The new smart city is an inevitable requirement for the development of urban digitalization to intelligence and further to wisdom, and is an important part of achieving high-quality development. This paper first introduces the background and basic concept of smart city, and analyzes the relationship and difference between the three stages of digital city, smart city and new smart city. Digital cities use computer networks, spatial information and virtual reality to digitize urban information, and focus on building information infrastructure. Smart cities, on the other hand, use spatio-temporal big data, cloud computing, and the Internet of Things to integrate systems across urban life, emphasizing intelligent management through a unified digital platform. New smart cities combine technologies such as digital twins, blockchain, and the meta-universe for citywide integration, and employ AI-based intelligent lifeforms for decision-making, blending real and virtual elements for advanced city management. This paper then explores the construction of new smart cities, focusing on high-quality urban development driven by technology and societal needs. It highlights the transition from digital to smart cities, emphasizing the role of information infrastructure and intelligent technology in this evolution. The paper discusses key technologies such as 3D urban modeling, digital twins, and the metaverse, and details their impact on urban planning and governance. It also examines how smart cities contribute to economic growth, meet national needs, and ensure public health and safety. The integration of technologies such as AI, IoT, and blockchain is shown to be critical to creating connected, efficient, and sustainable urban environments. The paper concludes by assessing the role of smart cities in measuring economic development, demonstrating their potential as a benchmark for national progress. Finally, based on the latest advances in AI technology, this paper analyzes and systematically looks forward to the key role AI can play in building new smart cities. AI's ability to analyze massive amounts of data, improve decision-making, and integrate various urban systems all provide important support for realizing the vision of a truly smart city ecosystem. With the synergy of "AI + IoT", "AI + Big Data", "AI + Big Models", and "AI + High Computing Power", the new smart cities are expected to achieve an unparalleled level of urban intelligence and ultimately a high quality of sustainable, efficient, and people-centered urban development.

The assurance of a consistent supply of daily necessities in megacities is pivotal in fortifying community supply resilience. It is axiomatic that a community system is not an insular entity; rather, it intricately intertwines with various elements of urban systems. As a foundational unit of urban governance, the urban community is instrumental in facilitating a congruent nexus between supply and demand, thereby augmenting urban resilience. This study proposes an exploratory evaluation method for the urban community supply support and resilience based on complex network theory, attempting to achieve a breakthrough in the underlying theoretical framework of resilience assessment from "single system assessment" to "multi-system correlation assessment". Taking the six districts in the central city of Guangzhou as an example, we build a supply-demand network based on citizens' spatio-temporal behaviors using multi-source data such as mobile phone signaling data and other data. The attacking strategies of network are based on five community resilience indicators. Besides, the cascade failure mechanism is introduced to evaluate the network resilience, and the entropy-weighted method is employed to obtain resilience evaluation results. The influence mechanism of community resilience on the supply system is further analyzed by studying the factors affecting community node failure at different stages of supply network. The findings are as follows: (1) The proposed evaluation model of the community supply support and resilience can effectively simulate urban community supply-demand networks and evaluate the resilience of communities. Low-resilience communities are mainly categorized into three spatial types: old blocks, urban villages, and suburban blocks; (2) Through the analysis of network resilience under five different attack strategies, it is found that the dominant influencing factors are different, with the population density being the primary factor; (3) There exists a complex bidirectional relationship between community resilience and supply security, including the obvious vulnerability of low-resilience communities. And the community self-organization ability, the supply facility layout, and the linkage scheduling between supply points all affect the overall community resilience.

Geographic Information Science (GIS) is not only the demand for the development of the discipline itself, but also the technical method to support the exploration of the frontiers of geography, earth system science and future geography, and the supporting technology to serve the national strategy and social development. In view of the intrinsic law of the development of geographic information science, the extrinsic drive of the development of related disciplines, and the pull of new technologies such as Artificial Intelligence (AI), this paper firstly analyses the development process of GIS and explores its development law from six dimensions, such as description content, expression dimension, expression mode, analysis method and service mode, etc.; then, on the basis of interpreting the original intention and goal of the development of geography, a geography discipline system oriented to the "physical-humanistic-informational" triadic world is proposed, the research object of information geography is discussed, and a conceptual model integrating the seven elements of information and seven dimensions of geographic descriptions is put forward; then, the development trend of geographic information science is analysed from three aspects, including geography from the perspective of information science, information geography from the perspective of geography, and geo-linguistics from the perspective of linguistics, information geography from the perspective of geography, and geolinguistics from the perspective of linguistics, the development trend of geographic information discipline is analysed. Finally, the paper summarises the possible directions and points of development of GIS, geography in the information age, geo-scenario, and geo-big model. We hope that our work can contribute to enriching the understanding of geographic information disciplines, promoting the development of geographic information related sciences, and enhancing the ability of the discipline to support national development needs and serve society.

