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.

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.

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.

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.

[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.

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.

Low-altitude space is an important component of urban space. Requisite measures for precise and meticulous management of urban low-altitude are indispensable. The urban low-altitude space should be characterized by spatial coordinates and possess significant geographic attributes. With the increasing adoption of low-altitude applications in urban areas, the intricate utilization of space, represented by low-altitude traffic, has transcended conventional airspace boundaries and encroached upon near-ground urban space, exerting an impact on urban architecture and human settlement environment. Emphasizing the 3D land space's utilization and service, the issue of the utilization and management of 3D land space has become increasingly conspicuous. The land parcel serves as the fundamental unit for urban land management, recording information of ownership relationships, spatial rights, and interests information. The establishment of easements between land parcels ensures the lawful utilization of relevant spaces. Given the inclusion of low-altitude flight activities within the purview of urban land management, there is an urgent imperative to elucidate the spatial utilization and impact of low-altitude passage processes on land space while establishing the service rights of urban low-altitude passage to safeguard parcel interests. However, the concept of easement is limited by the cognitive constraints imposed by a 2D land plane, neglecting the modeling and representation of 3D space. Precisely articulating the easement relationship formed by low-altitude passage activities in urban low-altitude space poses a significant challenge. Utilizing GIS technology for modeling urban spaces and facilitating the characterization and mapping from the physical to digital realms has consistently served as a crucial information tool in urban space management. Drawing upon the core principles of GIS modeling methodology and conceptual modeling, this paper presents a conceptual approach for describing low-altitude passage easement in urban areas. By analyzing the movement conditions of aircraft in urban low-altitude traffic, considering the impact on human settlement rights caused by overflight, and examining the path utilization of aircraft in three-dimensional space, we develop a constrained spatial modeling for low-altitude passage easement as a geometric description of the conceptual model. By integrating the spatial characteristics of 3D land parcels, we integrate the supply and service conditions of these parcels in terms of their space utilization, aircraft takeoff and landing modes, and path utilization. As a result, we propose a comprehensive supply and service model to address the demand for traffic within 3D parcels. The semantic relationship among low-altitude planning, space value, and space easement is established by extracting the concept of low-altitude access from existing research and regulations, thereby forming a conceptual model. Finally, we conduct an experimental demonstration using a logistics transportation case study in Shenzhen to instantiate the model and achieve 3D visualization. The findings demonstrate that the integrated approach of "space utilization conditions-data modeling-visual expression", implemented around the conceptual model, effectively describes urban low-altitude passage easement and conveys the equitable relationship of urban land space in low-altitude application scenarios, thereby providing valuable support for urban low-altitude management.

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.

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.

This paper addresses the challenge of discovering spatio-temporally associated vehicles involved in crimes using Automatic Number Plate Recognition (ANPR) data, which is a crucial resource in public security work for obtaining vehicle trajectories. The significance of identifying associated vehicles in the context of group-crime prevention and control is emphasized. Practical experiences reveal that criminal groups often adopt subjective strategies to avoid suspicion, leading to unique spatio-temporal association patterns such as intentional long-distance following, which differ from traditional accompanying relationships and are difficult to detect with existing methods. Oriented to the actual needs of public security work, from the perspective of group-crime, to tackle this issue, the paper first analyzes the travel patterns of criminal group vehicles and categorizes them into three main spatio-temporal association modes: close-following mode, intentional long-distance following mode, and alternative-route mode. These modes reflect the different strategies used by criminals to avoid detection, ranging from maintaining close proximity to the peer vehicle to deliberately choosing different routes. Based on these patterns, the paper develops a data model using ANPR data. The study introduces spatio-temporal constraint parameters to better capture the association relationships between vehicles. These parameters include the monitoring point time constraint (Δt_i), point accompanying number (Num_Wx), continuous point accompanying number (Con_Num_Wx), intermittent accompanying distance (d), and accompanying duration (δt).The proposed method for discovering spatio-temporally associated vehicles leverages these parameters to identify potential criminal associations. The methodology involves preprocessing ANPR data to obtain vehicle trajectories, extracting candidate accompanying vehicle sets, calculating spatio-temporal constraint parameters for each candidate vehicle, and setting thresholds for these parameters to discover associated vehicles containing different spatio-temporal patterns. Finally, taking city B as an example, the relevant ANPR data of group-crimes vehicles are used for test and analysis, and the spatio-temporal constraint parameter thresholds are quantitatively evaluated based on the historical data of group-crime cases, based on which the spatio-temporal correlation vehicle analysis of a typical case is conducted, and when comparing this paper's method with the two methods of frequent sequence mining and calculating the concomitant probability, the effectiveness of this paper's method can reach up to 87.59% on average, which is better than the the comparison methods. The results show that the method can effectively identify vehicles engaged in long-distance following and alternative-route strategies, which are often missed by traditional methods. As a result, it is able to quickly target those involved in the crime and further narrow the scope of investigation. In conclusion, the paper presents a comprehensive method for discovering spatio-temporally associated vehicles using ANPR data, significantly enhancing the ability to detect vehicles with complex association patterns. This method not only broadens the application scope of spatio-temporal association discovery but also provides new insights and technical support for public security departments in addressing group-crimes.

