensing data intelligence. The rapid development of technology has provided robust support for remote sensing data intelligence, primarily in three areas: the advent of the big data era in remote sensing, significant advancements in remote sensing data processing capabilities, and the flourishing research on remote sensing large models. Furthermore, a comprehensive technical framework is proposed, outlining the critical elements and methodologies required for implementing remote sensing data intelligence effectively. To demonstrate the practical applications of remote sensing data intelligence, the paper presents a case study on applying these techniques to extract ultra-high-resolution centralized and distributed photovoltaic information in China. [Results] By integrating large models with remote sensing data, the study demonstrates how remote sensing data intelligence enables precise identification and mapping of centralized and distributed photovoltaic installations, offering valuable insights for energy management and planning. The effectiveness of remote sensing data intelligence in addressing challenges associated with large-scale photovoltaic extraction underscores its potential for application in critical fields. [Prospect] Finally, the paper provides an outlook on areas requiring further study in remote sensing data intelligence. It emphasizes that high-quality data serves as the foundation for remote sensing data intelligence and highlights the importance of constructing AI-ready knowledge bases and recognizing the value of small datasets. Developing targeted and efficient algorithms is essential for achieving remote sensing intelligence, making the advancement of practical data intelligence methods an urgent research priority. Furthermore, promoting multi-level services for remote sensing data, information, and knowledge through data intelligence should be prioritized. This research provides a comprehensive technical framework and forward-looking insights for remote sensing data intelligence, offering valuable references for further exploration and implementation in critical fields.

Traditional settlements have gathered a wealth of traditional cultural resources such as ancient architecture and folklore, which have attracted significant attention for their outstanding historical, cultural and artistic values, and it is of positive significance to extract their abundant historical and cultural information and serve them for modern industrial development. Currently, there is a lack of knowledge extraction, organization and expression of the rich historical and cultural information of traditional settlements based on geographic knowledge extraction and expression perspectives to achieve the transformation of "data-information-knowledge-wisdom", this paper proposes the geographic ontology of cultural landscape genes of traditional settlements (GeoOnto-CLGTS) and explores the intrinsic correlation characteristics of the traditional landscape genes of traditional settlements. Firstly, combining the geographic information ontology and characteristics of traditional settlement landscape genes, the concept and expression method of GeoOnto-CLGTS are analyzed, and this paper proposes the construction method of GeoOnto-CLGTS model. Secondly, combing the landscape gene concepts, association relationships and data attribute characteristics, the seven-step geographic information ontology modeling method is applied to construct the conceptual layer of GeoOnto-CLGTS from top-down. By utilizing Protege tool to supplement examples using 123 traditional Chinese settlements as cases, the instance layer construction of the GeoOnto-CLGTS model is achieved. Finally, the GeoOnto-CLGTS data is stored through the Neo4j graph database to complete the construction of the knowledge graph of traditional settlement landscape genes, enabling the retrieval of landscape gene information. The results show that the GeoOnto-CLGTS constructed in this paper can provide a valuable reference for carrying out knowledge discovery of traditional settlement cultural resources and promoting digital preservation of traditional settlements in the future.

[Objectives] Street space serves as the primary perceptual interface for pedestrians in urban environments, and the visual quality of these spaces plays a crucial role in enhancing their vitality. Traditional evaluation methods often rely on single-objective indicators, making it difficult to effectively link objective environmental features with pedestrians' subjective perceptions. [Methods] This study proposes a novel evaluation framework based on Large Language Models (LLMs), incorporating the style dimension of subjective perception and extending traditional single-indicator quantitative analysis to a comprehensive approach that integrates both quantification and stylization. This framework utilizes Baidu Street View imagery to quantitatively assess two objective indicators, namely green view index and sky view factor, through semantic segmentation techniques. Additionally, it evaluates six subjective indicators, including vegetation diversity, building typology, building continuity, sidewalk usage, roadway usage, and signage usage, by leveraging prompt-optimized LLMs. The study then categorizes street space visual quality features within the research area using the Latent Dirichlet Allocation (LDA) topic model, aiming to explore the spatial characteristics of different streets and identify optimization strategies. [Results] Using Beijing's Xicheng District as the study area, the results reveal spatial distribution patterns of vegetation density and sky openness, along with pedestrians' subjective evaluations of indicators such as vegetation diversity and building type. Cluster analysis identified comprehensive service streets centered around Xidan North Street, characteristic streets centered around Xihuangchenggen South Street, and mixed-type streets centered around Lingjing Hutong. [Conclusions] This study innovatively introduces a large language model with human-like perceptual capabilities, enhancing its performance through prompt engineering. The resulting framework enables efficient and integrated evaluation of street visual quality by combining both objective and subjective factors. This approach provides a practical reference for large-scale, automated analysis of street view imagery.

