The continuous generalization of geographic information poses a huge challenge to the classic geographic information analysis modes. Networked knowledge services will gradually become a new mode for geographic information applications, facilitating to transform the form of geographic computing into social computing. Geographic knowledge services need to connect people, institutions, natural environments, geographical entities, geographical units and social events, so as to promote knowledge assisted data intelligence and computational intelligence. Facing the urgent need for spatiotemporal knowledge acquisition, formal expression and analysis, this paper firstly introduces the concepts and characteristics of spatiotemporal knowledge graph. The spatiotemporal knowledge graph is a directed graph composed of geographic spatiotemporal distribution or geo-locational metaphors of knowledge that is a knowledge graph centered on spatiotemporal distribution characteristics. Secondly we proposes a research framework for spatiotemporal knowledge graph. The framework includes various levels from multimodal spatiotemporal big data to spatiotemporal knowledge services that contain ubiquitous spatiotemporal big data layer, spatiotemporal knowledge acquisition technique layer, spatiotemporal knowledge management layer, spatiotemporal knowledge graph layer, software/tools layer, and industrial application layer. Thirdly this paper introduces relevant research progress from text implied geographic information retrieval, heterogeneous geographic semantic web alignment, spatiotemporal knowledge formalization and representation learning. Combined with application practice, we then enumerate the construction and application approaches of domain oriented spatiotemporal knowledge graph. Finally, it discusses the key scientific issues and technical bottlenecks currently faced in the research of spatiotemporal knowledge graph. It is argued that in the era of large models, constructing explicit spatiotemporal knowledge graph and conducting knowledge reasoning to meet domain needs is still the only way for spatiotemporal knowledge services.

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 new combat style places new requirements for battlefield environment service support. The intelligent service of battlefield environment urgently needs to improve knowledge based on the global multidimensional battlefield environment data. In view of the knowledge modeling problem of intelligent cognition of battlefield environment, this paper puts forward the classification method of battlefield environment knowledge and considers the battlefield environment knowledge graph as a new form of battlefield environment knowledge representation under the context of big data and artificial intelligence. To solve the fragmentation problem of triplet knowledge representation, a temporal hypergraph representation model of battlefield environment is constructed, a multi-level unified graph model combining entity knowledge, event knowledge, influence process knowledge, and service decision-making knowledge is realized, and all kinds of knowledge are represented as a unified knowledge hypergraph network with spatiotemporal and scene characteristics. Finally, the experimental verification is carried out based on the data of map, event, impact process, and combat impact effectiveness. The hypergraph network realizes the correlation of various battlefield environment knowledge from the semantic level, which can provide support for the further realization of intelligent reasoning and service decision-making based on hypergraph.

Image geo-localization is a technique that obtains the geographic location information of an image through a series of methods, so as to establish a mapping relationship with the real geographic space. This technique is important for further image information mining and has potential application value in cyberspace surveying and mapping, intelligence acquisition, user outdoor positioning, and augmented reality. Despite the tremendous progress in the field of computer vision, high-precision automatic geo-localization of images still needs to be addressed due to the involvement of multiple fields such as image feature extraction, large-scale data retrieval, large-scale point cloud processing, deep learning, geographic information feature extraction, geometric modeling and reasoning, semantic scene understanding, context-based reasoning, and multiple data fusion. This paper reviews the progress of image geo-localization research, mainly including image geo-localization methods, image geo-localization datasets, image geo-localization evaluation methods, and summary and prospect of image geo-localization. Firstly, image geolocation methods are classified into three categories, i.e., image retrieval, 2D-3D matching, and cross-modal retrieval, according to the relevance of the research content. Secondly, the datasets and evaluation methods used for image geo-localization research are categorized and summarized. The geo-localization datasets include image datasets, cross-view datasets, Structure from Motion (SfM) datasets, and multimodal datasets, etc. The image geo-localization evaluation metrics include Top-k candidates, localization error, position and orientation error per video frame, and accuracy/recall. Finally, the current status of image geo-localization research is analyzed, and the future research directions of image geo-localization are outlined in terms of global geo-localization, natural area geo-localization, multi-method fusion for geo-localization, Point of Interest (POI) data-based geo-localization, and pre-selected location refinement.

