Agricultural cultivation, as a strategic industry in China's national economy, plays a pivotal role in grain production and sustainable development goals. It serves as the "cornerstone" of the entire economic system. Agricultural management can greatly benefit from modern technologies such as remote sensing, allowing collection of growing status information of large-area crops from high-frequency sensors. To store, organize, and process large area and long time series remote sensing data in agricultural management, as well as to save user’s computing resources and enhance the timeliness and universality, remote sensing cloud platforms play a crucial role. Google Earth Engine (GEE), as an open-source intelligent cloud platform integrating data acquisition, computation, model establishment, precision verification, and visual analysis in geographic spatial analysis, has advantages including long-term data archiving, stable operation, low-cost computation, and convenient online programming. It significantly expands the research in the agricultural field, bringing new opportunities to the development of precision agriculture. From the perspective of GEE platform, agricultural application data, crop planting support management, and farmland management tools, this article analyzes the latest research progress of agricultural application on GEE platform. The article takes "platform-data-agricultural management-tool" as the structural frame, and the platform function echoes the agricultural management field in terms of content. It emphasizes that multi-source synergistic effective data can support the decision-making of agricultural monitoring and management, efficient and easily scalable farmland planting management and crop growth monitoring tools can help improve agricultural production efficiency, and agricultural data information and agricultural management tools can jointly promote sustainable agricultural development in the global context. This article analyzes and summarizes the current research status of GEE in agricultural application data support, agricultural monitoring management, and farmland management tools, and highlights the innovative changes that the GEE cloud platform brings to agricultural management by comparisons with geospatial data processing using traditional desktop platforms. The potential of GEE in the field of fragmented farmland and pest monitoring is highlighted, and the research direction of GEE in supporting sustainable agricultural development in the global context was discussed. With the effective improvement of GEE in data quality, data processing function, and agricultural parameter information utilization, the application prospects of GEE in agricultural management fields such as precision agriculture management, agricultural condition monitoring and forecasting, mobile intelligent agricultural decision support system development, and ecosystem management are forecasted. The objective of the article is to provide a reference for platform users and offer insights for the construction and application of cloud computing platforms in China's agricultural sectors.

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.

Moving target detection plays a pivotal role in extracting temporal information from time-series images, particularly from satellite data. This method enables the rapid acquisition, analysis, and utilization of dynamic change information, meeting the demand for "real-time target discovery and delivery." In the processing of optical image-based moving target detection, existing methods often fall short of meeting the requirements for large-scale target discovery, accommodating diverse speeds, and ensuring hardware acceleration compatibility. This study aims to achieve swift perception of large-scale moving targets using optical remote sensing satellites, with a primary focus on both camera innovation and algorithm research in terms of target discovery and target information processing. This paper proposes a novel imaging mode, leveraging a dual-linear array push-broom optical remote sensing camera to capture dual-strip images containing temporal changes associated with moving targets. The camera principle prototype was successfully deployed on the "Taijing-4 Satellite" on February 27, 2022, thereby validating the technical approach for large-scale detections. Furthermore, this paper introduces a pioneering approach for detecting moving targets based on saliency region proposal for dual-band images, which significantly enhances the temporal information captured in dual-linear array push-broom imaging. Subsequently, we employ a sophisticated saliency region proposal method to extract the prominent regions of moving targets by utilizing the temporal and spatial change information within the image. These salient regions encompass dynamic targets across the entire image, effectively reducing the amount of intermediate data processed by the algorithm. Finally, a lightweight and efficient deep learning object detection model is leveraged to classify moving targets and eliminate false positives from the initial detection outcomes. The results indicate that the proposed method can efficiently detect moving targets in dual-strip images, substantially improving the accuracy of dynamic target shape extraction and optimizing the results of target matching. Notably, by enhancing the recall rate of the moving target detection algorithm, the algorithm's execution efficiency is also increased by 61.4%. This paper demonstrates two remarkable strengths in its viewpoints and discussion. Firstly, it puts forth a groundbreaking imaging mode and method to enhance the temporal information of images, effectively addressing the challenge of observing large-scale moving targets without relying on satellite attitude maneuvering. Secondly, it proposes a highly efficient moving target detection model based on saliency region proposal, resolving the problem of detecting moving targets in complex backgrounds. The acquisition of key information about moving targets can significantly reduce the bandwidth requirements for ground transmission of remote sensing data, providing a new way of data acquisition and on-orbit processing for mega Earth observation systems.