Realizing convenient transfers between subway and regular bus systems is fundamental to advancing the integration and development of these two transportation networks, which is crucial for constructing a multi-modal and accessible public transportation system. This paper takes Shenzhen as a case study and innovatively combines mobile phone signal data with IC card data to identify the transfer characteristics between subway and regular bus systems. These characteristics include temporal and distance aspects, which effectively illustrate the daily travel patterns of transfer passengers. Through a detailed analysis of the overall transfer characteristics, this study establishes a distance threshold to estimate potential transfer demand and the gap in transfer demand at each subway station. Furthermore, this paper uses the Entropy Weight-TOPSIS Model to conduct a preliminary evaluation of the transfer supply conditions at various subway stations. Based on the evaluation results of the matching between transfer supply and demand, as well as the size of the transfer demand gap, this study proposes corresponding optimization strategies for subway stations, providing an effective method for identifying inefficient stations. The research findings indicate that, in Shenzhen, the subway stations with high potential demand for transfers to regular buses are mainly located near densely populated residential areas. The central urban area exhibits a high degree of matching between transfer supply and demand, with some old urban areas experiencing an oversupply due to the well-developed public transportation infrastructure. However, peripheral stations commonly face a situation where demand exceeds supply, necessitating focused attention on improving transfer supply conditions at these sites. Regarding the transfer demand gap, even among subway stations with the same level of transfer supply, variations in the size of the demand gap exist. Stations with insufficient transfer supply but efficient operations offer valuable lessons, while stations with large demand gaps and inefficient operations should be targeted for specific improvements based on their individual supply and demand matching situations. The results demonstrate that evaluating the alignment between potential subway-bus transfer demand and the level of transfer supply using multi-source data, and formulating optimization strategies in conjunction with the transfer demand gap, is of significant importance for enhancing the refined management level of subway and bus transfer services. Overall, the theory and calculation methods of transfer potential demand and transfer demand gap proposed in this study provide a new perspective and reference for transfer research, public transport planning, and urban planning in the field of public transportation.

The essence of geospatial knowledge lies in unveiling the spatiotemporal distribution, dynamics of change, and interaction patterns of geographical entities and phenomena. However, existing knowledge base management platforms often overlook the specific needs of geospatial knowledge representation and lack the capability to handle the unique attributes of geospatial data, making it challenging to meet the requirements for constructing and applying geospatial knowledge graphs. The Geospatial Knowledge Base Management System (GeoKGMS) is designed on the basis of an integrated geospatial knowledge base engine that efficiently aggregates geospatial knowledge resources across various modalities—'Image-Text-Number'—automates the construction of geospatial knowledge graphs, and facilitates a one-stop geospatial knowledge engineering process. This paper elucidates four key technologies for managing geospatial knowledge bases. First, the cloud-native geospatial knowledge base microservice unified scheduling technology decomposes the large geospatial knowledge base management system into fine-grained, independently operable, and deployable microservices. By comprehensively managing the lifecycle of the geospatial knowledge base, service classification and orchestration methods are determined to achieve unified scheduling of these microservices. Second, a human-computer collaborative geospatial knowledge graph construction method is proposed, supporting the sustainable, collaborative construction of geospatial knowledge graph engineering. Third, the spatiotemporal hybrid encoding technology of the geospatial knowledge graph achieves unified representation of geospatial knowledge by integrating multimodal geospatial data and spatiotemporal information. Fourth, a multimodal geospatial knowledge integrated storage and large-scale spatiotemporal graph partitioning technology is proposed to address the challenges of efficiently managing complex structured geospatial knowledge and retrieving large-scale spatiotemporal knowledge tuples. Based on these key technologies, an application service framework for GeoKGMS has been designed, featuring six functional modules: geospatial knowledge base management, multimodal geospatial knowledge extraction, human-computer collaborative construction of geospatial knowledge graphs, geospatial knowledge reasoning, geospatial knowledge graph quality assessment, and geospatial knowledge visualization. To demonstrate GeoKGMS's capabilities, the Karst landform knowledge graph is used as a case study. The Karst landform knowledge graph is an integrated 'Image-Text-Number' geospatial knowledge graph, constructed based on geospatial knowledge extracted from the texts, schematic diagrams, and related maps in geomorphology textbooks. Through a collaborative pipeline, geomorphology experts and computers jointly perform tasks such as mapping, alignment, supplementation, and conflict resolution of geospatial knowledge. This collaboration ultimately leads to the automated construction of the Karst landform knowledge graph by GeoKGMS. The resulting graph is highly consistent with expert knowledge models, ensuring the interpretability of knowledge-driven geocomputation and reasoning in practical applications.