Nowadays, cities have emerged as one of the core elements for the sustainable development of human society. This also aligns well with the United Nations Sustainable Development Goals on sustainable cities. The pivotal role of cities is also demonstrated by the rapid development of big data and artificial intelligence technologies. There have been more and more studies dedicated to the realm of data-driven urban sustainability, in which the complex processes of urban sustainable development, encompassing social, economic, and ecological dimensions, are monitored, interpreted, and evaluated through massive urban data from multiple sources. However, a common limitation is that most existing studies concentrate on individual application scenarios and singular data sources and ignore the intricate interconnections among diverse urban data sources and multiple urban elements, making it challenging to explore findings across diverse urban sustainability contexts. Therefore, to address this critical gap, in this paper, we propose a novel approach for urban sustainable development driven by Urban Business Area/Region Knowledge Graph (UKG). This approach incudes two fundamental steps: the construction of a comprehensive ontology for the UKG based on massive multi-source urban data, and the subsequent synthesis of knowledge guided by this ontology to create the UKG. The construction of the UKG ontology captures important elements in cities as well as their complex interconnections, e.g., people, locations, and organizations, and their relationships in terms of spatiality, function, and association. This ontological architecture lays the foundation for the subsequent knowledge fusion, ultimately leading to the construction of UKG. The practical applications of UKG in driving urban sustainability are manifold, ranging from real-time status monitoring and nuanced interpretation of urban phenomena to the holistic evaluation of decisions made for urban sustainability. To verify the effectiveness and efficiency of the proposed approach, the paper introduces a novel cross-modality contrastive learning framework that incorporates semantic knowledge for urban sustainability. The proposed framework includes a semantic encoder and a visual encoder to capture information from UKG and urban images (satellite images and street view images), respectively. Based on the assumption that the semantic representation of UKG entities should be close to their corresponding image representations, the proposed framework successfully incorporate semantic knowledge into visual encoder, which further enhances the predictive capabilities of urban socioeconomic indicators derived from urban images. Through empirical validation, this study demonstrates the real-world applicability and generalizability of the UKG framework for urban sustainability.

Under the background of high-density urban areas and aging population in China, it is not only necessary but also urgent to strengthen the research on the design and construction of urban pocket parks. This paper uses CiteSpace, literature review, technical analysis and some other methods to conduct cluster analysis and comprehensive literature analysis on the study of urban pocket parks in China from 2000 to 2022. The results indicate that the current research hotspots in this field are pocket parks, roadside green space, landscaping, vest-pocket park, public space, landscape architecture, micro green spaces, street green land, design strategy, planning and design, etc. The research progress of pocket parks is divided into three stages: basic research (2000—2006), steady progress (2007—2018), and rapid development (2019—2022). In the basic research stage, the paper mainly studies the basic theories of street green space and vest-pocket park, which are the predecessor of the concept of pocket park, such as the development status at home and abroad, humanized design, and behavioral psychology, which lays a good foundation for the research of pocket park in China. In the stage of steady progress, the concept of pocket park is clearly proposed, the connotation of pocket park is interpreted, and the basic strategy of pocket park planning and landscape design is summarized. In the stage of rapid development, the research perspective turns to more micro aspects such as urban renewal, spatial layout of pocket park in the context of park city, optimization strategy, accessibility, fairness, interactivity, and comprehensive evaluation, etc. The research focus includes basic research, planning and design research, and evaluation research. The basic research has systematically sorted out and summarized the concept and connotation, construction scale, construction types, and usage functions of pocket parks. The planning and design research has extracted design strategies related to pocket parks from aspects such as spatial layout, landscape design, and elderly-oriented design. The evaluation research has evaluated the current situation of pocket parks from three aspects: social benefits, landscape benefits, and spatial structure. The development directions of urban pocket park research in our country in the future include: research on collaborative group layout of multiple pocket parks and optimization of internal spatial layout of a single pocket park, optimization of landscape facility layout, and plant configuration and optimization; research on the adaptability of pocket parks to the elderly, children, accessibility, and humanization according to the behavioral characteristics and psychological needs of residents, based on the theoretical foundations of environmental behavior and environmental psychology; systematically study on the coupling relationship between pocket parks and the natural environmental effects in the area by comprehensively applying architectural environmental theory, Remote Sensing (RS) technology, and Geographic Information System(GIS) technology; normative research on design guidelines, construction, operation and maintenance standard paradigms of pocket parks; research on digitization of pocket parks design and intelligent operation and maintenance management, as well as evaluation system, evaluation method and statistical analysis of pocket parks on this basis.

In the field of intelligent transportation, various information collection devices have produced a massive amount of multi-source heterogeneous data. These data encompass various types of information, including vehicle trajectories, road conditions, and traffic incidents, soured from devices such as traffic cameras, sensors, and GPS. However, the current challenge faced by researchers and practitioners is how to correlate and integrate the massive amount of heterogeneous data to facilitate decision support. To address this challenge, knowledge graph technology, with its powerful entity-to-entity modeling ability, has shown great potential in knowledge mining, representation, management, and reasoning, making it well-suited for intelligent transportation applications. In this paper, we first review the construction techniques for geographic traffic graphs, multimodal knowledge graphs, and dynamic knowledge graphs, demonstrating the broad applicability of knowledge graphs in the field of intelligent transportation. Secondly, we summarize relevant algorithms of multi-modal knowledge graph representation learning and discuss dynamic knowledge graph representation learning in the field of intelligent transportation. Knowledge graph representation learning technology plays a crucial role in creating high-quality knowledge graphs by capturing and organizing the relationships between entities and their attributes within the transportation domain. This technology utilizes advanced machine learning algorithms to analyze and process the heterogeneous data from various sources to extract meaningful patterns and structures. We also introduce the completion technology and causal reasoning technology in dynamic transportation multi-modal knowledge graph, which is useful for improving the data of intelligent transportation systems. Comprehension ability and decision-making reasoning level have important theoretical significance and practical application prospects. Thirdly, we summarize the solutions of knowledge graph that provide important support for intelligent decision-making in several application scenarios. The utilization of knowledge graphs in intelligent transportation systems facilitates real-time data integration and enables correlation analysis of diverse data sources to provide a holistic view of the traffic ecosystem. This comprehensive understanding empowers decision-makers to implement targeted interventions and proactive measures, ultimately mitigating traffic congestion and reducing the occurrence of accidents. Through the continuous refinement and enrichment of the traffic knowledge graph, the intelligent transportation system can adapt and evolve to address emerging challenges and optimize transport networks for enhanced efficiency and safety. Finally, we analyze and discuss the existing technical bottlenecks. The future of traffic knowledge graphs and their auxiliary applications are also prospected and discussed, highlighting the potential impact of this important technology on intelligent transportation systems.