Optical remote sensing technology can efficiently capture a wide range of surface water information. However, in challenging applications such as flood monitoring, it is often difficult to obtain high-quality optical remote sensing images, due to cloudy, rainfall, or other inclement weather conditions. Synthetic Aperture Radar (SAR) technology can provide images of both day and night and unaffected by cloud coverage, which provides obvious advantages over optical remote sensing. In this paper, we summarize the methods of surface water body extraction using SAR data, analyze the advantages and disadvantages of Sentinel-1 Dual-Polarized Water Index (SDWI), and propose a Dual-Polarized First-Principal-Component Water Index (DFWI). In addition, aiming at the problem that radar shadow is easily confused with waters in SAR images, three kinds of ascending-descending polarimetric radar water body extraction methods are proposed: Ascending-Descending VV-Polarization (AD-VV), Ascending-Descending VH-Polarization (AD-VH), and Ascending-Descending Dual-Polarized First-Principal-Component Water Index (AD-DFWI) methods. Finally, two experimental sites, Yunnan Province, China, and Hatay, Turkey, are selected for water body extraction, which corresponded to normalized monitoring and emergency monitoring of water bodies, respectively. The accuracy of the water body extraction is evaluated and analyzed using four metrics：User Accuracy, Producer Accuracy, False Alarm Rate, and F1-score. The experimental results show that: (1) Compared with the single-polarization method and the SDWI method, the classification accuracy of our proposed method is significantly improved. The AD-DFWI method achieves the highest accuracy in both experimental areas, e.g., the F1-score index reaches 97.83% and 88.33%, respectively; (2) Our ascending-descending polarimetric SAR water extraction methods not only solve the problem of the fusion between shadows and waters but also generate a more prominent bimodal distribution histogram for improved discrimination of water and non-water. In flat areas that are not affected by radar shadows, the DFWI method can also obtain high-precision water body extraction results. This method is not only suitable for Sentinel-1 dual-polarization data but can also be theoretically extended to fully polarized data such as GF-3, ALOS-2, RADARSAT-2, and TerraSAR-X. If multiple satellite constellations can be used to obtain the ascending and descending orbit data at the same time in the future, or the revisit time of remote sensing satellites can be shortened, the accuracy of the ascending-descending polarimetric SAR water body extraction methods will be further improved. The method proposed in this paper can provide a reference for challenging applications such as surface water extraction and flood monitoring in the future.

Assessment of individual Socio-Economic Levels (SEL) is crucial for business decisions, urban planning, and public health. However, current methods highly rely on location data and call detail records to construct travel locations and mobile business features, which is inadequate to represent the semantic context of individual travel, and fail to understand the motivations and demands of travel activities. Consequently, it makes the modeling process lack interpretability. To address aforementioned issue, this paper proposes a novel assessment method of individual socio-economic levels based on the analysis of trajectory activity semantics. It models individual socio-economic levels from the perspectives of consumption ability and willingness by explicitly extracting six consumption patterns including residence, shopping, dining, entertainment, consumption preferences and exploration, thereby enhancing the interpretability of the assessment method. Specifically, ① Stay points extracted from trajectories are categorized into four types of activities, including residence, shopping, dining, and entertainment, by tagging semantic context through a grid-based semantic map; ② Spatiotemporal and semantic features such as temporal entropy, gyration radius, and economic level of activity areas, are calculated for the four activities respectively. We then employ the structural equation model to select appropriate features for measuring the values of consumption patterns; ③ Extreme random forest is utilized to assess individual socio-economic levels using the values of six consumption patterns, which is calculated based on the economic levels of regions where an individual stays in the travel activities, as well as the preferences for visiting these regions. We use GPS trajectories of 635 anonymous private car drivers in Shenzhen city of China from April to November in 2019 as experimental data, and assess individual socio-economic levels for each driver. The effectiveness of the proposed method is validated by selecting representative individuals with high and low socio-economic levels from five typical scenarios i.e., central business districts, labor-intensive factories, premium residences, and urban villages, which demonstrates alignment between the calculated socio-economic levels of individuals and the depicted value of the scenarios. Besides, we analyze the spatiotemporal distribution and work intensity of different socio-economic level groups, and explore their differences in travel patterns. The findings indicate that individuals with a higher socio-economic level tend to have more flexible morning commutes, and exhibit a smoother travel distribution in the afternoon. It also presents a more concentrated spatial distribution in terms of their activity areas, which is consistent with the urban structures of Shenzhen. In summary, the proposed method can provide a reference for modeling demographic characteristics of individuals from the perspective of human-environment interaction.