Road selection has always been a significant research aspect of cartographic generalization, which is of great significance for spatial data linkage updating and multi-scale representation. The existing selection methods mainly include those based on stroke, semantic information, graph theory, road density, and artificial intelligence, but they only consider the features of a single level selection unit. Therefore, this paper proposes an automatic road selection method that integrates road segment and stroke features. Firstly, the road segment and stroke are used as basic units to construct a dual graph representing the spatial structure of the road network. Based on this, feature calculations are performed: length, degree, closeness centrality, betweenness centrality, and hierarchy are considered as road segment features, while length, the number of containing road segments, and the number of connections of road segment under the same stroke are regarded as stroke features. These stroke features are then integrated into the corresponding road segment unit. The obtained feature matrix is input into the GraphSAGE model for learning, which outputs the classification result of road segment. Finally, a method that increases the minimum number of nodes while considering stroke coherence is utilized to maintain the connectivity of the road network, thereby completing the road selection. Experiments were conducted using 1:250 000 and 1:500 000 scale road network data from Zhengzhou, Henan Province. The results indicate that the proposed method effectively integrates the features of road segments and strokes, overcoming the limitations of using a single road segment or stroke as the selection unit. Compared to the method in reference 17 and the comparative methods that use a road segment or stroke as the selection unit, the model's prediction accuracy improved by 6.36%, 7.36% and 3.13%, respectively. The results processed by the proposed connectivity preservation algorithm were more in line with the cognitive rules of road selection and could further improve the accuracy of selection. After connectivity processing, the proposed method improved the consistent road length by 125.45 km and 110.438 km, and the proportion of consistent road numbers by 8.72% and 20.43%, respectively, while better maintaining the overall and local key structures and density distribution of the road network. Compared with existing road selection methods, this method can better utilize multi-level road features to improve the effectiveness of road selection, providing a new approach for subsequent research in areas such as cartographic generalization and linkage updating.