As Artificial Intelligence Generated Content(AIGC) rapidly advances, various disciplines are shifting toward AI-driven scientific research. GeoAI technology, which focuses on geographic spatial intelligence, has the potential to outperform traditional methods in solving cartographic tasks. This shift presents both new opportunities and challenges for cartography. Despite some progress in integrating AI into cartographic research, limitations in computational power and other factors have hindered significant success in the past. As we enter the era of intelligence, both humans and machines will play critical roles in map creation and interpretation. Through artificial intelligence algorithms, maps can be produced quickly, at low cost, and on a large scale. However, there are also issues such as the instability of the quality of map works. The generation of map content has gone through the stages of expert-generated content and user-generated content and is developing towards the stage of artificial intelligence-generated content. In the traditional map-making phase, professional maps are produced by cartographic experts. While the quality of these maps is assured, the number of experts is limited. Consequently, the production cycle is long, the cost is high, the quantity of map products is limited, and they have not been produced on a large scale. At the current stage, generative artificial intelligence can produce map content in three forms: text-to-map (txt2map), map-to-text explanation (map2txt), and map style transfer (map2map). People can already use ChatGPT to generate maps by entering a piece of text, produce a textual explanation of a map by uploading an image of the map to ChatGPT, and even achieve map style transfer from images using Generative Adversarial Networks (GANs). The integration of artificial intelligence with the map transmission model has derived an intelligent map transmission model. It includes four stages: (1) Intelligent acquisition of mapping information: Sampling and collecting information about the real-world geographical environment through artificial intelligence methods, which is then processed and filtered into structured information for mapping; (2) Intelligent mapping: The process of intelligently generating maps through the use of colors, symbols, grading, and other representational methods based on mapping information; (3) Intelligent map reading: The process by which readers use artificial intelligence methods, combined with map language, domain knowledge, and personal understanding, to recognize the real world; (4) Intelligent interpretation of map information: Using artificial intelligence to interpret maps, thereby gaining cognition and understanding of the real world. Although progress has been made, research on using intelligent methods to address cartographic challenges is still in its early stages. Challenges include the lack of comprehensive training datasets, limited model algorithm generalization, and interpretability. These areas offer promising directions for future development.

Human mobility data play a crucial role in many real-world applications such as infectious diseases, transportation, and public safety. The development of modern Information and Communication Technologies (ICT) has made it easier to collect large-scale individual-level human mobility data, however, the availability and usability of the raw data are still significantly limited due to privacy concerns, as well as issues of data redundancy, missing, and noise. Generating synthetic human mobility data through modeling approaches to statistically approximate the real data is a promising solution. From the data perspective, the generated human mobility data can serve as a substitute for real data, mitigating concerns about personal privacy and data security, and enhance the low-quality real data. From the modeling perspective, the constructed models for human mobility data generation can be used for scenario simulations and mechanism exploration. The human mobility data generation tasks include individual trajectory data generation and collective mobility data generation, and the research methods primarily consist of mechanistic models and machine learning models. This article firstly provides a systematic review of the research progress in human mobility data generation and then summarizes its development trends and challenges. It can be observed that mechanistic-model-based methods are predominantly studied in the field of statistical physics, while machine-learning-based methods are primarily studied in the field of computer science. Although the two types of models have complementary advantages, they are still developing independently. The article suggests that future research in human mobility data generation should focus on: 1) exploring and revealing the underlying mechanisms of human mobility behavior from a multidisciplinary perspective; 2) designing hybrid approaches by coupling machine learning and mechanistic models; 3) leveraging cutting-edge generative Artificial Intelligence (AI) and Large Language Model (LLM) technologies; 4) improving the models' spatial generalization and transfer-learning capabilities; 5) controlling the costs of model training and implementation; and 6) designing reasonable evaluation metrics and balancing data utility with privacy-preserving effectiveness. The article asserts that human mobility processes are typical phenomenon of human-environment interactions. On the one hand, research in Geographic Information Science (GIS) field should integrate with theories and technologies from other disciplines such as computer science, statistical physics, complexity science, transportation, and others. While on the other hand, research in GIS field should harness the unique characteristics of GIS by explicitly incorporating geographic spatial effects, including spatial dependency, distance decay, spatial heterogeneity, scale, and more into the modeling process to enhance the rationality and performance of the human mobility data generation models.