Spatial-temporal data missingness and sparsity are prevalent phenomena, for which spatial-temporal interpolation serves as a critical methodology to address these issues. Spatial-temporal interpolation constitutes a significant research domain within the field of Geographical Information Science. This technique enables the capture of dependencies in spatial-temporal data and the estimation of the geometric and attribute variations of geographical phenomena over time. With the advancement of geospatial technologies, particularly Geographic Information Systems, contemporary spatial-temporal interpolation methods predominantly rely on statistical, machine learning, and deep learning approaches that account for both temporal and spatial dimensions. These methods aim to reveal the evolutionary processes and spatial-temporal distribution patterns inherent in the data. However, a majority of such techniques often overlook long-term dependencies and contextual spatial information when interpolating. This study proposes an innovative model that intertwines Long Short-Term Memory (LSTM) networks with spatial attributes to address these limitations effectively. The proposed model operates through several key stages: (1) It employs a dedicated spatial layer to systematically eliminate weakly correlated information, focusing on extracting and feeding more significantly correlated spatial data into the LSTM network. (2) Given that conventional Artificial Neural Network (ANN) models are unable to consider the impact of the temporal dimension on interpolation, and unidirectional LSTM models can only factor in past moments' influence without utilizing future moment information, this research adopts a Bidirectional LSTM (BiLSTM) architecture. The BiLSTM inherently captures both spatial and temporal dependencies, thereby overcoming previous limitations. (3) To further enhance its performance by efficiently extracting comprehensive global spatial features while maintaining the advantages of bidirectional modeling offered by BiLSTM, we integrate a self-attention mechanism into the BiLSTM framework. This results in a novel, fused Bidirectional LSTM Interpolation Model with Spatial Layer-Self Attention (SL-BiLSTM-SA). In the experimental phase, the SL-BiLSTM-SA model is rigorously applied to a PM_2.5 concentration dataset from Shandong Province to conduct a meticulous investigation into its interpolation capabilities. Upon comparative analysis against other models, it is evident that the SL-BiLSTM-SA model outperforms with notably lower error metrics, demonstrating substantial improvements in accuracy—by 39.83% and 36.63% when compared to Spatio-Temporal Ordinary Kriging (STOK) and Genetic Algorithm-optimized Spatio-Temporal Kriging (GA-STK) methods, respectively. Moreover, our model exhibits commendable precision in forecasting high and low concentration levels. By seamlessly integrating spatial information and coupling the strengths of BiLSTM with self-attention mechanisms, this research not only extends the suite of interpolation methods for spatiotemporal data analysis but also furnishes robust theoretical underpinnings and methodological support to facilitate sophisticated spatiotemporal data analyses.

[Objectives] With the development of deep learning technology, the ability to monitor changes in natural resource elements using remote sensing images has significantly improved. While deep learning change detection models excel at extracting low-level semantic information from remote sensing images, they face challenges in distinguishing land-use type changes from non-land-use type changes, such as crop rotation, natural fluctuations in water levels, and forest degradation. To ensure a high recall rate in change detection, these models often generate a large number of false positive change polygons, requiring substantial manual effort to eliminate these false alarms. [Methods] To address this issue, this paper proposes a natural resource element change polygon purification algorithm driven by remote sensing spatiotemporal knowledge graph. The algorithm aims to minimize the false positive rate while maintaining a high recall rate, thereby improving the efficiency of natural resource element change monitoring. To support the intelligent construction and effective reasoning of the spatiotemporal knowledge graph, this study designed a remote sensing spatiotemporal knowledge graph ontology model taking into account spatiotemporal characteristics and developed a GraphGIS toolkit that integrates graph database storage and computation. This paper also introduces a vector knowledge extraction method based on the native spatial analysis of the GraphGIS graph database, a remote sensing image knowledge extraction method based on efficient fine-tuning of the SkySense visual large model, and a polygon purification knowledge extraction method based on the SeqGPT large language model. Under the constraints of the spatiotemporal ontology model, vector, image, and text knowledge converge to form a remote sensing spatiotemporal knowledge graph. Inspired by the manual operation methods for change polygon purification, this paper developed an automatic purification method of change polygons based on first-order logical reasoning within the knowledge graph. To improve the concurrent processing and human-computer interaction, this paper developed a remote sensing spatiotemporal knowledge graph management and service system. [Results] For the task of purifying natural resource element change polygons in Guangdong Province from March to June 2024, the proposed method achieved a true-preserved rate of 95.37% and a false-removed rate of 21.82%. [Conclusions] The intelligent purification algorithm and system for natural resource element change polygons proposed in this study effectively reduce false positives while preserving real change polygons. This approach significantly enhances the efficiency of natural resource element change monitoring.