Geoscience Knowledge Graph (GKG) has strong capabilities of knowledge representation and semantic reasoning, thereby becoming a required infrastructure for the development of geoscience big data and geoscience artificial intelligence. However, existing studies on GKG were mainly conducted under the experimental scenarios. Because of a lack of research on the general framework of construction methods, sharing, and application of large-scale GKG for practical applications, it has not been used in practical applications in the geoscience field. For this reason, towards the needs of research and applications of geoscience big data and artificial intelligence for GKG, this paper first studied the construction techniques of large-scale GKG. Then, a general framework for covering the lifecycle of GKG including its construction, sharing, and application was proposed. Taking the big science program “Deep-Time Digital Earth (DDE)” as an example, the practice of developing GKG platform towards the practical application of DDE was carried out. Using this platform, this paper realized the construction of DDE large-scale GKG, the open sharing and application of built GKG, proving that the proposed framework can effectively support the construction, sharing, and application of large-scale GKG. This paper plays an important role in promoting the realization of the practical application value of GKG.

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.

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.

Being able to penetrate clouds and fog, Synthetic Aperture Radar (SAR) imagery has been widely used in flood mapping and flood detection regardless of time and weather condition. Improving the accuracy of flood maps retrieved from SAR images is of both scientific and practical significance. However, errors in SAR-derived flood maps can come from SAR image measuring principles, image acquisition and pre-processing system, water detection algorithms, and the remarkable temporal dynamics of the flooding process. The aim of this paper is to provide an extensive literature review of flood detection using SAR images (about 108 peer reviewed journal papers), including SAR data sources, flood detection methods, application of auxiliary information, accuracy evaluation, and challenges and opportunities for future research. Based on the articles reporting flood detection methods, it is found that the threshold segmentation methods such as the OTSU and KI algorithms are computationally fast and have been most widely used. The classification methods (e.g., the support vector machine and K-means clustering algorithms) have the flexibility to account for both subjectivity and objectivity, and the change detection method using the difference and ratio algorithms can effectively suppress over-detection and image geometric errors. Additionally, combining SAR images with four major types of auxiliary data to increase flood detection accuracy has become a hot topic in the past decades. Specifically, terrain information such as Digital Elevation Model (DEM), Height Above Nearest Drainage (HAND), and topographic slope can effectively reduce the impacts of shadows and exclude non-flooded areas. SAR image textural and multispectral optical information (e.g., Landsat data and aerial photos) can enhance the recognition ability of water features. Land cover/use data facilitate removing non-water features that are similar to water features, and hydrological data can help excluding permanent water bodies from temporary flood areas. From the perspectives of SAR image types, image preprocessing, detection algorithms, and accuracy assessment, major challenges are further discussed including insufficient understanding of the complexity of SAR backscattering information, limited progress in improving the signal-to-noise ratio during image pre-processing, lack of versatile flood detection algorithms, and low availability of high-quality verification data. While opportunities for future SAR-based flood detection research include combination of auxiliary information in detection algorithms, use of multiple rather than single threshold for water detection, and transition from deterministic toward probabilistic flood mapping.

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.

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.

The discrete global grid system refers to the discrete partitioning of the earth's surface into grid cells with multi-resolution hierarchical structure according to certain rules, which is widely used in organization, management, and analysis of massive multi-source spatial data. The hexagonal global discrete grid has excellent geometric properties and is well suited for spatial data processing. However, how to further improve the efficiency of the hexagonal global discrete grid coding operation is still the focus of current research. In this paper, we adopt the model of icosahedral snyder equal-area projection aperture 4 hexagonal discrete global grid system and construct the base coding structure of aperture 4 hexagon based on the correspondence between the hexagonal triaxial coordinates and the coded binary numbers, consisting of 7 base digits in the first layer and 4 base digits int the other layer. We divide the icosahedron into 3 base hexagonal subdivision tiles according to the different subdivision structures and adopt the base coding structure for coding scheme in each hexagonal subdivision tile to establish the aperture 4 hexagonal discrete global grid coding scheme. Besides, this paper designs and implements a fast conversion between aperture 4 hexagonal code and hexagonal triaxial coordinates, based on which an efficient aperture 4 hexagonal discrete global grid encoding operation scheme is constructed, including arithmetic operation of encoding, spatial topology operation, and neighbourhood retrieval operation and cross-plane operation of encoding. Compared with the existing hexagonal discrete global grid coding scheme, the coding scheme proposed in this paper has fewer base code digits, is more concise, and facilitates faster conversion to the hexagonal triaxial coordinates of the grid. Compared with the existing coding operation scheme, the proposed scheme further improves the efficiency of coding arithmetic operation, spatial topology operation, and neighbourhood retrieval operation. The coding addition operation is 2~3 times more efficient than HLQT. The neighbourhood retrieval operation is 3~5 times and 2~3 times more efficient than HLQT and H3, respectively, and is less affected by the coding level of the grid coding. The proposed coding scheme in this paper has the same efficiency of additive operation and subtractive operation, and the efficiency of spatial topology operation is 2 times that of arithmetic operation. The coding cross-plane neighbourhood retrieval operation time is slightly longer than that of the in-plane operation, and the impact on the overall operation time is not significant. This study provides support for the research application of discrete global grid system.