The converting evolution of cascading disaster scenario refers to that in the process of disaster scenario evolution, the disaster bearing bodies transform into new disaster hazards, forming a disaster chain. Rainstorm can easily cause serious secondary disasters such as waterlogging, debris flow and flood, and the combination of these secondary disasters will make the city more vulnerable. However, existing research on rainstorm cascading scenario deduction lacks the analysis of specific scenario evolution situations such as multi disaster combination, scenario element converting, and human-induced emergencies. Meanwhile, traditional research often relies on the probability inference based on existing scenario evolution networks, without providing a construction method for scenario evolution networks, making it difficult to adapt to the knowledge requirements of actual scenario situation converting deduction. To address the scenario converting evolution problems of urban rainstorm cascading disasters, this paper proposes a scenario converting deduction method for rainstorm cascading disaster response based on multi-source spatial data and probability analysis tools. First, based on local and non-local historical emergency cases, the scenario elements involved in the rainstorm cascading disaster scenarios and their potential converting paths are identified. Next, with the support of Baidu Encyclopedia and Wikipedia network knowledge resources, relevant scenario element features and their associations are extracted, and a Group Lasso machine learning method is adopted to achieve feature selection of involved scenario elements. Then, considering the multi-stage and complex scenario correlation in the process of cascading scenario evolution, a dynamic Bayesian network model for scenario converting deduction is constructed. Finally, a Markov chain Monte Carlo method is used to solve the Bayesian network and generate the converting probabilities. The proposed method is applied to the rainstorm response practice of Wuhan High-tech Zone. The use case results show that the proposed method can combine historical cases and network data to achieve rapid and effective generation of key scenario elements and their features, helping to improve the reliability of scenario converting deduction. At the same time, the proposed method supports the scenario converting deduction of small-scale disaster-bearing bodies such as geographic grids, which helps to provide more accurate rainstorm emergency decision-making support and provide good performance in visual analysis. The uncertainty analysis of the proposed method shows that the precision of original probabilities of key scenario element features and the size of generated geographic grids significantly affect the scenario converting deduction results. These findings provide important information for the local area and are expected to help the rainstorm disaster management of other jurisdictions.

Urban areas often suffer from varying degrees of land surface deformation due to infrastructure construction and resources exploitation, which threatens the safety of residents' lives and property. So regular monitoring of urban surface deformation is of great significance for preventing related geological disasters. However, urban surface deformation has the characteristics of small-scale and continuous-slow change, it is necessary to process the error carefully in order to improve the monitoring accuracy. This paper proposes a high-precision surface deformation extraction method combining the principal component spatiotemporal analysis and time-series Interferometric Synthetic Aperture Radar (InSAR). Through the mining and analysis of time-series InSAR signals, a surface deformation model combined with polynomial functions is constructed to realize the hierarchical estimation of error and noise signals. Then the high-precision, small-scale surface deformation information is extracted. Taking Xuzhou, a typical city prone to geological disasters, as the research area, the results show that the proposed method can accurately separate the surface deformation information and error in the time-series InSAR signal, and the deformation monitoring accuracy is 10%~57% higher than other existing methods. The deformation rate from 2018 to 2022 is about -17~35 mm/a in Xuzhou, which is mainly distributed in the urban area, along the subway and in the old goaf. In recent 8 years, urban construction has continuously triggered local subsidence areas, the secondary deformation of the old goaf can last for more than 6 years, and the surface of several mining areas is still in an unstable state. The results can provide important technical support and decision support for high-precision monitoring of urban surface deformation and prevention of potential geological disasters.

Streets are an important attraction for urban tourism. Exploring the influence of street landscape color characteristics on tourists' emotional perception holds important reference value for the rational planning and layout of urban street landscape. This study takes the built-up area within the third ring road of Xi'an city as a study case, and employs the Full Convolutional Neural Network (FCN) and Random Forest (RF) algorithms to construct an emotional perception dataset of street images. We use the streetscape images as the basis to extract the color features of the streetscape using machine learning algorithms, and color quantifiers are constructed and spatially visualized; The RF regression algorithm is used to explore the relationship between streetscape color characteristics and tourists' emotional perception, and the optimal color characteristic parameters are derived. The results show that: (1) There is a distinct spatial distribution pattern of tourists' emotional perception. The emotions of beauty and liveness gradually increase from the central area outward, and emotions of safety and wealth emotions score higher in the area within the second ring road outside the main city. While boring emotions score lower in this area, and depressing emotions gradually decrease from the central area outward. This suggests that the spatial distribution pattern of emotional perception shares somewhat homogeneity between tourists' emotional perception in non-routine environment and residents' perception in familiar environment; (2) The color characteristics of the streetscape show a complex non-linear relationship with tourists' emotional perception. For example, color complexity has less effect on emotions of beauty and liveness compared to color coordination and has a greater effect on emotions of boredom, depression, safety, and wealth than color coordination. Moreover, when the value of color complexity is 0.86 and the value of color coordination is 0.84, tourists can obtain better emotional perception across six dimensions; (3) Under non-routine conditions, the more significant the color characteristics of the street landscape, the better the emotional perception of visitors. Theoretically, this study confirms the conclusion that the more colorful environment leads to better experience for tourists; and methodologically, this paper not only expands the traditional text-based and manually-assigned research methods in the field of tourism emotion, but also enriches the application of streetscape big data and machine learning methods in the field of tourism. This study provides a reference for city managers to understand tourists' visual preferences for streetscapes and to optimize streetscape design.

Land surface temperature is one of the important land surface parameters that characterizes the local thermal environment. Unmanned Aerial Vehicle (UAV) thermal infrared remote sensing has the advantage of high spatial resolution, which provides data support for obtaining high-resolution local land surface temperature data. In recent years, how to accurately retrieve the surface temperature based on UAV thermal infrared remote sensing data has attracted great attention. This paper systematically explores the method of retrieving land surface temperature from UAV thermal infrared remote sensing data and synchronized atmospheric vertical profile data. We collected the UAV thermal infrared images and atmospheric vertical profile data simultaneously within the central campus of Nanjing University of Information Science and Technology and its surrounding area using the UAV-based WIRIS Pro Sc thermal imager and temperature and humidity sensor. To obtain the accurate land surface thermal radiance, the atmospheric influence on the UAV thermal infrared images was eliminated by calculating the atmospheric downward thermal radiation, upward thermal radiation, and atmospheric transmittance. Land cover data were generated from UAV multispectral data, and then the land surface emissivity was calculated based on the land cover data and emissivity spectrum library. Finally, the land surface temperature was retrieved based on the land surface thermal radiance and surface emissivity. The retrieved land surface temperature was validated by comparing with the corresponding measured land surface temperature after corrections. We also analyzed the spatial pattern of the UAV land surface temperature and the factors that affect surface temperature retrieval. The results showed that the use of synchronized temperature and humidity profiles can effectively remove atmospheric effects, ensuring accuracy of off-ground radiance measurements under varying water vapor conditions. Our retrieval method can effectively retrieve surface temperature from UAV thermal infrared images. The retrieved land surface temperature achieved a coefficient of determination of 0.91. The difference between the retrieved and observed land surface temperature ranged from 0.06 to 4.96 K, with 55.56% of the samples showing differences less than 2 K. The surface temperature showed obvious spatial variation which was closely related to the type of surface cover. Artificial surfaces such as buildings and roads had relatively high surface temperatures, generally above 325 K. Natural surfaces such as woodlands and grasslands had relatively low surface temperatures, generally not exceeding 310 K. This study provides a valuable reference for retrieving high resolution land surface temperature from UAV-based thermal infrared remote sensing data, and also provides a technological support for local thermal environment monitoring.