With the expansion of urban areas, a mix of transportation modes has become prevalent during the daily commutes of city dwellers. That is, commuters often need to transfer between various modes to reach their destinations. Accurate identification and analysis of these transfer behaviors are crucial for advancing urban transportation research. Current research tends to focus on distance or time thresholds, typically derived from walking speeds or anecdotal experience. However, these approaches often overlook the distinct station densities within cities. Other studies, while utilizing GPS, GTFS, and similar datasets, construct intricate transfer identification methods that lack generalizability. Against this backdrop, we introduce a time-distance dual-constraint transfer recognition algorithm. Firstly, leveraging extensive traffic IC card data, based on the statistical characteristics of the proximity distance sequences between bus or subway stations and their M neighboring stations, distance thresholds for bus-bus, bus-subway, and subway-bus transfer are detected individually. Subsequently, a filtering algorithm based on these distance thresholds is applied to daily data to produce a candidate transfer data set. Based on this, four time thresholds for each day are determined by analyzing the statistical characteristics of the transit time differences within the datasets. Finally, these dual thresholds facilitate the precise extraction of transfer behaviors. Furthermore, we establish a classification framework for these behaviors, classifying them into nine distinct transfer modes. These modes are defined based on the duration of travel time in the first and second journeys, encompassing variations including long-long, long-medium, long-short, middle-long, middle-middle, middle-short, short-long, short-middle, and short-short. We analyze these models individually for their travel characteristics. Results reveal that the morning peak for all transfer trips precedes that of buses and subways, with short-long transfers leading by up to 30 minutes. This underscores the added effort required by commuters who rely on transfers. In contrast, evening peak times vary, with certain transfer modes like long-long and long-short lagging notably behind the general evening peak. This further emphasizes the increased commuting burden associated with transfers. In terms of travel distances, the peak of regular subway travel distances is around 10 km, while that of the bus travel distances is around 1 km. The peak commuting distances for all nine transfer behaviors are greater than those of typical trips and are distributed within a range of 20~40 km. In summary, our method for extracting and analyzing transfer behaviors offers a robust and effective tool for urban transportation research, urban vitality assessment, public transportation planning, and urban planning.

With the increasing improvement of the living standard of the residents in urban areas and their pursuit of quality of life, urban green spaces have become the main places of leisure and recreation for residents. Under this background, how to fairly evaluate the rationality of the layout of urban green spaces and put forward suggestions for improvement has become an important part of urban transportation and land use planning. Urban green space accessibility is a key indicator for evaluating the layout of urban green spaces. In response to the limitations of assessing attractiveness based solely on urban green space area in the past, this paper takes Suzhou urban area as an example. In addition to calculating accessibility using objective attributes in the traditional framework, the paper delves into social media data to incorporate urban residents' subjective sentiment towards urban green space quality indicators into the consideration scope of attractiveness. Through this innovative integration, the paper improves the Two-Step Floating Catchment Area (2SFCA) method, analyzing in-depth the accessibility of urban residents to urban green spaces and the dynamic changes in accessibility before and after public health emergencies. The improved 2SFCA method, combined with Sentiment Knowledge Enhanced Pre-training (SKEP) model, incorporates residents' emotional evaluations of urban green spaces to measure their subjective attractiveness. Meanwhile, considering the skewness characteristic of area indicators, the paper innovatively proposes the Scale Index (SI) as an objective attractiveness evaluation indicator for urban green spaces, providing more scientific and robust support for urban green space planning. The research findings reveal that during public health emergencies, individuals tend to prefer urban green spaces that offer convenient access, such as community parks. However, as daily life gradually resumes, there is a greater preference for urban green spaces equipped with high-quality facilities, such as specialized parks. Only considering objective area as the attractiveness of urban green space leads to overestimation of the accessibility of large-area and underestimation of small-area urban green space. Moreover, solely based on visitors' subjective quality perception of urban green space may underestimate the accessibility of communities around large urban green spaces. The improved 2SFCA method, considering both visitors' subjective perception and objective attributes of urban green space attractiveness, can more accurately assess urban green space accessibility, broadening the perspective of traditional urban green space accessibility assessment. This method can not only be applied to urban green space planning, but also provides a new idea and computational framework for the accessibility analysis of public service facilities.

