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

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

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

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.

Convolutional neural networks have been widely used in the task of Remote Sensing Image Scene Classification (RSISC) and have achieved extraordinary performance. However, these excellent models have large volume and high computational cost, which cannot be deployed to resource-constrained edge devices. Moreover, in the RSISC task, the existing knowledge distillation method is directly applied to the compression model, ignoring the intra-class diversity and inter-class similarity of scene data. To this end, we propose a novel class-centric knowledge distillation method, which aims to obtain a compact, efficient, and accurate network model for RSISC. The proposed class-centric knowledge distillation framework for remote sensing image scene classification consists of two streams, teacher network flow and student network flow. Firstly, the remote sensing image scene classification dataset is sent into the teacher network pre-trained on a large-scale dataset to fine-tune the parameters. Then, the class-centric knowledge of the hidden layer is extracted from the adjusted teacher network and transferred to the student network based on the designed class center distillation loss, which is realized by constraining the distance of the distribution center of similar features extracted by the teacher and student network, so that the student network can learn the powerful feature extraction ability of the teacher network. The distillation process is combined with the truth tag supervision. Finally, the trained student network is used for scene prediction from remote sensing images alone. To evaluate the proposed method, we design a comparison experiment with eight advanced distillation methods on classical remote sensing image scene classification with different training ratios and different teacher-student architectures. Our results show that: compared to the best performance of other distillation methods, in the case of the teacher-student network belonging to the same series, the overall classification accuracy of our proposed method is increased by 1.429% and 2.74%, respectively, with a given training ratio of 80% and 60%; and in the case of teacher-student networks belonging to different series, the classification accuracy is increased by 0.238% and 0.476%, respectively, with the two given ratios. Additionally, supplementary experiments are also carried out on a small data set of RSC11 with few classes and few samples, a multi-scale data set of RSSCN7 with few classes and multiple books, and a large complex data set of AID with many classes of heterogeneous samples. The results show that the proposed method has good generalization ability. Trough the comparison experiments with similar techniques, it is found that the proposed method can maintain excellent performance in challenging categories through confusion matrix, and the proposed distillation loss function can better deal with noise through testing error curve. And visualization analysis also shows that the proposed method can effectively deal with the problems of intra-class diversity and inter-class similarity in remote sensing image scenes.

With the deepening of global economic integration, maritime traffic congestion and ship accidents occur frequently. In order to supervise and analyze the marine ship activities, the traditional methods mainly use the ship positioning data for data mining without combining other marine multi-source data for the analysis of ship spatiotemporal activity process and behavior pattern, and thus lack deep knowledge mining. Therefore, this paper makes comprehensive use of multi-source data and constructs the ship activity knowledge map based on extracting the semantic information of trajectory, which provides an effective way for the transformation of trajectory spatiotemporal point sequence with low knowledge density to high-order semantic knowledge. Specifically, firstly, by analyzing the characteristics and constituent elements of ship activities, the ontology layer of ship activity knowledge map is designed based on the core idea of "process-event-behavior"; Then, the track semantic information is extracted by Stop/Move model, and the ship emergencies are extracted by DMCNN model to complete the filling of instance layer; Finally, the above model and method are verified by constructing a prototype system. The results show that the ship activity knowledge map constructed in this paper can support the knowledge representation of ship routine activities and emergencies, and realize spatiotemporal activity query and backtracking, so as to achieve the effect of semantic enhancement, which has a certain application value.

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

With the development of Unmanned Aerial Vehicle (UAV) technology, it has been applied to various tasks in different fields. The prerequisite for a UAV to perform successful aerial tasks is accurate localization of its own position. Generally, traditional UAV navigation relies on the Global Navigation Satellite System (GNSS) for localization. However, this system has disadvantages such as instability and susceptibility to interference, leading to situations where UAV cannot use GNSS for positioning, known as GNSS-denied environments. This study focuses on the navigation and positioning of UAV in GNSS-denied environments and proposes a UAV visual retrieval and positioning method that comprehensively utilizes local and global deep learning features of known satellite orthophotos. Specifically, ConvNeXt is used as the backbone network, combined with generalized mean pooling, to form a retrieval feature extraction algorithm for extracting global features of satellite and UAV images. A triplet loss function considering the overlapping area between images is designed for the retrieval and positioning tasks, and a corresponding training data set is established to train the feature extraction algorithm. Then, the satellite images within a certain range are retrieved according to the extracted global features, and the preliminary retrieval results are obtained. In order to further improve the accuracy of the retrieved target images, the LoFTR algorithm based on deep learning local features is used for matching and reordering. Since the LoFTR algorithm has many mismatches, RANSAC is used to screen the matching results. Experiments using the test datasets we established demonstrate that the proposed method obtains an average accuracy of 90.9% and an average time cost of 2.22 seconds for retrieving satellite images in different seasons from fully overlapped UAV simulated images. The accuracy of the UAV real image test is 87.5%, which can meet the UAV positioning requirements.