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.

Named Entity Recognition (NER) is the basis of many researches in natural language processing. NER can be defined as a classification task. The aim of NER is to locate named entities from unstructured texts and classify them into different predefined categories. Compared with English, Chinese have the features of flexible formation and no exact boundaries. Because of the features of Chinese and the lack of high-quality Chinese named entity datasets, the recognition of Chinese named entities is more difficult than English named entities. Fine-grained entities are subdivisions of coarse-grained entities. The recognition of Chinese fine-grained named entities especially Chinese fine-grained geographic entities is even more difficult than that of Chinese named entities. It is a great hardship for Chinese geographic entity recognition to take both accuracy and recall rate into account. Therefore, improving the performance of Chinese fine-grained geographic entities recognition is quite necessary for us. In this paper we proposed two Chinese fine-grained geographic entity recognition models. These two models are based on joint lexical enhancement. Firstly, we injected the vocabulary into the experimental models. The vocabulary was considered as the 'knowledge' in the models. Then we explored the appropriate fine-grained named entity recognition method based on vocabulary enhancement. And we found two models, BERT-FLAT and LEBERT, that were suitable for fine-grained named entity recognition. Secondly, to further improve the performance of these two models in fine-grained geographical named entities recognition, we improved the above two models with lexical enhancement function in three aspects: pre-training model, adversarial training, and stochastic weight averaging. with these improvements, we developed two joint lexical enhancement models: RoBERTa-wwm-FLAT and LE-RoBERTta-wwm. Finally, we conducted an ablation experiment using these two joint lexical enhancement models. We explored the impacts of different improvement strategies on geographic entity recognition. The experiments based on the CLUENER dataset and one microblog dataset show that, firstly, compared with the models without lexical enhancement function, the models with lexical enhancement function have better performance on fine-grained named entities recognition, and the F1-score was improved by about 10%; Secondly, with the improvements of pre-training model, adversarial training, and stochastic weight averaging, the F1-score of the fine-grained geographic entity recognition task was improved by 0.36%~2.35%; Thirdly, compared with adversarial training and stochastic weight averaging, the pre-trained model had the greatest impact on the recognition accuracy of geographic entities.

Trajectory clustering is a hot research topic in the field of spatial data mining, which is of great significance to many applications such as urban traffic control, road network construction and update. Trajectory clustering involves grouping similar trajectories into clusters where trajectory similarity measurement and clustering parameter setting are two core issues in the process of clustering. However, due to the complex morphological and structural characteristics of trajectories, the existing trajectory similarity measures are sensitive to noise or do not fully consider the consistency of trajectory motion direction. In addition, most clustering algorithms still need to manually set parameters, and the quality of clustering results is affected by the subjective experience of users. To address the above problems, this paper proposes an adaptive trajectory clustering algorithm. The proposed algorithm has two main components: a new trajectory similarity measure called Directed Segment-Path Distance (DSPD) and an improved hierarchical clustering algorithm based on the concept of central trajectory. The DSPD metric is a fusion of the spatial proximity and motion direction features of trajectories, providing a robust similarity measure. The enhanced hierarchical clustering algorithm extends the Ward hierarchical clustering algorithm by defining central trajectories and use the DSPD metric as the trajectory similarity measure. In addition, the proposed algorithm also utilizes the clustering characteristic curve to determine the optimal clustering parameters automatically. This eliminates the need for manual parameter tuning and reduces the subjectivity of clustering results. To evaluate the effectiveness of the proposed algorithm, experiments were conducted on both the simulated datasets and real-world trajectories of Wuhan. We first compared the effect of the DSPD with other commonly used trajectory similarity measures (i.e., Hausdorff distance, Fréchet distance, DTW distance, and LCSS distance) using the same clustering algorithm on the 11 sets of simulated datasets. The evaluation was based on the Adjusted Rand Index (ARI). Then we conducted another comparative analysis to access the effectiveness of the improved clustering algorithm in contrast to an average link-based hierarchical clustering algorithm. Finally, to verify the practicability of the proposed algorithm, we applied it to the process of road network updating. The experimental results show that the proposed DSPD measure outperforms alternative distance metrics on the ARI evaluation indicator. It can effectively distinguish moving trajectory clusters in different directions while considering the spatial proximity of trajectories, thus enhancing the accuracy and effect of the trajectory clustering. Furthermore, the proposed algorithm can significantly reduce the subjectivity of clustering results and provide suggestions for practical applications such as urban road network extraction and update.