Scientific and accurate monitoring of mangroves is the basis and premise for protecting marine coastal wetland ecosystems. Multi-source remote sensing data can be used to classify mangrove species effectively, but challenges remain in applying optical and SAR image features along with their time-varying information. In this paper, based on Sentinel-1/2 image data, we propose a mangrove species classification framework using Multi-source Features-coupled and Ensemble Learning algorithm (MFEL). The framework analyzs the classification advantages of spectral index features, SAR polarization features, and their temporal harmonic spectral features in feature selection and coupling. It then stacks the Random Forest (RF) and eXtreme Gradient Boosting (XGBoost) models to construct an Ensemble Learning model for mangrove species classification. Comparing the RF classification model and XGBoost models based on feature optimization, we evaluated the classification accuracy and feature application differences of the MFEL classification method. Zhanjiang Mangrove Forest National Nature Reserve was selected as the experimental area. The results show that: ① compared to using only spectral index features, classification accuracy improves by 6% and 8% with the addition of SAR polarization features or temporal harmonic spectral features, respectively. Adding both SAR polarization features and temporal harmonic spectral features simultaneously improves classification accuracy by 12%, making it more effective for mangrove species classification. ② The MFEL method achieves the highest classification accuracy, with an overall accuracy of 88.03% and a Kappa coefficient of 0.86. When the MFEL model trained on samples from the experimental area was applied to other areas, the classification accuracies were 83.94% and 82.77%, respectively. ③ This study verifies the potential application of SAR polarization features and time-sequence harmonic spectral features in mangrove species classification, significantly improving the accuracy for five mangrove species, with accuracies ranging from 76% to 91%. The study results provide valuable insights for expanding the use of medium-resolution remote sensing satellite imagery in monitoring mangrove species.

[Objectives] Land Use and Land Cover (LULC) plays a crucial role in shaping surface environments and ecological processes. Among various land cover types, built-up land, representing the dominant form of anthropogenic surface modification, has expanded rapidly in recent decades, exerting significant impacts on regional ecosystems while attracting increasing attention from multiple disciplines. This study aims to improve the spatial accuracy of built-up land mapping by evaluating and integrating multiple LULC datasets, thereby supporting research on regional sustainable development. [Methods] Taking the Bohai Rim region as the study area, seven medium to high-resolution LULC products from domestic and international sources were initially selected. Based on a comparative analysis of total built-up area and spatial distribution patterns, five datasets (ESA2020, CoLUCC2020, GlobeLand2020, CLCD2023, and GLC_FCS2022) were chosen for further evaluation and integration. Consistency analysis was conducted to assess the classification performance of each dataset, and a multi-criteria evaluation combined with threshold-based filtering was employed for multi-source data fusion. [Results] Evaluation results indicated that the ESA2020, CoLUCC2020, GlobeLand2020, and GLC_FCS2022 datasets exhibit relatively high classification accuracy for built-up land, while the CLCD2023 dataset performs less satisfactorily. The fused product achieved an overall accuracy of 93.51% and a Kappa coefficient of 0.745 5, demonstrating notable improvements over any individual dataset. [Conclusions] The proposed fusion method effectively overcomes the limitations of single-source data by leveraging the complementary strengths of multiple datasets. It provides a robust methodological foundation for regional LULC data integration and offers valuable data support for sustainable development research in the Bohai Rim and similar regions.