China is one of the countries that are most severely affected by typhoons. The direct economic losses caused by typhoons amount to more than 10 billion yuan, and the affected population is more than one million each year in China. Typhoon disasters seriously threaten the social economy and natural environment of coastal areas. As a result, there is an immediate necessity to improve disaster emergency management and comprehensive disaster prevention and mitigation. With the concept of knowledge graph proposed by Google in 2012, it has gradually become a research hotspot in the field of artificial intelligence and played a role in applications such as information retrieval, question answering, and decision analysis. The information integration and representation capability of the knowledge graphs can provide effective support for dynamic monitoring and management decisions of typhoon disaster events. There are problems with the current typhoon disaster models in the representation of spatiotemporal processes. Most disaster knowledge graphs are analyzed for single elements of disaster events, and the research on ontological representation and analysis of disaster development process is still lacking. Firstly, we propose a typhoon disaster events knowledge representation model established from five levels: concept, object, state, characteristic, and relationship, by analyzing the components and dynamic characteristics from the typhoon disaster mechanism. Second, this model considers the multi-granularity of typhoon event information, and unifies the different feature information into each object. Besides, in order to highlight the evolutionary characteristics of typhoon events, the state is taken as the cross-section of the process in a specific spatiotemporal feature. The state is an action or record of an object occurring in time and space. The process is the procedure that properties, forms, and patterns undergo as it gradually changes over time. Finally, we construct a knowledge graph of typhoon disaster events using the 2021 severe typhoon In-Fa as a case study. The results show that the model not only highlights the characteristics of different objects portrayed in the event, but also expresses the spatiotemporal processes of typhoon disaster events through the state sequences of multiple objects. The knowledge graph can be applied as a primary knowledge source in the emergency decision management of typhoon disaster events, which can undoubtedly enable relevant decision-makers to better perceive the spatial and temporal development of typhoon disaster situations. In disaster assessment, based on the rich contents covered in the knowledge graph, it can cope with assessing various aspects such as human casualties, economic losses, and secondary disasters.

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

Wetland is of great significance to biodiversity and climate change, and it is also one of the basic living environments of human beings. In order to better understand and express wetland knowledge and the relationship between classifications, this paper proposes an ontology-based wetland knowledge graph construction method. Based on the land cover classification system of GlobeLand 30, this paper establishes the conceptual structure of wetland data and the rich semantic relationship between the elements around wetland type definition, spatial pattern, case distribution, and trend change. Firstly, based on the prior knowledge of wetlands, taking the wetland types in the GlobeLand 30 classification system as an example, we analyze the wetland domain knowledge around the wetland types, feature distribution, and other elements, extract the semantic relationship between knowledge, and construct the ontology database of wetland knowledge by combining top-down and bottom-up methods. The conceptual framework of wetland knowledge graph is formed through ontology modeling. Secondly, based on the wetland knowledge automatically extracted from the technical specification text and encyclopedia website, the extracted conceptual knowledge is stored in the model layer, and the data layer is constructed from bottom to top. The main contents include knowledge acquisition and knowledge fusion. According to the concepts contained in wetland knowledge, the relationship extraction of wetland knowledge is carried out, mainly including attribute relationship, spatial relationship, and temporal relationship. Using the wetland directory crawled from the wetland China website, the wetland entity name and knowledge are directly extracted from Baidu Encyclopedia by means of web crawler to form a triple. Finally, Through the above construction processes of wetland knowledge graph, the wetland related data with different structures are transformed into structured knowledge triple data, and the graph database Neo4j is used for semantic relationship storage with the "node relationship" storage model. Knowledge graph provides a new idea for the study of rich knowledge representation and storage in the field of land cover. It is a bridge between the basic geographic data of surface coverage and spatial knowledge service. It is of great significance to promote the sharing and reasoning analysis of surface coverage data. Taking the wetland land cover type as the research example, the knowledge graph constructed in this paper expands the conceptual description information of wetland entities, explores the wetland knowledge representation method by considering the temporal and spatial characteristics, and provides a new perspective and application demonstration for the expression of land cover knowledge.

