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

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

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.

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.

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.

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.

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.

Extraction of ship routes and analysis of traffic flow are the basis of route design, maritime management, and trade analysis. Based on the massive ship trajectory data, existing ship route extraction methods still have limitations in terms of adaptability to large sea areas, model complexity, and consistency with real maritime traffic routes. In this study, we propose a ship trajectory extraction model and traffic flow analysis based on massive ship Automatic Identification System (AIS) data. Firstly, the ship's navigation trajectory is abstracted as the combination of a ship's stay points (stop points) and movement points (waypoints). Stop points represent the characteristic of a ship's stop trajectory at the port, and the waypoint represents the ship's moving trajectory characteristic, e.g., the sailing speed or sailing angle changes significantly. The navigation trajectory abstraction model denoted as "departure port (stop point)→movement (waypoint)→destination port (stop point)" of a ship is constructed, enabling the division of ship navigation trajectory. Secondly, based on the abstract model of ship navigation trajectory, further clustering of stop points and waypoints of the massive ship navigation trajectory based on graph theory is implemented to extract route points (stop points and waypoints) of ships. Finally, a route point connection matrix is established to realize ship route extraction in the form of ship traffic map, which provides a new method for ship route extraction and traffic flow analysis. Taking the Silk Road area in the South China Sea as the study area, the historical AIS data for the entire year of 2017 are used to analyze the traffic flow characteristics and ship routes of typical merchant ships (i.e., container ships, bulk carriers, and oil tankers). The results show that the extracted ship routes are highly consistent with the maritime traffic in high-density areas and can reflect the real maritime traffic routes. Compared with the existing shipping route data, the details of extracted routes are enriched, and the structure is more comprehensive. In addition, compared with the existing shipping route extraction methods, the proposed method has two following advantages. First, the shipping routes extracted by the proposed method have greater richness. The proposed method not only extracts the shipping routes but also identifies the characteristic points during ship navigation, i.e., the stopping points and the waypoints of the ship route, which provides important knowledge support for route design and navigation safety. Second, the extracted routes can be easily applied to analyze the maritime traffic network. Since the extracted routes are in the form of point-to-point starting from the port, it is easy to construct a maritime traffic network, allowing for potential transportation network analysis. This study contributes decision-making support for ship route planning, traffic flow analysis, navigation safety, etc.

The 15-minute community-life circle is defined as an ideal geographical setup where most human needs are located within a 15-minutes travel distance. This concept represents a new trend for green, low-carbon, and sustainable urban development. However, there are challenges in building 15-minutes community life circles effectively, efficiently, and equality, such as how many service facilities are required, where to locate the facilities, and how to promote service efficiency and equality for urban residents. In this paper, a novel facility location model, DC-CFLP, is proposed for facility site selection within the 15-minute community-life circle and addressing the aforementioned facility planning challenges. First, we use a maximum service radius, a recommended service radius, a minimum percentage of demand covered by facilities within the recommended service radius, and an optional maximum number of facilities to extend the single-source facility location problem (SSCFLP). Second, the constraints on the facility capacity, such as maximum service radius and minimum percentage, are formulated as soft constraints, which are addressed through penalties in the objective function. The proposed model is tested in Zhengdong New District, Zhengzhou, China. Based on the geographic condition of the region and its detailed urban planning maps for the year of 2035, three different scenarios are designed for locating the community convenience service centers, each with three sets of planning parameters. In this case study, we solve nine model instances using a metaheuristic algorithm. The planning results show that our proposed model can efficiently select facility locations that satisfy planning criteria, including balancing service demand and supply, minimizing cost service and travel costs, achieving a minimum percentage of demand covered by facilities within the recommended service radius, and reducing spatial inequality of service, while considering service cost, accessibility, and equity. Based on the model results, several planning recommendations are provided for the study area. Moreover, to demonstrate the superiority of the proposed model, we conduct comparison experiments with the SSCFLP, the Capacitated P-Median Problem (CPMP), and the DC-CFLP. These experiments show that the facility location selection using SSCFLP is the most efficient, the CPMP results are largely dependent on the number of facilities, and the DC-CFLP results archive a better balance between service quality, cost, and equality. In conclusion, our study demonstrates that our proposed model is a planning-parameter-driven, efficiency-equality balanced model, and highly flexible for the site selection of public facilities.