The rapid development of information and communication technologies and mobile computing has generated a variety of mobility big data, providing new opportunities for understanding and exploring the spatiotemporal distribution and mobility characteristics of resident travel, and further contributing to the construction of smart cities. However, the emerging mobile data have experienced significant growth in both scale and complexity compared to traditional data, posing challenges for its structural characteristic analysis. To address these issues, this paper proposes an analytical framework to deal with the spatiotemporal distribution characteristics of high-dimensional ride-hailing travel pattern. Compared to traditional square partitions, a regular hexagon is closer to a circle, and the six adjacent hexagons connected to its edges are symmetrically equivalent, which can be more advantageous in aggregating demands with similar travel characteristics into the same partition. Therefore, hexagonal partition is selected as the basic clustering unit, and different spatiotemporal patterns are identified by clustering homogeneous travel distribution groups. Firstly, the spatiotemporal characteristics of travel distribution aggregated in the hexagonal partition are summarized into three main components: the departure demand distribution at the origin partition, the spatial distribution at the destination partition, and the arrival demand distribution at the destination partition. The spatiotemporal similarity between two partitions can be expressed as the product of these three types of distribution similarity. Furthermore, a Clustering Algorithm with Fast Search and Find of Spatiotemporal Density Peaks (CFSFSTDP) is proposed to identify the spatiotemporal patterns of ride-hailing travel distribution in each partition. The spatiotemporal distances between different partitions are obtained through the calculation of spatiotemporal similarity. Finally, affinity propagation clustering algorithm is used to perform clustering analysis on the time series variation pattern of spatiotemporal pattern of travel distribution in each partition. The time series similarity of spatiotemporal patterns between different partitions is represented by the sum of Euclidean distances between time series of each interval, and the model converges through continuous updates of attractiveness and affiliation indices. Through the empirical analysis of Didi Chuxing order data in Chengdu for one month, the validity of the method is verified. Based on the identified seven spatiotemporal distribution patterns, the differences of spatiotemporal patterns in the size, location, and time of demand are analyzed, and the functional types of ride-hailing travel in different partitions are discussed. The identified six time series patterns better grasp the time continuity of spatiotemporal patterns of ride-hailing travel distribution and help to better build the corresponding spatiotemporal evolution digital.

Technological knowledge has become the key element of regional innovation and development in the new era. Exploring the inherent mechanism of the growth and development of technology transfer network is of great significance to improve the vitality of regional innovation. However, the endogenous mechanisms and spatial differences of technology transfer network evolution is rarely studied. This study aims to gain a better understanding of the growth and development process of urban technology transfer networks in China and their spatial differences. First, this paper takes 282 cities of China as research units. Second, information on patent transferred data is subjected to ownership linkage mode to construct the urban technology transfer network, resulting in a panel dataset of 282 cities in China in 2001—2020. Finally, stochastic actor-oriented models for the evolution of networks are constructed to study the evolution of technology transfer networks and spatial heterogeneity. Results show that: (1) The evolution pattern of urban technology transfer network in China presents a "core-periphery" structure. The network exhibits strong polarization characteristics, but it is decreasing gradually. The increasingly complex tripartite relationship between cities is an important feature of network evolution. These tripartite relations not only affect the formation of link relations but also promote the differentiation of local levels of the network; (2) Endogenous structural factors are the key factors for the growth and development of urban technology transfer network in China. Reciprocity and network closure constitute the micro basis of the evolution of urban technology transfer network. Path dependence is a key force in strengthening the link relationship between urban technology transfer networks; (3) The endogenous mechanism of the evolution of urban technology transfer network in China has obvious spatial heterogeneity. In the southern region of eastern China, the urban technology transfer network has strong dynamics. Reciprocity, network closure, and path dependence have become the endogenous driving forces for the growth and development of technology transfer networks. In the northern region of eastern China, the evolution rate of the network shows a downward trend. Reciprocity and path dependence contribute to the formation of network link pattern. In the northwest inland and the Qinghai-Tibet Plateau, the network evolution rate tends to increase, but the network density is small, and only the reciprocity effect is significant. This paper will deepen the understanding of the evolution law of urban networks and provide a scientific reference for China's urban innovation and development policy.