Natural language is an effective tool for humans to describe things, with diversity and ease of dissemination, and can contain human spatial cognitive results. How to use natural language to describe geographic spatial scenes has always been an important research direction in spatial cognition and geographic information science, providing important application values in personalized unmanned tour guides, blind navigation, virtual space scene interpretation, and so on. The essence of natural language description of geographic spatial scenes is the process of transforming the two-dimensional vector of geographic space into a one-dimensional vector in word space. Traditional models perform well in handling spatial relationships, but are somewhat inadequate in natural language description: (1) spatial relationship description models are one-way descriptions of the environment by humans, without considering the impact of the environment on the description; (2) spatial scenes emphasize traversal-based descriptions of spatial relationships, where each set of spatial relationships is equally weighted, which is inconsistent with the varying attention paid by humans to geographic entities and spatial relationships in the environment; (3) the spatial relationship calculation of traditional models is a static description of a single scene, which is difficult to meet the requirement of dynamic description of continuous scenes in practical applications; (4) the natural language style of traditional models is mechanical, lacking necessary knowledge support. This article proposes a spatial scene natural language generation framework Map2Text (M2T) that integrates multiple knowledge graphs. The framework establishes knowledge graphs for spatial relationships, language generation style, and spatial attention, respectively, and realizes the fusion of multiple knowledge graphs and the generation of natural language descriptions of spatial scenes within a unified framework. The spatial scene description knowledge graph solves the pruning problem of traversing spatial relationships, and establishes the relationship between spatial scenes by building a spatial relationship graph, supporting continuous expression of spatial scenes; the natural language style knowledge graph establishes the relationship between spatial expression and language style, achieving diversified language styles that are appropriate for spatial natural language expression; the spatial attention knowledge graph captures the nuances of natural language spatial expression by establishing an attention matrix based on the interaction state between the subject and object of the spatial scene. An experimental prototype system designed based on the Beijing Forbidden City demonstrates that the system-generated results are close to human travel notes, with more complete content coverage and more diverse styles, verifying the effectiveness of the M2T framework and demonstrating the potential value of natural language description of spatial scenes.

Western Guangdong is one of the three major sugarcane producing areas in China. Sugarcane in western Guangdong is mainly distributed in Zhanjiang, with more than 2 million sugarcane farmers. In 2020, the sugarcane planting area in Zhanjiang reached 130 030 hectares. Mapping sugarcane plantation and analyzing its spatiotemporal characteristics in western Guangdong are of great values for making polices in sugarcane industry, optimizing the distribution of sugarcane plantation, and promoting production efficiency. Remote sensing technology provides an efficient way to acquire land cover information. In this study, the sugarcane plantation distribution information in 2000, 2008, and 2020 was acquired based on Landsat remote sensing data and statistics data in sugarcane planting areas. Following steps were implemented: preprocessing of Normalized Difference Vegetation Index (NDVI) time series, construction of reference NDVI of sugarcane, extraction of the amplitude and the maximum of NDVI time series, and identification of sugarcane using the Time Weighted Dynamic Time Warping (TWDTW) method. The TWDTW method calculated the distance between NDVI time series of unknown pixels and sugarcane pixels, and a distance threshold was set via the statistics data to acquire the sugarcane plantation distribution. Based on the extracted distribution of sugarcane plantation, the kernel density of sugarcane distribution was calculated to analyze the spatial clustering characteristics of sugarcane planting areas. Landscape pattern indexes such as the percentage of landscape, average patch area, patch density, and aggregation index were calculated to analyze the spatial distribution characteristics of sugarcane planting patches. The topographic characteristics of sugarcane planting areas were also analyzed based on DEM data. The results showed that: ① the TWDTW model could realize sugarcane identification with high accuracy by combing remote sensing time series data and statistics data. In 2000, 2008 and 2020, the average accuracy of sugarcane mapping reached 87.62%; ② Sugarcane was mainly distributed in Suixi, Leizhou, and Xuwen in western Guangdong. The distribution of sugarcane planting in Suixi and Leizhou presented a pattern of high-density aggregation in multi centers; ③ From 2000 to 2020, the average area of sugarcane patches increased, the patch density decreased, and the aggregation index increased in Suixi and Leizhou, which indicated that the layout of sugarcane plantation had been significantly adjusted in these areas, and sugarcane production showed a trend of intensive production; ④ In Suixi and Leizhou, most of sugarcane was planted in flat areas, showing a great potential to develop mechanized production.