With high quality development becoming the primary task of comprehensively building a socialist modernized country, the importance of geographic spatiotemporal information in supporting national and local socio-economic development has been raised to new heights. Based on the urgent need for high-quality development to empower geographic spatiotemporal information, this paper first comprehensively reviews the theoretical and methodological research status of geographic spatiotemporal expression and computation from the perspectives of complex land surface system expression, spatiotemporal uncertainty analysis, and geographic spatial intelligent computing. It is pointed out that there is an urgent need to update concepts, integrate across borders, and innovate technologies to improve the production level of spatiotemporal information products and assist in the high-quality transformation and development of social and economic activities in the three living spaces. Furthermore, driven by the problems of deconstructing complex land surface and analyzing precise parameters, we propose relevant theoretical thinking and research ideas of geographic spatiotemporal digital base (GST-DB) with an overview of basic concepts and technical points. The GST-DB is based on the uniqueness and distribution of time and space, and is proposed by three basic elements around brackets, containers, and engines. The paper focuses on analyzing three key scientific issues, including multiple representations and knowledge association for complex land surface systems, uncertainty analysis of spectral feature reconstruction under spatial form constraints, signal transmission and optimized control with the collaboration of satellite, ground, and human. The three key objectives, namely deconstruction of global space, analyticity of local space, and transferability between spaces, cut into the process of connecting the two-step process of spatial expression and parameter calculation, and further explain the difficulties and feasible solution paths of reliable expression, reliable analysis, and controllable computing. Through the analysis of the solution approach, the feasibility and necessity of the organic synergy of geoscientific analysis ideas, remote sensing mechanism knowledge, and machine intelligence algorithms are demonstrated. On this basis, this paper focuses on the monitoring and supervision of agricultural production as a demand-oriented problem for introducing agricultural application cases of GST-DB. Four types of application models for people, land, money, and things are preliminarily described. By demonstrating the construction process and implementation effectiveness of integrated intelligent computing, the advantages and basic supporting role of the base in carrying and utilizing spatiotemporal data elements are highlighted. This case study demonstrates the potential to provide high-quality spatiotemporal information services for the development of modern agriculture in complex mountain areas.

Coastal megacities are typically situated in low-lying and densely populated areas. The occurrence of storm surge compound flooding has the potential to result in catastrophic social, economic, and ecological impacts for these coastal cities. The rising sea levels and the increased intensity and frequency of tropical cyclones caused by global warming will exacerbate the challenges faced by coastal cities. Therefore, accurately assessing compound flooding events caused by tropical cyclones is critical to protecting coastal areas from inundation. However, research on the impact of climate change on the risk of tropical cyclone induced compound flooding in coastal areas is still limited. In this study, we used the EC-EARTH3P climate model and selected a dataset of climate change tropical cyclone trajectories synthesized by the STORM model. This dataset is generated using historical data from the International Best Track Archive for Climate Stewardship (IBTrACS) to simulate synthetic tropical cyclones under future climate conditions. Subsequently, we used the coupled Delft3D FLOW & WAVE hydrodynamic model to simulate the impact of storm surge compound water levels on coastal areas due to the nonlinear effects of tropical cyclones wind fields and waves. Furthermore, we investigated the contributions of tropical cyclones and sea level rise to coastal storm surge compound flooding under different Shared Socioeconomic Pathways (SSPs) scenarios, taking the Shanghai city, located within an estuary and along the coastline of China, as our case study. The results showed that climate change had a significant impact on storm surge compound flooding. The future compound flooding disasters exhibited spatial variations in shanghai and differences in water level heights, influenced by future cyclone paths and intensities. Among these areas, Chongming district was the most seriously affected area by storm surge compound flooding. In addition, sea level rise under different climate scenarios will lead to more severe flood hazards in the Shanghai area. We found that although sea level rise will further intensify the impact of storm surge compound flooding in Shanghai, tropical cyclones will have a greater influence on future compound flooding in the city. The spatial risk analysis framework for compound flooding hazards under climate change designed in this study can also be applied to research future storm surge compound flooding hazards in other coastal megacities. Our research findings not only provide a foundational basis for policymakers and flood risk managers to identify risk vulnerable areas, but also provide significant implications for coastal adaptation measures and urban emergency response planning.

[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.

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.