With the acceleration of population aging, the urban built environment for the elderly faces severe challenges. Urban street environments, one of the most frequently used places by the elderly, require high-quality construction, which is vital for realizing an age-friendly society. However, few studies have focused on the spatial effects and influencing factors of urban street environment quality for the elderly from a large-scale and human perspective, resulting in difficult practical applications. Therefore, this study took Tianhe district, Guangzhou as a study area, using machine learning and deep learning technology to evaluate the urban street environment quality for the elderly and analyze its spatial distribution and influencing mechanisms. Based on 14 916 human-centric street view images taken by panoramic cameras, semantic segmentation and object detection techniques were used to extract environmental elements. Greenness, openness, crowdedness, enclosure, sidewalk ratio, and scene diversity were obtained finally as explanatory variables in this study. A human-machine adversarial scoring system was constructed for the age-friendly street environment quality assessment. Twenty-two elderly volunteers were invited to rate their sense of walkability, vitality, security, belonging, and pleasure from 1 000 randomly selected images. A residual neural network 50 (ResNet50) was used to predict the urban street environment quality in the Tianhe district based on street view images and crowd-sourced data. The spatial autocorrelation was measured by global and Local Moran's I. Ordinary Least Square regression model (OLS), Spatial Lag Model (SLM), and Spatial Error Model (SEM) were established to analyze the influence mechanisms. Results show that: (1) Using human-centric street view images, machine learning, and spatial statistics methods, this study conducted a fast, large-scale, and precise age-friendly street environment quality assessment and accounted for spatial heterogeneity to identify its key influencing factors; (2) There was a moderate degree of spatial aggregation of different street environment qualities for the elderly in the Tianhe district. For older people, commercial streets and streets near low-density residential areas were associated with higher levels of walkability, activity, sense of safety, and pleasure. Although waterfront streets had higher levels of activity and security, the level of pleasure was low. Streets near high-density residential areas were found to have lower levels of activity level, sense of safety, and pleasure. The sense of belonging was higher in commercial streets and lower in streets close to residential areas; (3) The effects of environmental factors on different street environment quality indexes for the elderly were different. Greenness, openness, and enclosure were important factors while visual crowdedness, sidewalks, and scene diversity played a weak role. Greenness had a positive effect on activity level and sense of safety, but a negative effect on pleasure and sense of belonging. Openness was positively correlated with walkability, pleasure, and sense of belonging, and negatively correlated with activity levels. Enclosure had negative effects on all indicators, especially the sense of belonging. These results reveal the spatial association, heterogeneity, and influencing mechanisms of the street environment quality for the elderly based on human-centric street view images, machine learning, and deep learning techniques on a large urban scale. It shows a feasible paradigm to analyze the street environment for the elderly, providing practical implications to build resilient streets more conducive to an age-friendly society. It's of great value for policy-making, urban planning, and management.

The timely identification of potential criminal travel routes of key surveillance individuals is a crucial research focus for public security early warning systems. Current studies often concentrate on the travel patterns and destination preferences of criminals, but there is a lack of research from the criminals’ perspective, considering the built environment and road network structure to analyze their criminal travel routes. To address this gap, a spatiotemporal analysis approach is proposed, considering criminals’ cognition of concealment. Based on the principles of criminal psychology and rational choice theory, this paper categorizes the travel patterns of criminals under the cognition of concealment into two principles: "the Principle of Minimal Exposure Risk" and "the Principle of Minimal Travel Cost". Firstly, the urban perceptual elements within a criminal's perceptual range, including safety and risk perception elements that bring exposure risks, are calculated and introduced into the choice degree model. The "Criminal Choice Degree of Roads" is proposed to measure the optimal local roads within a criminal's perceptual range. Next, using the "Length of the Route Already Traveled" as the cost function and the "Criminal Choice Degree of Roads" within the perceptual range as the heuristic function, an improved heuristic algorithm is employed to calculate the overall optimal criminal travel route. Finally, from the perspective of crime prevention and control, experiments are conducted to analyze the distribution of urban road choice degrees and the criminal travel routes of key personnel. By comparing the routes obtained by this method with those derived from existing methods and actual criminal travel routes, it is shown that the routes calculated by this proposed method are more reasonable. They are more likely to be chosen by criminals for concealment and have relatively short travel distances, without long-term exposure in public areas. The routes, in terms of distance, travel time, and the urban perceptual elements they pass through, are closer to the actual travel behaviors of criminals, verifying the rationality of this method. The research conclusion provides decision support for public security early warning efforts, emphasizing the importance of balancing travel distance and exposure risk when monitoring key personnel, and the need to allocate resources based on the distribution of urban perceptual elements and road networks to enable timely and accurate crime prevention and interception.