The graph structure-based knowledge graph plays important roles not only in the description and reasoning of semantic network, but also in the structured abstraction and spatial reasoning of spatial entities. The relational information of spatial entities is recorded in edges in the knowledge graph. Through the edge-based knowledge graph computational reasoning such as path detection, sub graph alignment, pattern discovery, etc., it can play an important role in spatial scene cognition. Geographic knowledge graph is a knowledge system that formally describes geographic concepts, entities, and their interrelationships. It has both the connotation and characteristics of general knowledge and the specific spatiotemporal characteristics of geographic knowledge. It can connect semantic models with spatiotemporal models to describe semantic relations, spatial relations, and temporal relations, and has great application potential in the expression, understanding, acquisition, and reasoning of geographic knowledge. The existing research work of geographic knowledge graph is mostly focused on semantics, and the extraction and expression of semantic relations are very rich and comprehensive, which can support further functions such as semantic search and association analysis of geographic knowledge. However, the knowledge expression of geographic knowledge graph in spatiotemporal model is relatively lacking, and the existing spatial relationship is limited between elements, rarely involving the further distribution situation and spatial pattern in spatial cognition. Thus, the geographic knowledge graph needs to be strengthened in terms of spatial semantic knowledge. Based on the principle of knowledge graph construction, this paper takes the construction of geographic knowledge graph of buildings as an example to realize the grid-pattern recognition of buildings. Firstly, the buildings are abstracted into entities and expressed as nodes of the graph, and the spatial neighborhood relations between buildings is extracted based on geometric proximity analysis, so as to build the geographic knowledge graph of the building group. On this basis, combined with the domain knowledge of building pattern recognition, it further infers and constructs other spatial semantic relations, and improves the geographic knowledge graph. Then the grid-pattern of the buildings complex scene is expressed as the rules of the knowledge graph, which is based on NoSQL language for reasoning. The results show that this method can effectively extract the linear pattern of buildings and further deduce the grid-pattern, which demonstrates the important role of geographic knowledge graph in spatial reasoning and its good adaptability in domain problem research, and provides ideas for the application of geographic knowledge graph in the field of spatial cognition.

The development of financial network has profoundly changed the form of knowledge spillover between cities and further affected the level of urban economic growth. The research on the externality of financial network has gradually become a research hotspot in financial geography. With the assistance of the data of headquarters and branches of enterprises in China's financial industry from 2005 to 2020, this paper combines the methods of social network analysis and recursion thought to calculate the centrality of financial network to analyze its spatial and temporal distribution pattern. Meanwhile, we use the OLS model to analyze the influence mechanism of financial network centrality on urban economic growth in China. The study finds that: (1) From 2005 to 2020, the centrality of financial network showed a "core-edge" distribution pattern, but the spatial agglomeration degree of financial network was decreasing. In this paper, with the help of the Core/Periphery algorithm in UCINET software, cities are divided into core and peripheral cities according to the 2020 financial network，and finally 52 cities with core status, such as Beijing, Guangzhou, and Hangzhou, and 239 cities with peripheral status, such as Anshan, Binzhou, and Foshan, are identified. Cities in the eastern region play an important role in the allocation of financial network resources, forming a "core-periphery" pattern with Beijing, Shanghai, and Shenzhen as the core cities and the other cities as the periphery cities； (2) The development of financial network can not only alleviate financing constraints, but also affect urban economic growth by promoting knowledge spillovers under the influence of network externalities. Cities with larger network links and better accessibility have higher levels of economic growth, and the knowledge spillover effect is more dependent on network proximity than location advantage in "place space"; (3) The degree of knowledge spillover promoted by financial network centrality shows spatial heterogeneity, and the core city which plays the role of "knowledge gatekeeper" can obtain greater benefits from network links. Due to the lack of "knowledge gatekeepers", peripheral cities are unable to make full use of external resources, which exacerbates the risk of being at the low end of the value chain and finally leads to the economic growth level of core cities much higher than that of peripheral cities. In the future, we should attach great importance to the construction of financial network and give full play to the driving role of financial network in urban economic growth in China.