Calculation of shape similarity between curves is one of the most fundamental and theoretical problems in cartography, graphics, and geometry. Although existing machine learning methods can be used to calculate curve shape similarity, they often rely on extensive sets of sample curves, leading to a low efficiency. To address this issue, this paper proposes a method for directly calculating shape similarity between simple curves. First, two curves are moved, rotated, and scaled to obtain the optimal position where the mean distance between the two curves is the least. Second, the two curves are divided into a number of subsections based on their intersections of the curves. Third, the shape similarity within each subsection (i.e., two sub-curves) is calculated by the principle of proximity in Gestalt. Finally, the shape similarity of the two curves can be obtained by calculating the weighted shape similarity of all subsections. The proposed method is validated through the psychological experiments, and the results show that the calculated shape similarity aligns with human spatial cognition, indicating its practical applicability in specific scenarios. Moreover, the proposed method not only directly calculates curve shape similarity but also eliminates the reliance on a large number of curve samples, resulting in increased computational efficiency. The method presented in this paper provides a more efficient and direct tool for calculating curve shape similarity and holds promise for applications in various fields such as cartography, graphics, and geometry.

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.

Cyberspace surveying and mapping has become a hot research topic of widespread concern across various fields. Its core task involves surveying the components of cyberspace, analyzing the laws of cyberspace phenomena, and mapping the structure of cyberspace. Research on cyberspace surveying and mapping faces issues such as diverse conceptual terminologies which is lack of unified research frameworks, unclear understanding of elements and laws, non-standardized methods of cyberspace map expression, and the absence of unified standards. Based on systematically reviewing the current status of cyberspace surveying and mapping research across fields, a common understanding of the essence of cyberspace has been analyzed. Starting from the spatial, geographical, and cultural characteristics of cyberspace, the features and advantages of studying and utilizing cyberspace from the perspective of mapping geography are dissected. A research framework for cyberspace surveying and mapping is proposed, focusing on the core content and key technologies of "surveying " and "mapping" in cyberspace, and explaining its relationship with 3D Real Scene, Digital Twins and Metaverse. Cyberspace surveying has been divided into narrow and broad senses, pointing out the lack of holistic measurement of cyberspace features and the lack of research on measuring the phenomena and patterns of human activity in cyberspace. From the perspective of cyberspace cognitive needs, a conceptual model and classification system for cyberspace maps have been proposed. Focusing on the cyberspace coordinate system, "geo-cyber" correlation mapping, and methods of expressing cyberspace maps, the key technologies for creating cyberspace maps are described in detail, and the methods of representing cyberspace maps and their applicability are systematically analyzed. Finally, key scientific questions and critical technologies that need focused research, such as the top-level concepts of cyberspace, cyberspace modeling methods, theories and methods of cyberspace maps, and the design of application scenarios for cyberspace maps, are discussed.

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.

Remote sensing image change detection is a crucial technique that utilizes remote sensing technology to analyze and compare image data captured at different time periods or scenes. In practice, features at varying scales encompass diverse representation ranges, enabling the extraction of more comprehensive and detailed information. This paper proposes a Multi-Scale Cross Dual Attention Network (MSCDAN) method for building change detection in remote sensing images using the multi-scale Cross Dual Attention (CDA) mechanism and residual convolution neural network architecture. The proposed method leverages the characteristics of a residual network to extract change features of different dimensions from remote sensing images. For each feature dimension, a CDA module is created, which utilizes both cross attention and dual attention mechanisms. It combines spatiotemporal information to capture time-series features of surface changes and identifies time-series related change patterns, such as periodic and persistent changes. In this way, the multi-scale CDA module enhances the correlation between different perspectives or feature maps within the input data, which facilitates the exchange and fusion of information in multiple dimensions and enhances the model capability for complex change scenes, leading to improved change detection performance. A Fully Transposed Convolutional Upsampling Module (FTCUM) is introduced to perform local feature fusion for each point in the feature map, and the change boundary is identified by the neural network. This avoids the problems of blurring and jaggedness brought by traditional methods like bilinear interpolation and allows for end-to-end training and optimization, making the method more effective in meeting the requirements of change detection tasks. Extensive experiments are conducted on two benchmark datasets, namely WHU-CD and DSIFN, to evaluate the performance of the proposed method. Compared to the mainstream method, i.e., DTCDSCN (Dual-Task Constrained Deep Siamese Convolutional Network), our proposed method increases the accuracy by 5.13% on the DSIFN dataset and by 1.3% on the WHU-CD dataset. Additionally, for other exiting methods, the proposed method is also better than the ChangeNet and LamboiseNet on the three datasets and outperforms the improved DeepLabv3+ and SRCD-Net on the CDD Dataset. These exceptional findings across various datasets confirm the effectiveness of the proposed method in detecting changes in remote sensing images. Through the application of residual networks and attention mechanisms, our approach achieves superior results in intricate scenarios. This study shows that our proposed method performs remarkably well on various datasets. It serves as a reference for further comprehensive research on remote sensing image change detection using multi-scale cross-pairwise attention networks.

The segmentation of fruit tree canopy based on Unmanned Aerial Vehicle (UAV) visible spectral images is greatly influenced by complex background information such as topographic relief, shrubs, and weeds. Although existing deep neural networks can improve the robustness of canopy segmentation to a certain extent, they ignore the global context and local detailed information of the canopy due to limited receptive field and information interaction, which restricts the improvement of canopy segmentation accuracy. To address these issues, this paper introduces the Canopy Height Model (CHM) and deep learning algorithms, and proposes a fruit tree canopy segmentation method that couples Convolutional Neural Networks (CNN) and Attention Mechanisms (AM) based on UAV photogrammetry. This method first constructs a coupled deep neural network based on CNN and AM through transfer learning to extract both the local and global high-level contextual features of fruit tree canopies. Meanwhile, considering the correlation between deep semantic features and the position information of fruit tree canopies, a local and global feature fusion module is designed to achieve collaborative tree canopy segmentation of attributes and spatial positions. Taking the citrus tree canopy segmentation as an example, the experimental results demonstrate that the use of the CHM can effectively suppress the influence of topographic relief. Our proposed method can also significantly reduce the interference of underlying weeds or shrubs on canopy segmentation, and achieves the highest Overall Accuracy (OA), F1 score, and mean Intersection over Union (mIoU) of 97.57%, 95.49%, and 94.05%, respectively. Compared with other state-of-the-art networks such as SegFormer, SETR_PUP,TransUNet, TransFuse, and CCTNet, the mIoU obtained by the proposed method increases by 1.79%, 8.83%, 1.16%, 1.43%, and 1.85%, respectively. The proposed method can achieve high-precision segmentation of fruit tree canopies with complex background information, which has important practical value for understanding the growth status of fruit trees and fine management of orchards.