Improving the structural resilience of the knowledge innovation network of urban agglomerations is conducive to building a safe regional innovation system and ensuring the knowledge creation function of cities. The knowledge innovation network of urban agglomeration is constructed based on the data of papers published on WOS website for 41 cities in the Yangtze River Delta from 2011 to 2021. Based on evolutionary toughness theory and complex network theory, a four-dimensional evaluation index system of network structure toughness is constructed, which is "vulnerability-invulnerability-resilience-evolution". Social network analysis and GIS spatial analysis are used to describe the temporal evolution and spatial pattern of network structure toughness. GTWR and MRQAP models are used to identify the driving factors. The results show that: (1) From 2011 to 2021, the structural resilience of the knowledge innovation network of the Yangtze River Delta urban agglomeration shows an upward trend, with weakened three-dimensional characteristics, optimized transmission environment, weakened heterogeneous connections, and enhanced integration attributes. There are obvious regional differences in the resilience of the network structure, showing a layout trend of "high in the middle and low in the north, high in the east and low in the west"; (2) The leading nodes of resilience are core cities such as Shanghai, Nanjing, Hangzhou, Hefei, and Suzhou. Ensuring the stability of the dominant node is the key to ensuring the structural resilience of the network. The vulnerable nodes are mostly concentrated in the northwest and southwest of the Yangtze River Delta. Although they can avoid large-scale network collapse through the regional "lock-in" effect, there may also be connection instability caused by poor network linkage and poor resource transmission; (3) Science and education support and industrial structure have significant positive driving forces on the resilience of urban nodes. The explanatory power of economic development, opening up, human capital, and knowledge base has distinct spatial heterogeneity. There is a significant correlation between the driving factors, indicating that the interaction of two factors is an effective way to improve the resilience of urban nodes and promote the evolution of the overall resilience of the network; (4) The MRQAP results show that network aggregation effect, matching effect, siphon effect, and multi-dimensional proximity all drive the resilience evolution of network structure. Network topology, similar industries and similar human resources, good educational environment, and three-dimensional proximity of institutions, society, and organizations are all conducive to the formation of strong knowledge cooperation between cities.

Understanding the spatial and temporal characteristics of the global squid fishery can positively guide the assessment and management of squid resources. In this paper, the 19 major marine fishing areas from FAO major Fishing Areas are selected as the study area, and the spatial and temporal characterization of global squid fishing is analyzed based on the 2020 global squid fishing vessel AIS trajectory data and VIIRS nighttime light imagery with the GIS spatial analysis approaches. The results show that: (1) Squid fishing is mainly distributed around the Argentine Exclusive Economic Zone (EEZ) in the south-west Atlantic Ocean, the offshore of northeastern Japan in the north-west Pacific Ocean, the offshore of western Peru in the South-east Pacific Ocean, the Arabian Sea in the north-west Indian Ocean, and the Arafura Sea in the west and central Pacific Ocean, which are mainly concentrated in the mid- and low-latitudes within 100 km and 400~700 km offshore, and show a strip-oriented distribution pattern; (2) The temporal rhythm of global squid fishing shows a "two peaks and two valleys" trend, with an upward trend in January-April and June-August, and a downward trend in April-June and August-December. Specifically, squid fishing at mid-latitudes shows an increasing trend during the summer and autumn and a decreasing trend during the winter and spring, while squid fishing at low latitudes shows an increasing trend during the fall and winter and a decreasing trend during the spring; (3) The spatial and temporal characteristics of squid fishing are affected by ocean currents, squid reproduction characteristics, and the policies of various countries on pelagic squid fishing. The direction of its spatial distribution is consistent with the main ocean currents, and the fishing intensity apparently decreases during the squid spawning peak season and the fishing moratorium period; (4) The AIS track density and nighttime light intensity are well correlated spatially and temporally with squid fishing signals. The greater their spatial correlation, the more similar the temporal patterns of variation exhibited. This study further demonstrates the capability of nighttime light data to provide a good squid fishing metric, which can be complemented with vessel trajectories to achieve highly accurate squid fishing assessments.