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

Terrain music, the study of terrain feature lines, describes terrain undulation patterns in audible form. This paper takes terrain music as the starting point to simulate and quantify the morphology of terrain characteristic lines, and further discusses the spatial differentiation characteristics and laws of the terrain on the Loess Plateau. Based on the DEM data with a resolution of 30 m, 53 typical watersheds evenly distributed on the Loess Plateau in northern Shaanxi were selected as test sample areas, and the music theory knowledge, digital terrain analysis, and geostatistical theory were integrated. The watershed boundary profile was taken as the entry point to realize the digital expression of the land surface morphology. The Kriging method was used to construct a spatially differentiated map of terrain music index, so that the spatial distribution patterns and characteristics of the terrain could be further analyzed. On this basis, the spatial distribution of terrain music index on the Loess Plateau in northern Shaanxi and the comparative analysis with traditional indicators were further discussed. Results show that the terrain music index can quantitatively describe and reveal the spatial distribution characteristics of the terrain from multiple angles: ① The correlation coefficient between jump-in index and terrain undulation is -0.486, which quantitatively describes and reveals the spatial distribution characteristics of terrain from the degree of terrain undulation; ② The correlation coefficient between the grade progression index and slope is -0.328, which quantitatively describes and reveals the spatial distribution characteristics of terrain from the degree of slope of the terrain; ③ The correlation coefficient between the modal progression index and the profile curvature is -0.309, which quantitatively describes and reveals the spatial distribution characteristics of the terrain from the degree of terrain curvature. This study expands the research scope of digital terrain analysis and promotes the integration of music theory and geomorphology research, which further reveals that the application scope of terrain music related research methods in the field of geomorphology is different from that of traditional methods. This study examines the characteristics and internal mechanisms of spatial differentiation of terrain from the perspective of audibility, which deepens the understanding of development processes and internal mechanisms of terrain on the Loess Plateau.

With the development of science and technology, cyberspace has been deeply integrated with people's daily lives and represents a new spatial form of human activities. The cyberspace correlates to the real world, but on the other hand it also differs from it. Cyberspace has distinct geographical characteristics, and the spatial-temporal relationship in geograph remains an indispensable element in cyberspace. Therefore, it is of great significance to apply geographical thinking to the cognition of cyberspace in order to describe the situation of cyberspace and maintain cybersecurity. In this paper, we review the emergence and development of cyberspace, analyze the basic structure and characteristics of cyberspace, and examine the geographical properties of cyberspace based on different views of cyberspace. From the perspective of the three laws of geography, this paper discusses how to use geographical thinking and Geographic Information Science (GIS) methods to describe cyberspace, and takes the visualization of cyberspace, the construction of geographic knowledge graph of cyberspace, and the intelligent analysis of cyberspace behavior as examples to illustrate how to apply geographical thinking to the analysis and research of cyberspace. Exploring the geographical properties of cyberspace and applying geographical techniques to cyberspace protection can provide new insights into the comprehensive governance of cybersecurity, thus improving the cognitive level and governance capabilities of cyberspace in the new era.

Digital city is one of the main requirements of three-dimensional (3D) real scene and leads the direction of future smart city construction. Digital city usually uses the 3D model of the real scene as the spatial data volume and integrates the object-linked data of various sensors to achieve virtual-real fusion. The integration of spatial data volume and object-linked perception data is the key to digital city applications. Visual sensor is an important sensor type which is widely used in urban life, such as surveillance cameras, vehicles, and other devices. The key to digital city application is registering the visual sensors with virtual 3D model accurately. The purpose of spatial registration for visual sensor is to estimate or optimize the position and orientation of the visual sensor and to get the accurate spatial position of any object in the image. It is one of the key technologies for applications such as Augmented Reality and Video GIS. Currently, the spatial registration methods for visual sensors can be divided into hardware-based and vision-based methods. Due to the popularity of vision sensors, vision-based registration methods have been widely used. However, in digital city applications, seasons and weather always change, there are often large differences in appearance between the real image taken by visual sensor and the image of virtual scene. Therefore, the accuracy of outdoor 6 Degree of Freedom (DOF) position obtained by existing methods is usually insufficient, resulting in low registration accuracy of the visual sensor. In order to improve the accuracy of visual sensor spatial registration in digital city scene, this paper presents a method of virtual-real registration for digital city scene with structural semantic information in urban area. Firstly, the virtual perspective image of digital city scene is obtained, the plumb line which contains structural semantic information is extracted from the target image, and the properties of global constraints of the plumb line is used to restore the camera's position accurately and achieve the registration of monocular image in the virtual digital scene. Experiments show that this method achieves accurate registration of virtual and real images with large differences in appearance. Compared with the existing methods, the position and orientation errors are reduced by 35.9% and 39.3%. This method can effectively optimize the initial pose and improve the registration accuracy of visual sensors in digital city scene. A lightweight cloud-edge registration framework is designed and can be used in image geolocation tasks based on portable devices.