Facility location is of great significance for improving residents’ quality of life, and geographic accessibility indicators, such as the road network, are often used as the main decision-making factors. Clustering analysis based on geographic accessibility is an important tool for solving such problems. However, existing clustering algorithms often fail to guarantee the accuracy of clustering results, the accessibility of cluster centers, or the selectivity of cluster centers, making them less effective in solving the facility location problem in real scenarios. This paper proposes a Fuzzy C-Means clustering algorithm based on Reachable Distance (FCM-RD), which modifies the objective function, the membership function, and the cluster center function of the classical FCM. It employs reachable distance as a measure of geographic reachable similarity and iterates the cluster centers during the clustering process. Specifically, to capture the true relationships and connectivity between different elements, FCM-RD takes into account physical and spatial barriers, employs the shortest path distance along the road network as the reachable distance, and aligns geographic coordinates with the road network. It is possible for one position on the road network to correspond to multiple positions in geographic coordinates. Consequently, when multiple candidate positions for cluster centers are obtained, a cluster center correction mechanism is designed to iterate the accessible cluster center with reachable distance during the clustering process. Mathematical analysis and experiments in actual scenarios both show the validity of the cluster center iteration mechanism, showing the selected cluster centers in each iteration of FCM-RD are the unique and minimum value points of the intra-cluster objective function. The rationality of FCM-RD is further verified through experiments, and it is compared with baseline algorithms from three aspects: experimental results, convergence, and performance. The results indicate that, compared to the baseline algorithms, FCM- RD improves performance on both the mean and maximum indicators of the shortest reachable distance, with some indicators even improving by up to 38.9%. In a few experiments, there are slight improvements in the DB index and silhouette coefficient indicators, and 100% of the cluster centers selected by FCM- RD are located on the road network. FCM- RD overcomes the shortcomings of ignoring geographical obstacles and unreachable cluster centers. In conclusion, FCM-RD not only obtains accessible cluster centers without location restrictions but also achieves better clustering results. FCM-RD provides an effective and precise solution for geographical spatial clustering in practical scenarios.

[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.

[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.

[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.

Due to the coupling effects of climatic conditions, surface and subsurface conditions, and human activities, soil moisture is highly heterogeneous on spatial and temporal scales. The SMAP soil moisture products from satellite microwave remote sensing can be used from continental to global scales, but they are not suitable for small- and medium-scale applications due to low spatial resolution. To improve the spatial resolution of soil moisture products, various downscaling methods have been developed, with the empirical downscaling method being widely used due to its relatively simple calculation. These models require downscaling factors, which are mostly obtained based on optical remote sensing and are susceptible to cloud influence. Therefore, it is impossible to obtain high spatial resolution soil moisture continuously over time using this model for downscaling. To solve this problem, we proposed a downscaling framework based on multiple data sources using machine learning and deep learning methods. The main idea is to use traditional machine learning methods in the absence of clouds and super-resolution methods to downscale soil moisture in the presence of clouds. The combination of these two methods yields time-continuous, high-resolution soil moisture. First, multi-source data were used to obtain fifteen downscaling factors, including surface temperature, normalized vegetation index, albedo, elevation, slope, slope direction, soil cover type, soil texture, etc. Then, three machine learning models (Random Forest, LightGBM, and XGBoost) were used to establish empirical downscaling models of SMAP soil moisture product data with downscaling factors. The best performing XGBoost model was chosen to downscale the spatial resolution of SMAP soil moisture products from 9 km to 1 km. Finally, the DSCGAN super-resolution model was trained based on 9 km and 1 km soil moisture data pairs. The trained models were used to obtain spatio-temporally continuous soil moisture data for the study area. The results show that, by comparing the downscaled soil moisture and original SMAP data, the R is 0.96, the RMSE is 0.034 m³/m³, the bias is 0.011 m³/m³, and the ubRMSE is 0.034 m³/m³. The comparison between the downscaled soil moisture and the measured data shows that the R is 0.696, the RMSE is 0.192 m³/m, the bias is -0.171 m³/m³, and the ubRMSE is 0.089 m³/m³. The downscaling method proposed in this study provides a framework for generating higher resolution spatio-temporally continuous surface soil moisture that can meet the needs of small-scale applications such as regional moisture surveys and agricultural drought monitoring.