The focus of geography is shifting from qualitative descriptions and quantitative analysis to support decision-making. The process of geographic decision-making usually involves multiple factors to consider and balance to achieve an optimal solution. It is a typical process of multi-objective optimization. Thus, multi-objective optimization algorithms from the field of mathematics are fundamental and have great potential to be applied in geographic decision-making. New algorithms of multi-objective optimization serve as an important source of new methods and tools for geography. This paper reviews a series of Nondominated Sorting Genetic Algorithms (NSGA-I/II/III), which are among the cutting edge and most popular algorithms in the field of multi-objective optimization. This review summarizes the principles, applications, improvements, and problems of these NSGA algorithms. Our findings include: NSGA-II is the most popular algorithm among the series because of its low computational complexity and high usability; NSGA-III has few applications in geographic decision-making for its sophisticated principles; currently, water resource management is the most successful field in applying the NSGA algorithms, and the experiences from this field are of use to others; and land use planning is the most successful field in improving the NSGA algorithms, making the NSGA algorithms more applicable to geographic decision-making. In the future, it is necessary to reduce the difficulty of applying the NSGA algorithms by summarizing typical issues in geographic decision-making and by developing user-friendly software tools for geographers. The efficiency of the NSGA algorithms can be further improved by coupling local searching strategies. It is also recommended to deeply incorporate the NSGA algorithms into the processes of geographic simulations.

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.

Scientists still cannot fully understand and explain many complex physical phenomena and dynamic systems, which cannot be described by deterministic mathematic equations and be analyzed and predicted through compact physical mechanistic models. With the ever-increasing of observational data, data-driven machine learning methods can effectively describe many complex non-linear phenomena. Nevertheless, pure data-driven models still have shortcomings in representation, interpretation, generalization capabilities, and sample efficiency. Conventional machine learning methods are confronted with challenges brought by spatiotemporal heterogeneity and sample sparsity. Recently, Physics-Informed Machine Learning (PIML) can effectively leverage observation data to describe and analyze dynamical systems when physical principles are uncertain. PIML has gain wide attention and been extensively applied in physics, computer science, biology, medical science, and geosciences. In recent years, artificial intelligence and machine learning technologies have been widely applied in geography, especially in GIScience and remote sensing, attracting wide research interests of geographers. This line of research is termed GeoAI and has become a cutting-edge research frontier in geography. PIML methods integrate the ideas of model-driven and data-driven methods, introducing new research paradigms for GeoAI and improving the description and prediction of complex geographical phenomena. This survey first summarizes recent progress in this domain from the perspectives of the representation of physical priors and the integration of physical priors in machine learning methods. Physical prior refers to existing independent knowledge that is already available before building machine learning models. This survey reviews the representation of physical priors from the aspects of augmented data and customized features, physical laws and constraints, governing equations as well as geometric properties. We also review how physical priors are integrated into various machine learning models, including constraint modeling, auxiliary task design as well as model training and inference. Based on the PIML survey framework, we explore the relationships between spatiotemporal priors and other physical priors, before briefly reviewing and summarizing typical case studies of spatiotemporal prior-informed GeoAI research. We also discuss the research agenda and future prospects of spatiotemporal prior representation and the spatiotemporal prior-informed GeoAI in the context of geo-machine learning and GeoAI frontiers. In light of fast progress of PIML, we contend that GeoAI studies that are well informed by spatiotemporal priors can gradually establish a generic geographical representation, analysis, prediction, and interpretation framework, which not only helps handle many classical problems in GIScience but also addresses future profound challenges of human being by encouraging geographers to explore more research opportunities when collaborating with researchers from other disciplines.