Geometric and semantic integration of 3D building models are important infrastructure data for smart city, they are conducive for promoting the refined management and intelligent application of building facilities. However, most of the existing point cloud-based modeling methods focus on the reconstruction of geometric models with simple roof structure, and semantic and topological relations are ignored. Moreover, these methods are sensitive to noise, which are difficult to assure topological consistency and geometric accuracy. To solve these problems, this paper proposes a 3D primitive fitting algorithm for automatically reconstructing building models with complex roof structure from point clouds. Firstly, a 3D building primitive library is designed, including various 3D building primitives with simple and complex roof types. Secondly, an individual building point cloud input is segmented into multiple planes using RANSAC algorithm. The Roof Topology Graph (RTG) is then generated according to the relationship of roof planes, and the roof type of point cloud is subsequently recognized by comparison of RTG between point cloud and building primitives. Thirdly, the reconstruction is formulated as an optimization problem that minimizes the Point-to-Mesh Distance (PMD) between the point cloud and the candidate meshed building primitive. The sequential quadratic programming optimization algorithm with necessary constraints is adopted to perform holistically primitive fitting, so as to estimate the shape and position parameters of a 3D primitive. Finally, the parameterized model is automatically converted into City Geography Markup Language (CityGML) building model based on the prior 3D building primitive. The generated CityGML LoD2 (second level of detail) models are different from mesh models created by conventional building modeling methods, which are represented with geometric, semantic, and topological information. To evaluate the quality and performance of the proposed approach, airborne lidar and photogrammetric building point clouds with different roof structures are collected from public datasets for test. Several building models with complex roof types are successfully reconstructed by using this approach, and the average PMD of five models is 0.17 m. The proposed algorithm is also compared with three other methods. Experimental results indicate that the proposed method achieves the best geometric accuracy, because the average PMD of each model is less than that of other methods. Moreover, this automatic primitive fitting method is efficient, and it is also robust to noise and local data missing. This study demonstrates that the resulting building models can well fit the input point cloud with topologic integrity and rich semantic. This method provides great potential for accurate and rapid reconstruction of geometric-semantic coherent building models with complex roof condition.

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.

Landform recognition has become a key part of geomorphological research, which has been widely concerned by scholars. The research of geomorphic units based on catchment has become a hotspot in the field of landform recognition. Previous studies have generated a series of new questions, such as whether large-scale landform types can be identified based on local catchment landform features, which landform description methods are more adaptable, and what is the knowledge bottleneck of current landform recognition methods based on the catchment. So, in this paper, we selected sample areas representing five major landform types in China, including karst, loess, periglacial, aeolian, and fluvial. Based on the complex network theory, we took the complex network indicators and the topographic metrics as the basic data sources. Three typical machine learning methods, i.e., LightGBM, XGBoost, and RF, were used to automatically identify the main geomorphic types in China. Results show that both the complex network structure and the terrain features of the catchment have certain explanatory power and recognition effect on landforms, and the overall recognition accuracy is 77.5% and 72.5%, respectively. Among the five geomorphologic types selected, LightGBM, XGBoost, and RF machine learning methods have the highest recognition accuracy (up to 100%) on periglacic geomorphology. Compared to a single geomorphic description data source, the geomorphic recognition effect that combines the two data sources is significantly improved. The overall accuracy using two data sources is 5% and 10% higher than that using the single complex network dataset and the single topographic dataset, respectively. Moreover, LightGBM has better adaptability to the combination of complex network and terrain factor feature sets, and the overall accuracy can reach 82.5%. In general, this study expands the application area and scope of catchment landform recognition methods, and provides a new idea for the research of catchment landform recognition.

Geological and mineral resource survey and scientific research in "geology, geophysics, geochemistry, and remote sensing " have established a large amount of geological and mineral survey data, which contain rich knowledge related to mineralization and distribution of gold mine, such as the metallogenic and tectonic setting, geological environment of occurrence, geological characteristics of mineral mine, genesis and metallogenic model of mine, and so on. The transformation from massive mineral related data to effective metallogenic knowledge has become one of the most important breakthroughs to improve the accuracy of geological prospecting. To solve this problem, through the in-depth analysis of knowledge representation, information extraction, and knowledge fusion in knowledge engineering, this paper explores the knowledge graph construction method of gold mine based on ontology. Firstly, referring to industry norms, gold mine knowledge base, and reference material of geological and mineral resource exploration, the metallogenic model of gold mine is sorted out, and the gold mine concept, gold mine entity, gold mine relationship, gold mine geological attribute, and gold mine metallogenic attribute are determined. In addition, the schema layer of gold mine knowledge graph is constructed by using the top-down ontology knowledge representation method, which represents the conceptual model and logical basis of gold mine knowledge graph. Secondly, based on structured, semi-structured, and unstructured multi-source heterogeneous geological data, the deep learning model is used to realize gold mine knowledge extraction, semantic analysis, and knowledge fusion, which enriches the data layer of gold mine knowledge graph and provides data support for gold mine knowledge graph. The gold mine knowledge graph is constructed in a bottom-up way, and the gold mine knowledge triplet is stored by Neo4j graph database, in which nodes represent gold mine concept, gold mine entity, and gold mine attribute value, while edges represent relation and attribute. Finally, the gold mine knowledge management system is developed based on the graph database. It can be applied to the management of gold mine data, acquisition of knowledge, visualization representation of gold mine knowledge graph, inquiry of knowledge, management and presentation of knowledge base, and other functions well, so as to lay a foundation for the intelligent analysis and mining of geological big data. This study develops a geological prospecting method driven by data and knowledge, and provides a reference for identifying, controlling, and managing mineral resources, which can improve the prospecting accuracy in geological exploration.

