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.

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.

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.

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.

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.

[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.

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.

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

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

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.

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

The 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.

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

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.

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

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.

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.

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

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

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

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.

The development of hydrologic time series analysis is crucial for the effective management and utilization of water resources. Based on the WoS Core Collection database and the CNKI database, this paper employs bibliometrics and CiteSpace software to reveal the development trends, research hotspots, and future directions in the field of hydrologic time series analysis both domestically and internationally. Firstly, starting with the randomness, nonlinearity, and uncertainty of hydrologic time series, as well as emerging methods such as machine learning and neural networks, this paper divides the recent advances in the field of hydrologic time series analysis into six aspects. Then, a detailed introduction for each advance is provided, and a comparison with traditional methods is also made to summarize the shortcomings of traditional methods. Finally, the directions for improving the accuracy of hydrologic time series analysis are pointed out, including:1) modeling at spatiotemporal scales and integrating multi-source data for analysis; 2) incorporating physical mechanisms into machine learning models to enhance interpretability and generalization capabilities; 3) considering the coupling of climate change (extreme weather events) and hydrologic processes in research advances; 4) conducting comprehensive research on multiple complex characteristics and improving the research level of each complex characteristic. By revealing the development trends, research hotspots, and future directions of hydrologic time series analysis both domestically and internationally, we can better understand and respond to the impacts of climate change, extreme weather events, and human activities on water resources, enhance our understanding of hydrologic processes, and provide scientific basis for water resources planning, flood risk management, and sustainable development.

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

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

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

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

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

The big data on natural resources and territorial space provides an essential foundation for scientific allocation and efficient management of natural resources, as well as a vital support for territorial space optimization and governance. As a fundamental technical support, it has become increasingly important for the management and research of natural resources, especially for fully fulfilling the duties of "two unification". Firstly, this paper provides a general overview of the development history, current technical status, and main challenges of big data, and focuses on key advances in perception, management, analysis technologies, along with the technical development of big data on natural resources. The problems such as monotonic research areas and piecemeal technical applications in the research and application of big data in the field of natural resources are also pointed out. This paper further expounds the basic connotation of big data on natural resources and territorial space from the dimensions of "natural resources", "territorial space", and "human activities". Infinite regional space and coupling of resources and space are the distinctive charactertistics in the connotation. Secondly, based on the application requirements and combined with full life circle processing technology of big data, the technical application framework of big data on natural resources and territorial space is developed and characterized by factors, digital intelligence and integration. This framework contains five structural and technical layers including data collection layer, data governance layer, support layer, application layer, and user layer. The advantages of industrial data center manager in mass, multimode, and isomerism data process, organization and management are displayed. This paper archives the transition from big data to knowledge services and then to digital intelligent governance and focuses on the core business of natural resources "two unification". Besides, four types (ten items) of natural resources management business applications are reconstructed. Finally, this paper carries out application practice analysis from three aspects: natural resources survey and monitoring, "smart+" regulatory decisions, and "Internet+" government services, and analyzes the application scenario, application effect and development direction. The deficiencies and main challenges of the proposed framework are also discussed. Additionally, this paper prospects the future development of big data on natural resources and territorial space in terms of data acquisition and storage, data governance, expression and visualization, analysis and mining, application and services.

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.