Multi-scale representation is one of the important research contents of geospatial data. This paper summarizes the research status of multi-scale representation of geospatial data from three aspects: geospatial data management, geospatial data scale transformation, and multi-scale representation of the map, and makes a systematic analysis and prospect of current research results. The main conclusions are as follows: ① In terms of multi-scale database and multi-scale spatial index of geospatial data management, three kinds of multi-scale database can provide better data support for multi-scale representation methods, and the hierarchical multi-scale index is the mainstream construction structure for the multi-scale database. However, at present, multi-scale database and multi-scale spatial index still have limited integration and matching ability of data at different levels, and the real-time consistency adjustment ability of data at different scales is also insufficient; ② In terms of the multi-scale transformation of geospatial data, automatic map generalization can be well combined with artificial intelligence technology. But due to the limitation of knowledge acquisition, there is still a long way to achieve automatic map generalization. The relevant achievements of intelligent automatic generalization research are mainly used to assist decision-making now, and the autonomous learning of comprehensive knowledge needs further research. Currently, most of the research is based on a discrete scale transformation model, which is incapable of continuous scale transformation. And due to the lack of a strong quality control mechanism, the results of automatic scaling have great uncertainty; ③ In terms of multi-scale representation of the map, map data types are multi-source, diverse, and flexible to use, and the multi-scale display is highly complex. Currently, the phenomena of hidden geographic information in map visualization need to be further explored. Finally, the future prospect of research on geospatial data presentation is proposed from the aspects of intelligent automatic generalization method, continuous multi-scale representation model, deep learning and cartographic synthesis, and multi-scale representation in the "new" era.

As a forward-looking and proactive policing mode, predictive policing has been a major innovation of modern policing reforms across the USA and European countries since it was proposed in 2008. As it does not involve the use of personal privacy data and can be integrated with police patrolling and precise crime prevention strategies, place -based spatial -temporal crime prediction has been a hot research topic and main component of policing practices. This research presents a systematic review of the progress of spatial-temporal crime prediction across the world since 2013 when the RAND Corporation released its special report on predictive policing. It contributes to the literature with the following five aspects: (1) summarizing the new trends in the field of spatiotemporal crime prediction studies in terms of the number of papers, research topics, leading scholars, and academic journals. The studies on spatial-temporal crime prediction have received extensive attention from various countries in recent years, and the research themes have shown a diversified trend. The most productive scholars are mainly from China and the USA, with the main focus on spatial-temporal crime prediction model development; (2) describing the new dynamics and progress of six basic components involved in the spatial-temporal crime prediction research, which are the prediction target, temporal scale, spatial scale, prediction method, performance evaluation measure, and practical evaluation. The four most widely studied types of crimes are theft, robbery, burglary, and motor vehicle theft. For burglary crime, the typical temporal unit for spatial-temporal prediction is 1-month; For the other three types of crime, the typical temporal unit is 1-day. For these four types of crime, the typical spatial unit is 200-meter grid. The top three models with the best prediction performance are random forest model, spatial-temporal neural network model, and Hawkes process model; (3) introducing several main commercial softwares for spatial-temporal crime prediction and global predictive policing practices; (4) investigating the relevant ethical issues and potential challenges that are embedded in each stage of practical applications, including data & algorithm biases, lack of transparency and countability mechanism; (5) prospecting future research directions in spatial-temporal crime prediction areas. This research provides a brief and panoramic image of the field of spatial-temporal crime prediction and can act as a reference for researchers and practitioners in relevant fields including crime geography, smart policing, and Policing Geographic Information System (PGIS).