With the development of big data and artificial intelligence, the scope of digital earth modeling has extended to full-time holography beyond the earth surface. However, the current data model of digital earth still remains in the data modelling of earth tile or grid subdivision. This model severely limits the application of scenario-based and intelligent digital earth development. This paper proposes the concept of digital holographic earth and a corresponding data organization model of earth data cube. By using global multi-level grid reference system to describe and express multi-scale space and using two or three-dimensional grid cells to describe spatial positions, the traditional spatiotemporal description of "longitude, latitude, elevation, and time" is transformed to a new spatiotemporal description system of "time granularity, time coverage, grid position, and grid scale". The proposed model is characterized by the dimensions of "time-space-scale-attribute" based on spatiotemporal big data in the digital earth. The model encapsulates vectors, rasters, grids, time series arrays, and 3D models into an unified system. This unified system ensures that any data value of a specific earth data cube is aligned perfectly in time, space, and scale, which solves the problem of multi-dimensional or spatiotemporal dynamic fusion of big earth data. Finally, this paper develops a deep-time and spatiotemporal dynamic visualization simulation system to verify the data model based on the requirements of the Deep-time Digital Earth International Science Program.

Real-time identification of urban hotspot areas can improve the response ability of city managers on emergencies. With the development of the Internet of Things and communication technology, the starting and ending information of taxi trips can be uploaded to the data center in real-time, forming a massive and continuous data stream of pick-up and drop-off events. Taxi is a welcoming means of transportation, and have characteristics of all-weather operation, full regional coverage, and high spatial-temporal resolution, so its pick-up and drop-off data stream can be used as a high-quality data source for real-time identification of urban hotspots. However, the hotspots area identification methods aimed at historical data sets have a high delay and can’t meet the real-time requirement. At the same time, the existing clustering algorithm based on distributed streaming processing technology is difficult to meet all the requirements including low aggregation cost, good scalability, and supporting arbitrary shape cluster recognition when facing pick-up and drop-off streams. Based on the distributed stream processing technology, an urban hotspot area identification method suitable for taxi pick-up and drop-off data stream is designed in this study. By mapping the real-time pick-up and drop-off records to grid monitoring units, we can obtain the heat value of each monitoring unit for each time window, filter the monitoring units which have higher heat values than a specified threshold as hot units, and finally gather the hot units of same time window into hotspot areas. To avoid the performance bottleneck of the aggregation operator in distributed region identification, a multi-stage distributed hot area aggregating method is designed. The method is implemented on Apache Flink, and the pick-up and drop-off data stream is simulated with the historical taxi trip records from Wuhan and New York City. The results show that: (1) The spatial distribution and status of hotspots differ from time to time, which is related to citizens' activities at different times; (2) Using smaller monitoring units can get finer spatial positions of each hotspot area; (3) Our method can accurately identify the hotspot areas using different parameter pair of monitoring unit sizes and hotspot thresholds; (4) The method has excellent throughput which increases with the computing parallelism and reaches 90k/s with parallelism at 8. The proposed method can correctly capture the spatial distribution of urban hotspot areas of each period in real-time and has good performance and scalability.

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

The multispectral LiDAR system can simultaneously provide the 3D space and spectral information of the target ground object, which is convenient for ground object recognition, land cover/use classification, and scene understanding. However, most multispectral LiDAR point cloud classification methods cannot fully mine the geometric information of point clouds and achieve poor performance in fine-scale classification. To overcome this limitation, this paper presents a continuous kernel point convolutional network which uses local point cloud geometric information to enhance features. Firstly, the network combines a random sampling with a farthest point sampling to quickly process large-scale multispectral LiDAR point clouds. Then, an enhanced convolution module based on continuous variable convolution is designed to improve the semantic information expression of multispectral LiDAR point cloud data. In order to address the problem that kernel point convolution simply using the distance relationship between the geometric space and feature space of neighboring points and centroids is insufficient to express the local information as a complementary feature of the kernel point convolution network, the local features given to the kernel points are enhanced by using the position relationship between neighboring points and centroids while aggregating the local features to provide richer semantic information for the multispectral LiDAR point cloud classification network. Finally, the weighted label smoothing loss and the Lovasz-Softmax loss are combined to further improve the classification performance. The results on the Titan multispectral LiDAR dataset show that the proposed network achieves an overall accuracy of 96.80%, a macro-F₁ index of 88.51%, and a mIoU value of 83.42%, which is superior to the state-of-the-art (SOTA) multispectral LiDAR data networks. The proposed model uses the combination of grid sampling and KD-Tree to better preserve the geometric features of the original point cloud. In the case of a single batch of 65,536 points, the point cloud sampling time is reduced by 28 261.79 ms compared with similar multispectral LiDAR point cloud classification networks. This Study demonstrates the potential of enhanced feature kernel points convolutional network for multispectral LiDAR point cloud classification tasks.