The advent of intelligent connected vehicles has seamlessly integrated into the fabric of contemporary intelligent transportation systems, emerging as an indispensable and transformative constituent. At the nucleus of this paradigm shift lies the autonomous driving high definition maps, assuming a pivotal role in propelling the evolution of intelligent transportation. The high definition maps, as a core element in intelligent connected vehicles, stand as a linchpin in advancing the development of intelligent transportation systems. Effectively establishing intricate connections among drivers, vehicles, road environments, driving conditions, significant landmarks, and the broader social environment, high definition maps act as a catalyst, propelling autonomous driving technology from Level 0 to Level 5. This article delves into the urgent imperatives steering the progression of intelligent connected vehicles and the critical role played by autonomous driving high definition maps. Beginning with an exploration of the essence, mainstream foundational data models, concepts, and characteristics of high definition maps, the discussion underscores their transformative role as a groundbreaking map data paradigm, crucial for realizing autonomous driving in intelligent connected vehicles. Subsequently, a nuanced analysis unfolds, dissecting the multifaceted characteristics woven into the entire lifecycle of high definition maps. This comprehensive examination spans diverse perceptual data types, encompassing multiple map construction methodologies, a variety of crowd-sourced updating techniques, various map application methods, the inherent intelligence embedded in map auditing processes, and innovative management modalities. Additionally, a prototypical route for high definition maps crowd-sourced updating technology is proposed, elucidating the dynamic landscape of map data refinement. Addressing the current challenges in high definition maps auditing, the study introduces an online intelligent map auditing methodology, providing a promising avenue to navigate the intricacies of the auditing process. This approach not only addresses key issues but also ensures the precision and reliability of map data. The practical application of these conceptual frameworks is exemplified through an extensive case study of the Shenzhen high definition maps pilot, offering valuable insights derived from practical experiences and explorations. In conclusion, this paper provides a forward-looking perspective on the developmental trajectory of high definition maps. It envisions their sustained significance and potential advancements, anticipating the continuous refinement and innovation in high definition maps. This ongoing evolution is expected to significantly contribute to the further enhancement of intelligent transportation systems and the maturation of autonomous driving technologies. The transformative impact of high definition maps is poised to usher in a new era of seamless and intelligent mobility, reshaping the landscape of contemporary transportation systems.

[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.

The mobile communication technology and social media has been deeply embedded into people's daily life, affecting people's choices of leisure activities. However, there is still limited understanding of the spatial regularity characteristics of its impact, particularly due to the lack of empirical analysis utilizing specific quantitative indicators. Given that the layout of leisure spaces is closely linked to social equity, it is essential to obtain a better understanding of the emerging spatial patterns in order to improve residents' well-being. To address this gap, leisure check-ins on Xiaohongshu (a Chinese social media platform) and leisure Points of Interest (POI) in Dalian are used to measure the characteristics of these two types of leisure spaces in two dimensions: concentration and clustering, and at two scales: the main urban area and subdistricts. Various spatial analysis methods, including kernel density estimation, head/tail breaks, hot spot analysis (Getis-Ord Gi*), and DBSCAN (Density-Based Clustering), are employed to analyze the data. The findings are that: (1) Leisure check-ins are mostly located in the urban central area, with a smaller distribution range and fewer hotspot cores; (2) At both the main urban area and subdistricts scales, the distribution of leisure check-ins exhibits lower concentration and clustering, with obvious "decentralized dispersion" characteristics. However, the degree of significance of these features varies across different subdistricts; (3) The majority hotspots of leisure check-ins are located in traditional hotspots, with a few emerging in expansion of urban central area or regions with unique features, such as historic urban landscape district and marina space; (4) The distribution patterns of leisure check-ins can be grouped into four types based on differences in subdistricts' concentration and clustering ratio: "original center cluster type", "original center scattered type", "new center scattered type", and "no center scattered type". The subdistricts with these different distribution patterns exhibit differences in functionality, location, and other characteristics. This study analyses the behavioral processes of leisure activities under the influence of social media through the lens of Actor-Network-Theory. Based on the fundamental principles of temporal geography and differences between "space of places" and "space of flows", it is argued that social media engenders a novel "local order" of leisure pursuits, marked by a desire for spatial exploration. This new order reflects the impact of "space of flows" based on virtual connections on "space of places" based on physical presence, which strengthens the role of node attractors, reduces the constraints of accessibility at micro scales, and increases the flexibility of location.