In the context of culture-tourism integration, digital China, and activated utilization of heritage, heritage tourism has become a hot topic in academia and industry. The mismatch between spatial representation of heritage value and tourists' spatial perception is one of the most prominent contradictions in current heritage tourism. From the perspective of heritage value, this paper combines bibliometric analysis and systematic review to discuss relevant research from four aspects: interpretation and quantification of heritage value, spatial calculation and representation of heritage value, tourists' perception of heritage value and space, and tourists' spatial behavior in heritage site. Besides, comparisons between Chinese and foreign literature of these four themes are conducted to figure out the similarity and difference. The main findings are as follows: (1) there are abundant achievements in the interpretation of heritage value, which mainly focus on the connotation and interpretation technology of heritage value, but lack of quantitative methods; (2) the spatial calculation and representation of heritage value is object-oriented and application-oriented, and the geographic information system and spatial information technology are commonly used methods; (3) studies on tourists’ perception of heritage value and space are mostly from the perspective of tourism destinations of heritage sites but ignore the heritage value and spatial attributes, lacking the exploration of relationship between heritage value, heritage space, and tourists. The measurement dimension of sensory perception is mainly visual; (4) the research on tourist behavior in heritage site mainly focuses on the characteristics, patterns, causes, and influencing factors of behavior. It emphasizes the importance of practical application and reflects the orientation of heritage responsibility; (5) the spatial calculation and representation of heritage value, as well as tourists' perception of heritage value and space, are still lack of concern in the context of natural heritage and mixed heritage; (6) there are similarities and differences in the research objects, methods, and contents of Chinese and foreign literatures; (7) in the future, the interpretation and representation of heritage value will transition from traditional narrative to spatial quantification, and the perception and calculation of heritage space will shift from spatial footprint to perceptual behavior. Based on above findings, this paper puts forward a theoretical framework and methodological path from multidisciplinary perspective for tourists' spatial perception and calculation of heritage value, in order to promote the interdisciplinary theory and technology integration of heritage research. In conclusion, this paper provides theoretical references for related research and practical references for heritage protection, heritage site management, tourism development, and heritage value inheritance.

Focusing on the difficulty in image matching caused by different imaging mechanisms and large nonlinear spectral radiation distortion between multi-source remote sensing images, a deep Feature Fusion Matching (FFM) algorithm is proposed in this study. Firstly, the feature pyramid network is constructed to extract image deep features, and the feature connection structure is used to complementarily fuse high-level features with rich semantics and low-level features with accurate positioning, so as to solve the problem of difficult representation of homonymous features in multi-source remote sensing images and improve the positioning accuracy of feature vectors. Secondly, the feature map of the original dimension 1/8 is cross transformed to fuse its own neighborhood information and the feature information of the image to be matched. The first matching result is obtained by calculating the similarity score between the feature vectors. For the sparse feature area, an adaptive score threshold detection algorithm using sliding window is proposed to improve the matching effectiveness for sparse feature regions. Then the matching results are mapped to the sub-pixel feature graph, and the expected value of the matching probability distribution between pixels is calculated in a small window to check and optimize the matching results and improve the accuracy of matching point pairs. Finally, the PROSAC algorithm is used to purify the precise matching results, which can effectively eliminate the false matching and keep the correct matching points to the maximum extent. The experiment selects six pairs of multi-source remote sensing images, and compares FFM with SuperPoint, SIFT, ContextDesc, and LoFTR algorithms. The results show that the FFM algorithm is superior to other algorithms in terms of number of correct matching point pairs, matching point accuracy, matching point root mean square error, and matching point distribution uniformity. The FFM matching results are used for multi-source remote sensing images registration, and the registration efficiency is also greatly improved.

Traditional remote sensing image change detection method relies on artificial construction of features, and the algorithm design is complex and has a low accuracy. Moreover, the remote sensing image change detection technique, which superimposes two different phase images and then inputs them into the neural network, will cause the interaction of characteristics of different phases. It is difficult to maintain the high-dimensional features of the original image, and the model is less robust. Therefore, this paper proposes a remote sensing image change detection method that improves the DeepLabv3+ Siamese network based on the encoding and decoding structure of the classic DeepLabv3+ network: 1) In the encoding stage, the features are extracted by the Siamese network sharing weights, and remote sensing images are received through two input terminals respectively, so as to preserve the high-dimensional features of different time-phase images; 2) The dense atrous space pyramid pooling model replaces the atrous space pyramid pooling model in feature fusion. In addition, the method that combines the output of each atrous convolution through dense connections improves the segmentation of objects of different scales; 3) In the decoding stage, multiple levels of feature map information contain variance that causes integration problems. As a result, a feature alignment model based on the attention mechanism is introduced to guide the feature alignment of different levels, and then strengthen the learning of critical features to enhance model robustness. The open-source dataset CDD is used to verify the efficacy of the method in this paper, compared with UNet-EF, FC-Siam-conc, Siam-DeepLabv3+ and N-Siam-DeepLabv3+ networks. The test results demonstrate that the presented approach in the study achieves 87.3%、90.2%、88.4%、96.4% in precision rate, recall rate, F1 value, and overall accuracy, respectively, which are higher than those of the UNet-EF, FC-Siam-conc, Siam-DeepLabv3+ and N-Siam-DeepLabv3+ networks. The detection results turn out to be more comprehensive, and the boundary detection is smoother and more robust to scale changes.