In spatial analysis and modeling of urban crime, the spatial autocorrelation of model residuals poses an significant obstacle to model parameter estimation and produces deviations in analysis of the determinants of urban crime. The presence of significant spatial autocorrelation of model residuals and overdispersion of the model could lead to biased estimates and misleading inferences, even resulting in wrong conclusions. This study employed a new spatial regression method, namely Poisson regression with Eigenvector Spatial Filtering, to solve the problem of model residual spatial autocorrelation and model overdispersion to avoid subsequent biased estimation in model results. To explain the spatial variation of urban crime, we used two theories in spatial crime analysis: crime pattern theory and social disorganization theory. The case study focused on the main urban area of the Haining city in Zhejiang province, China, and the crime data that we used were larceny-theft over a four-year period, from January 2018 to September 2021. Other datasets that we employed for generating covariates included POI data of various facilities in Haining, the Luojia 1-01 nighttime light data, and the WorldPop global population data. We established a Poisson regression model with eigenvector spatial filtering and further identified several important determinants of larceny-theft with unbiased model parameters. The major findings are as follows: (1) The Poisson regression with eigenvector spatial filtering identified the spatial autocorrelation of model residuals, ensuring no significant spatial autocorrelation issue in model residuals. This can improve the model's goodness of fit, correct model parameter estimation, alleviate the impact of overdispersion, and retrieve omitted variables. More importantly, the eigenvector spatial filtering method could be applied to other generalized linear models such as Poisson regression; (2) The results of Emerging Hot Spot Analysis showed that the absolute number of larceny-theft decreased during the period of COVID-19 pandemic, and crime hot spots occurred in the central places of the main urban area of Haining while the cold spots exhibited a trend of multipoint distribution; (3) The level of relative deprivation measured by per capita nighttime light had a significant impact on larceny-theft in the unbiased model with eigenvector spatial filtering; (4) The crime generator, attractor and enabler in various built environment of interest had a significant impact on larceny-theft. The inconsistencies with the conclusions of previous studies were also discussed.

Tree shade is an important resources for mitigating the effects of extreme heat in urban areas. Quantifying the extent of tree shade resources can assist in the prediction and risk assessment of high temperatures in cities. Among the existing methods for estimating tree shade resources, the measured method is time-consuming and ineffective, while the image identification method is difficult to accurately respond to the spatial and temporal changes of tree shade. In this paper, a method was proposed for simulating and quantifying tree shade based on a three-dimensional(3D) scene. We simulated the urban street scene by employing 3D reconstruction technology, distinguished different geographic entity models, utilising the sun's geometric position parameter and construct the corresponding lighting environment, and the shade in 3D scene was simulated according to the principle of linear propagation of light and shadow. The formation of tree shade is determined through the use of a ray intersection algorithm, which allows for the differentiation of sun rays within a 3D model of the shading situation. This process enables the generation and classification of tree shade, which can then be distinguished from shadows cast by their features. The attributes of tree shade (e.g., shade area and shade coverage duration facilitates)can be quantified and visualized in the 3D scene for intuitive representation. A comparison and verification of the shadows taken by the Unmanned Aerial Vehicle(UAV). The results of relative error range from 3.35% to 13.27%, with an average relative error of 9.29%. This method is potential for the estimation of shade tree resources. In addition, a case of shade resources of trees in an urban street scene was simulated and quantified, taking into account their spatial orientation, species and life cycle. The method enables the simulation of the spatial and temporal distribution of shadow resources for real and virtual scenarios (both future and planned) at any given moment. It can be classified and counted, thereby providing the potential service for urban planning and management, as well as fundamental data for the analysis of the cooling effects of urban trees.

In high-resolution remote sensing images, challenges such as blurred visual features of objects and different spectra for the same object arise. Segmenting similar ground objects and shaded ground objects in a single mode is difficult. Therefore, this paper proposes a remote sensing image segmentation model based on multi-modal feature extraction and hierarchical perception. The proposed model introduces a multi-modal feature extraction module to capture feature information from different modalities. Using the complementary information of IRRG and DSM, accurate pixel positions in the feature map are obtained, improving semantic segmentation of high-resolution remote sensing images. The coordinate attention mechanism fully fuses the features from different modalities to address issues of blurred visual features and different object spectra during image segmentation. The abstract feature extraction module uses MobileNetV3 with dual-path bottleneck blocks as the backbone network, reducing the number of parameters while maintaining model accuracy. The hierarchical perception network is introduced to extract deep abstract features, and the attention mechanism is improved by embedding scene perception of pixels. Leveraging the inherent spatial correlation of ground objects in remote sensing images, efficient and accurate class-level context modeling is achieved, minimizing excessive background noise interference and significantly improving the semantic segmentation performance. In the decoding module, the model uses multi-scale aggregation dual fusion for feature recovery, strengthening the connection between the encoder and the decoder. This combines low-level features with high-level abstract semantic features, enabling effective spatial and detailed feature fusion. Progressive upsampling is used for feature recovery, resolving the issue of blurred visual features and improving segmentation accuracy. Based on high-resolution remote sensing images from the ISPRS Vaihingen and Potsdam datasets, the experimental results demonstrate that MFEHPNet outperforms a series of comparison models, including C3Net, AMM-FuseNet, MMFNet, CMFet, CIMFNet, and EDGNet, across various performance indicators. In the ISPRS Vaihingen and Potsdam datasets, MFEHPNet achieves an overall accuracy of 92.21% and 93.45%, an average intersection ratio of 83.24 % and 83.94 %, and a Kappa coefficient of 0.85. The frequency-weighted intersection ratio is 89.24 % and 90.12%, respectively, significantly improving the semantic segmentation performance of remote sensing images and effectively addressing the issues of blurred feature boundaries and different spectra during segmentation.