Car-sharing services can meet the diverse travel needs of users while helping to alleviate traffic congestion and reduce pollution. In many scenarios, car-sharing is more economical than taxis. One-way car-sharing allows users to rent and return cars at any station within the system, which leads to low operating costs and flexible services. However, the spatiotemporal skewness of user travel demand gives rise to imbalances between vehicle supply and demand among stations, which limits the profitability of car-sharing companies. Relocating vehicles can alleviate the above problems to some extent. Most existing studies construct optimization models with the goal of maximizing expected revenue or reducing system imbalance. The former is limited by the insufficient accuracy of travel demand prediction, and the mode of discarding definite orders and pursuing higher possible expected benefits instead cannot guarantee actual profits. To improve system balance, the latter pays more relocation costs such that reduces the profitability. To this end, we propose a revenue-driven one-way car-sharing user relocation model RUG that is suitable for real-time scenarios. The model is based on the deterministic effect of prospect theory, which ensures the current definite gains. For orders that cannot be fulfilled due to imbalanced resources, RUG provides users with alternative travel routes, which not only attempts for promising gains but also effectively balances the system. Users are incorporated into the system as relocation subjects by designing rational user incentive and acceptance models. Public transportation is utilized to break through the distance limitations of user relocation. Relocation plans are evaluated with a greedy heuristic. Experimental results on real-world New York datasets show that the RUG model has significant advantages over existing user-based relocation methods. Under the same parameters, compared to the representative user-based relocation method, RUG increases service order volume and profit by 14% and 60%, respectively. Notably, RUG can effectively raise unit profits during traffic rush hours. By incorporating travel demand forecasting, the model further increases revenue by 5.4% while also improving user service level and system balance.

The 31st International Cartography Conference (ICC) was held in Cape Town, South Africa from August 13 to August 18 in 2023. This paper first introduced the overview of the 31st ICC, the participation of Chinese experts and enterprise. Secondly, based on the technical reports during the ICC2023, new research hotspots of cartography were analyzed and summarized from eight aspects including the basic theory of cartography and technologies of cartography, map data, map products, and Spatial Data Infrastructure (SDI) construction, public applications, sustainable development applications and historical and cultural ethics. We concluded some obvious hotspots that the traditional mapping fields have paid more attention to multi-element fusion mapping, user and scenario experience enhancement and rapid mapping capabilities; On the other hand, the emerging geographical information fields have focused on multi-modal ubiquitous sensing, big data fusion processing, artificial intelligence analysis, knowledge construction and services which have been deepened continuously; In addition, government agencies, scientific research institutions, industrial enterprises across the world have continuous passion on global, regional, national and urban sustainable development cartographic applications for resource management, ecological protection, social development. Subsequently, the new characteristics of map visualization methods from the award-winning maps were explored as well which incorporate more modern elements and cultural imprints, and emphasize people-map interaction. Afterwards, in the era of big data and artificial intelligence, the development trend of theoretical systems, technical methods and application services for cartography in the next few years are discussed. That is, the theoretical system of cartography becomes more professional and refined. In the era of artificial intelligence, the technical content of cartography becomes more knowledgeable. Cartography application services become more ubiquitous driven by big data. Cartography plays a more profound role in supporting the sustainable development of the United Nations. In the last place, some suggestions were put forward for the development of the cartography discipline in China. For example, in the future, we must make full use of the ICA international platform to continuously establish and improve a new theoretical system of cartography in the intelligent era, break through the key core technologies of cartography, and promote the high-quality development of cartography in our country with a global perspective. Meanwhile, we should pay more attention to the international frontier developments in important and emerging research fields such as geospatial data fusion, knowledge construction, spatial analysis, ubiquitous mapping, geographic intelligence, and data quality, and strengthen scientific and technological exchanges and cooperation between relevant domestic and foreign research institutions. A more proactive and proactive opening-up strategy should be implemented to promote the continuous improvement of the international influence of cartography research.

Glacial lakes, as the primary carriers of glacier meltwater, can postpone the loss of local glacier freshwater resources to some degree. However, they also offer a breeding ground for Glacial Lake Outburst Floods (GLOFs) and other mountain natural disasters (e.g., landslides, mudslides, etc.). In the mountain glacier zones, glacial lakes play a crucial role in the chain of glacier-related disaster risk. The sudden release of a massive volume of water occurs when a glacial lake dam breaches, is overtopped, or is influenced by other events such as earthquakes and avalanches of ice or rock, which poses a major danger to the downstream infrastructure, possessions, and lives of residents living in high-altitude mountains. Glacial lake evolution and glacial changes are closely related to each other. As glaciers shrink and recede, glacial lakes develop and expand. Effective prevention and management of glacial lake disaster risk requires knowledge of glacial lake changes, in addition to retrospective and investigative studies on past glacial lake outburst flood events. However, due to the distribution of glacial lakes in high-altitude mountain regions, its susceptibility to global warming, and the difficulty in accessing these areas, remote sensing monitoring has emerged as the most practical technical method and provides opportunities for analyzing global climate change and assessing natural disasters. Recent research has indicated an increase in the frequency and impact of GLOFs incidents, emphasizing the growing significance of studying these disasters. Based on this, in this study, we first identified key research areas in recent years through the metrological analysis of the literature on the remote sensing monitoring of glacial lakes and GLOFs. Second, focusing on three main directions of the research on glacial lakes and GLOFs (109 important research literatures), namely remote sensing monitoring of glacial lakes and GLOFs, response analysis of glacial lake evolution in the context of climate change, and glacial lake risk assessment with case studies of GLOFs, ten essential topics of recent research advances at home and abroad as well as the shortcomings of current studies are systematically summarized and analyzed. Finally, the direction of future research is prospected, including extraction of glacial lake morphology using artificial intelligence and GLOFs events inventory, glacier-glacial lake (especially for proglacial, supraglacial lake) system evolution and its relationship to climate change, glacial lake monitoring, and early warning and disaster prevention. Our review offers references for the management and adaptive planning of glacial lake and mountain glacier related catastrophes.

[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.