The horizontal positioning accuracy of ICESat-2 (Ice, Cloud, and land Elevation Satellite-2) data reaches the meter level, and the plane positioning accuracy reaches the sub-meter level. Nevertheless, it is inevitable that poorly accurate laser footprint cannot be used as elevation control point due to various external factors. Therefore, this paper proposes a technique that employs multiple parameters to extract high-precision elevation control points from ICESat-2 data. At first, this method utilizes the built-in parameters to check the quality of the laser footprint point data, eliminating abnormal laser footprint points. The second step is to remove the elevation error in reference to the built-in Digital Elevation Model (DEM) data. The final step aims to set thresholds for fine screening to reserve the elevation points that meet the criteria of quality inspection, small slope, and low cloud cover based on attributes parameters such as cloud cover marker, slope parameter, and a time marker. Moreover, high-precision reference elevation data are also used to verify the selected elevation control points. To verify the effectiveness of the proposed technique, we employed the ICESat-2 laser data from western Zhengzhou, southwestern part of North Kodata, and northern Indiana (mean absolute height elevation is 3.711 m, 0.582 m, and 0.333 m, respectively) to extract elevation control points. Experimental results show that the mean absolute errors of laser footprints were 0.827 m, 0.393 m, and 0.131 m after screening, respectively. The extraction method can extract a certain number of high-precision elevation control points in multiple terrain scenarios. It also provides data support for 1:50 000 and 1:10 000 stereo mapping and offers references to the elevation control points extraction and elevation control point database construction throughout China or around the globe.

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.

User Generated Content (UGC), as a new type of geographic big data for perceiving the physical space of tourism destination, depicts the objective environment of tourism destination from the perspective of users, which is an important way to explore the perception of tourism destination. However, the traditional tourism research has limited ability to deal with travel photos. The development of deep learning image semantic segmentation technology provides strong support for mining tourists' visual behavior patterns and exploring tourism destination environmental perception. This study proposes a framework for tourists' visual behavior model and perception evaluation, which integrates the big data of online travel photos and small data of questionnaire survey, and applies it to the case of Gulangyu Island. Firstly, 744 tourism trajectories are clustered into six types of visual behavior patterns, and visualized and spatiotemporal analysis is carried out; Secondly, based on the full convolution network algorithm, the semantics of 22 507 travel photos are quantified to explore the spatial differentiation of the elements concerned by tourists with different visual modes; Finally, through the correlation analysis of photo semantics and scene perception questionnaire and the multiple linear regression model, the overall visual perception satisfaction of tourism destination is evaluated, and the corresponding spatial optimization suggestions are put forward. The results show that: (1) the visual behavior patterns of tourists on Gulangyu Island are clustered into six categories: single point tour, island scenery tour, around the island tour, street and lane space tour, heritage building tour, and whole island tour; (2) Tourists with different visual behavior patterns have spatial agglomeration in their visual interest areas, and the transfer of visual space follows the geographical proximity effect; (3) The results of correlation analysis and model show that tourists prefer areas with high spatial openness, and the areas with lower perceived satisfaction have less photography behavior, which is the focus of environmental improvement; (4) Maximizing travel time and cost efficiency, built environment, psychological environment, and social environment are the main factors affecting tourists' visual perception. This study extends the application of artificial intelligence technology in the study of tourists' visual perception, and provides a reference for tourism destination spatial optimization.

Urban functional area recognition based on multi-source big data is a complex nonlinear pattern recognition problem. The traditional machine learning methods are limited to effectively extract the information of multi granularity, time-varying, and multi-scale spatial interaction from large-scale trajectory data. Therefore, this paper designs and implements a Deep Learning model based on time series dynamic graph embedding, integrates Didi travel and Point of Interest (POI), extracts urban areas' spatiotemporal implicit features, and realizes the semantic recognition combined with cluster analysis. The results show that the land use functions in the center of Chengdu tend to be complex and diversified, and the land use attributes change with time. Furthermore, the scope and land use functions show a temporal and spatial law that they change with the activities of urban groups. The comparative experiments with the relevant literature show that the proposed method can identify the functional areas with a finer granularity. Moreover, the agglomeration degree within the same type of functional areas is higher, which can better capture the land use function changes of the composite area in different time modes. This study provides a new technical method for urban land function identification, helping researchers fully understand the structural attributes of urban areas, and has a particular value in improving the use of urban space.