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.

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.

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.

Construction waste is an inevitable byproduct of urban renewal processes, causing serious environmental pollution and ecological pressure. Precisely quantifying the annual production of urban construction waste and the resource conversion rate is crucial for assessing the cost of urban renewal. Traditional manual methods of estimating construction waste production rely heavily on statistical data and historical experience, which are inflexible, time-consuming, and labor-intensive in practical application, and need improvement in terms of accuracy and timeliness. Existing deep learning models have relatively poor capabilities in extracting and integrating small targets and multi-scale features, making it difficult to handle irregular shapes and fragmented detection areas. This paper proposes a Multi-Scale Feature Fusion and Attention-Enhanced Network (MS-FF-AENet) based on High-resolution Remote Sensing Images (HRSIs) to dynamically track and detect changes in buildings and construction waste disposal sites. This paper introduces a novel encoder-decoder structure, utilizing ResNet-101 to extract deeper features to enhance classification accuracy and effectively mitigate the gradient vanishing problem caused by increasing the depth of convolutional neural networks. The Depthwise Separable-Atrous Spatial Pyramid Pooling (DS-ASPP) with different dilation rates is constructed to address insufficient receptive fields, resolving the issue of discontinuous holes when extracting large targets. The Dual Attention Mechanism Module (DAMM) is employed to better preserve spatial details, enriching feature extraction. In the decoder, Multi-Scale Feature Fusion (MS-FF) is utilized to capture contextual information, integrating shallow and intermediate features of the backbone network, thereby enhancing extraction capabilities in complex scenes. MS-FF-AENet is employed to extract and analyze changes in building areas at different time periods, calculating the engineering waste from new constructions and demolition waste from demolished buildings, thereby obtaining the annual production of urban construction waste. Furthermore, MS-FF-AENet is utilized to extract construction waste disposal sites at different time periods, estimating the amount of construction waste landfill based on changes in landfill waste, indirectly assessing the resource conversion rate of urban construction waste. Based on HRSIs of Changping District, Beijing from 2019 to 2020, experimental results demonstrate: (1) Among a series of baseline models including UNet, SegNet, PSPNet, DeepLabV3+, DSAT-Net、ConvLSR-Net and SDSC-UNet, MS-FF-AENet exhibits advantages in terms of precision and efficiency in extracting buildings and construction waste; (2) During the period from 2019 to 2020, the annual production of construction waste in the study area due to urban renewal is approximately 4 101 156.5 tons, with approximately 2 251 855.872 tons being landfill waste and approximately 1 849, 300.628 tons being resource conversion waste, resulting in a construction waste resource conversion rate of 45.09%, further corroborating government statistical reports. This paper provides a convenient and effective analysis approach for accurate measurement of the cost of urban renewal.