While rapid urbanization endows people with a modern life, it also brings many urban diseases such as traffic congestion and uneven distribution of resources. Taxi is one of the main transportation methods for urban residents. Taxi data effectively record the spatial and temporal information of residents' travel and can be widely used for residents' travel characteristics mining. Analyzing residents' travel characteristics is an important way to solve and alleviate the increasingly prominent urban problems. At present, rich research results have been achieved in mining residents' travel characteristics using taxi OD flow data. Cluster analysis, which is based on taxi OD flow data, represents one of the primary methods for uncovering the travel characteristics of residents. But most of the studies ignore the semantic information of OD flow. Urban POI data is an important data support for semantic extraction of OD flow, and semantic information can be extracted by studying the relationship between OD flow and POI. To address the problem of insufficient consideration of semantic information in spatiotemporal clustering algorithms, a method for extracting semantics of OD flow based on Global Vectors (GloVe) model and density based spatiotemporal semantic clustering algorithm (STS DBSC AN, Spatial Temporal Semantic DBSCAN) is proposed in this paper. Firstly, OD flow semantics are extracted by combining POI visiting probability and GloVe model, the GloVe model not only fully considers the local geographic context information of POIs, but also takes into account its global statistical information in the corpus. Based on this, a spatiotemporal semantic similarity measurement rule for OD flow is proposed, which comprehensively considers temporal, spatial, and semantic information. Then, the DBSCAN clustering algorithm is improved according to the spatiotemporal semantic similarity measurement rule, and the spatiotemporal semantic clustering of OD flow data is realized. Finally, analysis of travel characteristics of residents in Xiamen island based on OD flow semantics and spatiotemporal semantic clustering, and a total of seven types of residents' travel semantics are extracted. Results show that: 1) Residents' travel semantics are influenced by the time factor, and the main residents’ travel semantics are different in different time periods; 2) residents' travel hotspots are mainly distributed in the central developed area of Xiamen Island; 3) seven typical residents' travel patterns are extracted from four main residents' travel semantics through spatiotemporal semantic clustering analysis. The results demonstrate that OD flow semantic and the spatiotemporal semantic clustering method can effectively mine the travel characteristics of urban residents.

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

Ningxia is one of the first provinces in China that has finished the delineation of Ecological Conservation Red Line (ECRL). Nearly one fourth of Ningxia has been included in ECRL, significantly contributing to maintain the ecological security of the northern region. However, there is a lack of relevant research on the evaluation of the contribution of the ecological barrier and the protection effect of the eco-redline in Ningxia. In this paper, based on the equivalent value of Ningxia ecosystem service base and land use data at a 2-m resolution, we objectively evaluated the Ecosystem Service Value (ESV) in 2020 and its changes between 2018-2020, and analyzed the effect of the ECRL delineation. Consequently, a critical direction for ecological protection goals is emphasized for this autonomous region. Our findings include: (1) In 2020, the total natural ecological assets stock in Ningxia was 3.32 million hectares, which mainly consists of forest, grasslands, and wetlands, providing a ESV of around 88.90 billion yuan for the region. During the period of 2018—2020, there was an increase of ESV (106 million yuan); (2) The ECRL area ( one fourth of the regional area) contributed more than one third of the ESV in the entire region, and 63% of the increase in ESV during the evaluation period was attributed to the ECRL area; (3) Meadows provided 52% of ESV in the entire region, with only 31% located in ECRL area. During 2018—2020, meadow ecosystems also faced problems such as stock reduction, desertification, and degradation. Increasing the meadows ecosystem stock and quality is the key factor for the incremental value of ecosystem services; (4) It is suggested to follow the internal mechanism of ecosystem structure and process, center on the regional dominant ecological function and the characteristics of the ecosystem structure, systematically carry out ecological protection and restoration based on local conditions.