Most of the precipitation datasets in Central Asia have problems such as data missing, geographical bias and outliers, low resolution, and so on. The normal prediction results obtained by most machine learning methods are usually hard to interpret, not only due to the uncertainties from input information but also due to the complicated global geographical environments as well as the underlying local geographical conditions. In this paper, to overcome this problem, we proposed a novel downscaling precipitation model to adjust and optimize the precipitation computation results from Conditional Generative Adversarial Networks (CGAN) using an inverse distance weighting method based on the prior information of geographical differences of local digital terrain model and multiple weather stations. In this study, the Amu Darya River Basin was selected as the research area due to its various geographical environment and complicated topographic and geographical conditions. First, the input Climate Research Units (CRU) precipitation data with 55 km resolution were spatially corrected based on the topographic map using the spatial deformation model. The spatial deformation model was extended from spatial transformation network methods. Second, we input the corrected CRU precipitation data, temperature, wind speed, humidity equivalent data, and remote sensing data to the CGAN computing framework for high-resolution precipitation reconstruction. The experiment adopted the cross-validation method, taking 80% of the data as the training set, and the remaining 20% as the verification set. The test set contained 20 raster maps of annual precipitation from 2000 to 2019. The model was built based on pytorch 1.10.0, the batch size was 16, and the learning rate was 0.000 3. The epoch was 8 000 iterations in the Adam optimizer for gradient descent. Finally, the precipitation data of meteorological stations were used as the true values for analyzing the geographical differences of inverse distance weights and the accuracy of the corrected precipitation grid data. The results show that the proposed method can improve the resolution and accuracy of precipitation data，especially for the complex terrain and mountainous area. And Experiments on the Amu Darya in Central Asia show that the Root Mean Square Error (RMSE) of the downscaling result within the watershed was 15.96 mm, the Mean Absolute Error (MAE) was 11.82 mm, the R² value was 0.83, and the deviation was 0.08. This study provides a robust, accurate method for improving the spatial resolution of precipitation data in complex geographical areas.

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.

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 China, urbanization has entered a later stage characterized by a slowdown in growth rates and a focus on quality enhancement. The urban growth paradigm is transitioning gradually from "incremental development" to "quality improvement of existing urban stock", marking the adoption of a new urbanization mode centered around urban renewal. Urban renewal, as a spatial governance activity within the scope of national territory, aims to continuously enhance city functions, optimize spatial layout, improve environmental quality, and stimulate economic and social vitality. However, challenges of urban renewal, such as the ambiguous definition of urban renewal oriented towards national spatial planning and the lack of a systematic logical framework for geographic information technology tailored for urban renewal, still persist. Therefore, this study reexamines the connotations of urban renewal research from the perspective of the "Production-Living-Ecological" space, expecting to achieve "intensive and efficient production space", "livable and moderate living space", and "beautiful and ecofriendly ecological space". Furthermore, with reference to the three processes of perception, assessment and optimization in "Urban Cognition", the logical architecture of geospatial information technology application for urban renewal is constructed, and based on this framework, the contributions of geographic spatial information technology in data collection, model assessment, and simulation optimization are elucidated. In the production space, geospatial information technology is able to perceive the production elements of urban renewal in real time, rapidly construct the economic benefit assessment index system and spatial assessment model, simulate the geographical process of industrial development, and optimize the spatial pattern of production. In the living space, the application of geospatial information technology helps to integrate the resources of living elements by means of spatial and temporal digitization, comprehensively assess the social benefits and carry out the spatial optimization of the allocation of public service facilities. In the ecological space, geospatial information technology provides an efficient and fast technical method for perceiving the elements of the natural environment and natural resources in a timely manner, constructing an ecological efficiency assessment index system to identify "urban diseases", optimizing the ecological spatial pattern, and exploring coping strategies to solve "urban diseases". Finally, based on the actual needs of urban renewal, the prospects for application of geographic spatial information technology in urban renewal research are discussed. This paper proposes comprehensive perception, comprehensive assessment, comprehensive optimization of urban renewal and construct an urban renewal technology system covering the whole process of "Perception-Assessment-Optimization", so as to improve the city's ability to adapt to the future development of regulation. These efforts will facilitate the modernization of national spatial governance systems and capabilities.