In the transition of urban taxi fleets toward electrification in large cities, the charging demand of taxis exhibits characteristics of high charging load and strong spatiotemporal randomness, with a noticeable mismatch between charging supply and demand in time and space. To accurately estimate the potential charging demand post- the full electrification of the taxi fleet, this study introduces a bottom-up conceptual framework and a binary tree algorithm based on trajectory map matching, leveraging fuel taxi trajectory data exclusively. This approach provides a new paradigm for estimating charging demands in regions lagging behind in the electrification transition. The model and algorithm are validated using trajectory data from 890 electric taxis with battery status fields (State of Charge, SOC). Results show that the estimation errors of indicators such as the number of charging segments and charging amount are less than 6.5%. Moreover, the model also exhibits high spatiotemporal distribution estimation accuracy under different parameter settings (battery depletion threshold θ) and spatial scales (with grid sizes of 500 m, 1 000 m, and 10 000 m), ensuring their applicability in real-world scenarios. Specifically, the temporal distribution error of charging amount is less than 8.5% in the best-case scenario, and over half of the charging amount within 500 m grids has a spatial distribution error less than 0.3, with 59% of the 500 m grids having an estimated error of charging segment count less than 0.3. Building upon this, the Singular Value Decomposition (SVD) algorithm is used to decompose and reduce the dimensionality of the spatiotemporal matrix of charging demands, identifying spatiotemporal patterns of potential charging demands within the real road network of Beijing's Sixth Ring Road at road level. Finally, a case study is conducted using trajectory data from 1 913 taxis in Beijing over three consecutive days from March 9th (Monday) to March 11th (Wednesday) in 2019, and the results indicate that the spatial distribution of potential charging demands for taxis in Beijing exhibits prominent clustering features in key areas and critical corridors, corresponding to high-density charging demands associated with residents' high activity levels and long-distance travel. The decomposed charging demands reveal a spatiotemporal structural pattern dominated by regular charging demands, with supplementary heterogeneity in charging demands between morning and afternoon, as well as during working and non-working hours. This analysis method assists in uncovering the spatial distribution structural characteristics of potential charging demand and spatiotemporal coupling relationships, providing decision-making references for long-term planning of charging infrastructure, grid load scheduling, and charging demand management in the electrification transformation of taxi fleets.

Scientific knowledge of the spatio-temporal evolution processes and formation mechanisms of territorial space in countries along the Central Asia-West Asia Economic Corridor holds significant scientific value and practical importance for supporting the current "Going Global" strategy and the "Belt and Road" initiative. Based on the dominant functions of the territories, the Central Asia-West Asia Economic Corridor is divided into three major types of territorial space: urban and rural construction, agricultural production, and ecological protection. A long-term analysis base map of territorial space from 2002 to 2022 was constructed by integrating multi-source spatio-temporal data. The spatio-temporal cube model was employed to depict the spatio-temporal evolution processes and typical patterns, while the integrated spatial transformation intensity model analyzed the characteristics of spatial structural transformation across three dimensions: scale, location, and intensity. The VIVI-SHAP framework of an interpretable machine learning model was used to analyze the evolution mechanisms, focusing on the importance of driving factors, interaction intensity, and non-linear dependencies. The results show that: (1) Approximately 6.14% of the territorial space in countries along the corridor underwent structural transformation over the past 20 years. The proportion of urban and rural construction space, though small, increased steadily by 0.17%, while agricultural production space decreased by 19.04% overall, with significant structural changes within the ecological protection space. (2) The dynamic interchange between green and other ecological spaces within the ecological protection space is predominant, with a systematic tendency for green ecological space to convert into agricultural production space, while the main source of urban and rural construction space expansion was green ecological space, accounting for 56.36% of the total converted area. 3) The territorial spatial pattern of the corridor is shaped by multiple processes of territorial space transformation, each with different magnitudes, intensities, and driving mechanisms. Natural geographic factors and transportation location factors played decisive roles, while the global influence of population growth and socio-economic development on territorial space structural transformation was less pronounced. This study provides new perspectives and methods to reveal the patterns and mechanisms of changes in land spatial types in the Central Asia-West Asia region. It further provides data support for decision-making departments to formulate reasonable land spatial planning, and demonstrates its application value in achieving greater spatial comprehensive benefits and promoting coordinated regional economic development.

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.

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.