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.

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 Earth's surface exhibits a diverse topography, characterized by ups and downs in spatial geometry and carrying many geographical entities. With the advent of the era of spatiotemporal big data, the exploration of geospatial information gradually has shifted from spatial perception to spatial cognition. However, traditional information description methods based on Reductionism and independent hierarchical modeling are increasingly oriented toward geographic information management and visualization. It is difficult to describe the massive information, complex interactions, and spatiotemporal changes of terrain and its associated environmental elements using this information description method. To address this issue, this paper conducts a systematic geospatial analysis of the content and interaction of terrain ontology and associated environmental elements from the perspective of scenario science. We comprehensively describe the entities, relations, and spatiotemporal changes in the terrain scenario. Here, the terrain scenario is defined as the specific environment in which the terrain exists and the state changes. It is a regional complex composed of terrain objects and other natural and human objects with connections and functions within a certain time and space. The element composition and logical structure of terrain scenario are analyzed by employing holistic, semantic, and logical elements centered around time, place (space), things, events, and phenomena to describe terrain scenarios. The elements of terrain scene should be based on the elements of things, develop and evolve in a certain time and space, form the elements of events and phenomena, and finally form a complex that operates dynamically under the framework of time and space. We further analyze and organize the content composition of terrain scene objects and their associated information and create a structure description framework based on element object information architecture. Moreover, we prospect the future development of terrain scenario and the possible technical application directions in the future, such as the geoscience semantic network and knowledge map dominated by terrain and associated elements, the scene event spectrum based on the characteristics of spatiotemporal nodes and associated elements, and the scene holographic digital terrain analysis. The introduction of terrain scenes not only deepens the traditional methods but also provides a new idea at the conceptual level. It is an exploration of the concept of scene science and lays a foundation for a systematic approach for solving and expressing the evolution and development of terrain ontology and environment within the space-time framework.

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.

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.

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.

With the new generation of information technologies, such as earth observation, IoT monitoring, the Internet, 5G, and the deepening of the concept of open data sharing, there has been an explosive growth of openly shared earth surface system data on the web, and open big data on the earth surface system has taken shape. Rapid discovery, mining, and utilization of massive open scientific data of the earth surface system (hereinafter referred to as "surface system" ) is a new development trend and frontier research direction of scientific data sharing of surface system in the era of big data. The open scientific data of surface system has the characteristics of decentralized organization, multi-source heterogeneity, multi-modality, and multi-type, and usually exists in the form of thematic sharing websites, data services, metadata, journal papers (especially data papers), etc. The research on the development of open data mining methods for surface system adapted to different modalities and the analysis of its sharing quality are key scientific issues to make full use of these data. The association network provides a powerful support for semantic interconnection and knowledge discovery of open scientific data of surface system, which takes metadata Uniform Resource Identifier (URI) as nodes, semantic relationship between metadata as edges, and the strength of association between nodes as the value of edges. This paper investigates and analyzes the current development status, basic features, and construction technology from the perspective of construction of open scientific data association network of surface system. We select typical association networks and related literatures at home and abroad as the research objects. Based on the selected nine mainstream association networks and more than 200 related literatures, we make a comparative analysis from the aspects of basic features of the association networks and the construction technology. In terms of basic features, the data source, automation degree, and updating method of the association networks are analyzed; in terms of construction technology, the selection of association indexes is introduced, and the methods of extracting, representing, and calculating the features of open scientific data of the surface system are discussed. Finally, recommendations for future construction of the surface system association network are put forward, including construction of a high-quality and full-coverage surface system open scientific data ontology, consideration of "time-space-content" geoscientific knowledge complex relationship and reasoning, establishment of a multi-language surface system open data association network method, and enhancement of the effectiveness of the surface system open scientific data association network application.

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 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.

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.

Due to the influence of buildings, the Global Digital Elevation Model (GDEM) obtained through remote sensing measurement still contains various ground features in urban areas and cannot reflect the bare Earth's surface. This limits its application as basic data in hydrological simulation, geological disaster prediction, urban construction, and other fields. Therefore, in order to improve the quality of GDEM products in urban areas, this paper proposed a GDEM correction method that takes into account spatial heterogeneity and SHAP (Shapley Additive Explanations) feature screening. Firstly, the Gaussian Mixture Model (GMM) and Theissen polygons were used to divide the study region into the several sub-areas to solve the heterogeneity of GDEM correction error and the spatial relationship of characteristic factors. Then, the Random Forest (RF) was used as the base model, coupled with the SHAP interpretable framework, to screen the optimal feature variables in each subregion. Finally, based on the selected features, the corresponding modified model was reconstructed for urban GDEM correction. To verify the practicability and efficiency of the proposed method, 23 initial feature variables were selected in this paper. COPDEM (COPDEM30) with 30 m resolution in New York was taken as the research object, and airborne Light Detection And Ranging (LiDAR) DTM data were used as the reference. The proposed method was compared with three other methods, including spatially heterogeneous random forest (SH-RF), global random forest (FS-RF), and traditional Random Forest (RF), as well as an existing GDEM (FABDEM) product which removes building and vegetation biases. The experimental results show that the proposed method had the best prediction performance, with its Mean Absolute Error (MAE) decreasing from 5.209 m to 1.436 m and median error (RMSE) decreasing from 8.884 m to 2.258 m, followed by SH-RF, with its MAE decreasing by 3.607 m and RMSE decreasing by 6.389 m. RF was the worst, with MAE decreasing by 3.179 m and RMSE decreasing by 5.838 m. In addition, compared to FABDEM, the MAE and RMSE of the proposed method were reduced by 42.3% and 63.2%, respectively. The migration experiments on the proposed method showed that, compared to the original COPDEM30, the modified GDEM's MAE and RMSE were reduced by 50.5% and 50.4%, respectively. The visual comparison of the DEM before and after modification also showed that the modified COPDEM30 not only retained the topographic features well but also had similar elevation distribution with LiDAR DTM. Therefore, the method in this paper shows a novel generalization ability.

Digital economy has been rapidly growing and raises worldwide attention due to the accelerating diffusion and permeation of Information and Communication Technologies (ICT) from the fields of Information Technologies (IT) to more. At the same time, it has further triggered the great innovations and changes in depth in the areas of society and economy in China. This objectively makes it clear that the inheritance of and remaining Chinese traditional cultures are the paramount social need at current stage. Although more attentions are put on traditional Chinese settlements than ever before, there is a lack of documents and research findings on how to capture and reveal the core features and value of the cultural resources of traditional settlements. To resolve this issue, this work firstly dissects the core features of Cultural Landscape Genes of Traditional Settlements (CLGTS) from the perspective of geographical information based on the basic elements and contents. In terms of the theoretical model of ternary space in information geography, this work first presents a recognition-dimension model for understanding the geographic information of CLGTS via the perspective of environmental space, settlement space, and social cultural space and taking natural values, survival values, and cultural values as the pertinent understanding dimensions. Then, from this point of view, the concept of Core Feature Parameter of CLGTS (CFP-CLGTS) is proposed by this work according to the heart characteristics such as spatial position, form, culture, attributes, evolution, and geographic values, etc. Simultaneously, this work also explores the connotations, classifications, and characterizations of CFP-CLGTS and further clarifies its mathematical implications. On the basis of the above findings, this work develops three recognition rules of CFP-CLGTS, which are uniqueness, accordance, and whole superiority, respectively. In accordance with the above three recognition rules, this work presents an identification method to extract CFP-CLGTS. Finally, this work designs an experiment to verify the concept of CFP-CLGTS and test the corresponding extraction approach through developing a dedicated program. In the experiment, 6 national historic-cultural famous villages located in Hunan Province are chosen as the test examples. According to the results, the extraction method is possible and potential. Through this work, CFP-CLGTS is a possible way to analyze and reveal the critical features of the cultural resources of traditional settlements from the perspective of geographical information. Therefore, CFP-CLGTS has great significance and practicable applications value. In next research, much more attention should focus on the efficiency of extraction algorithm for CFP-CLGTS, and develop more application examples.