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

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

Many large cities have been actively promoting the policy of "replacing oil with gas" for taxis. Taxis are converted from traditional gasoline consumption to Compressed Natural Gas (CNG) to achieve energy conservation and emission reduction goals. To accurately evaluate the carbon dioxide (CO₂) emission reduction benefits of CNG taxis, taking Wuhan as an example, a vehicle microscopic CO₂ emission model based on deep learning method and trajectory data was proposed to investigate the spatial-temporal characteristics of CO₂ emissions of taxis under different fuel scenarios. Considering the driving feature sequence and fuel type of vehicles, the Portable Emission Measurement System (PEMS) was used to collect vehicle CO₂ emission data in the road test experiment, then we constructed a vehicle microscopic CO₂ emission model by the BiLSTM algorithm and further verified its accuracy. Based on the proposed CO₂ emission model and the trajectory data of 15 752 Wuhan taxis, the CO₂ emissions throughout the entire lifecycle of urban taxis by 92# gasoline and CNG were estimated respectively to quantify the CO₂ emission reduction benefits of CNG taxis. The results show that the proposed model had a higher accuracy than common regression algorithms such as SVR and LSTM, and the predictions matched well with real vehicle CO₂ emission changes, meeting the accuracy for a large-scale estimation of urban taxi CO₂ emissions. In addition, the accuracy of taxi CO₂ emission estimation based on deep learning methods was also higher than that of physical microscopic models such as IVE and CMEM. Especially, when using CNG as vehicle fuel, the physical models had significant computational errors due to not involving technical parameters. The empirical results show that, taxi CO₂ emissions using CNG were reduced by 22.05% during the PTW process and by 49.45% during the WTP process, compared to emissions using 92 # gasoline. Our results reveal both the temporal and spatial patterns of taxi CO₂ emission as well as the CO₂ emission reduction benefits of CNG taxis. The outperformance of deep learning methods over other methods for estimating vehicle CO₂ emissions provides new ideas for large-scale and high-precision estimation of vehicle emissions. The CO₂ emission reduction benefits of using CNG as fuel in taxis are significant, which provides a reference for the government to formulate relevant energy-saving and CO₂ emission reduction policies.

Spatiotemporal crime prediction often employs quantitative techniques such as Geographic Information Systems (GIS), geo-statistics, and big data analysis to predict the time and risk area (or location) of crime events that are more likely to occur in the future. In the era of big data, how to dynamically optimize the deployment of limited police forces and successfully improve the effectiveness of crime prevention based on data-driven crime predictions is a research focus in the field of global predictive policing. It is also a main practical direction for law enforcement agencies worldwide to implement modern proactive policing models. Traditional crime geography and spatiotemporal crime prediction methods mainly use police precincts or grids as the basic spatial analysis unit, and the analysis results are not conducive to guiding refined patrol force planning and deployment. The graph neural network based on deep learning can be combined with the topological structure of the road network at the micro scale, enabling precise crime prediction at the street scale. However, existing approaches rarely consider the impact of road weights in model prediction. In this paper, a Road Weighted Spatiotemporal Graph Convolutional Network (RW-STGCN) is constructed for street crime prediction by introducing road network accessibility and distance attenuation factors, and the model is evaluated using street theft crime data of Chicago. The results show that: (1) Compared to the spatiotemporal graph convolutional networks without considering road weights, the hit rate of the RW-STGCN increases by more than 6.5% across various road network coverage ratios (1%, 5%, 10%, and 20%), and the increase becomes more significant as the coverage ratio decreases, with a maximum increase exceeding 50%. This indicates the effectiveness and superiority of the RW-STGCN for smaller units; (2) Model ablation experiments show that the hit rate of the RW-STGCN considering road weights increases by 13.5% compared to the model result without considering road weights, and the model considering both road weights has a more significant improvement in prediction performance than the model considering only a single factor of distance attenuation weight or road network access weight, with a maximum increase of 12.9% in hit rate. This suggests that deep learning methods combined with geographic features can effectively improve the accuracy of crime prediction. The RW-STGCN is helpful for street crime prediction and can provide auxiliary decision support for law enforcement agencies to conduct scientific patrol planning and police force deployment based on road networks. In addition, it is also useful for the study of road-related urban computing problems.

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.

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

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

Based on the Point of Interest (POI) big data of the catering industry and related service industries in Guangzhou in 2021, this study analyzes the spatial distribution characteristics, influencing factors, and the spatial spillover effects of the overall and subdivided catering industry in Guangzhou based on the methods of HDBSCAN clustering, Collaborative Location Quotient, and Spatial Durbin Error Model. The study mainly explores the overall and local spatial relationships between the catering industry and surrounding service industries. The results show that: 1) Different catering density areas show variations in the spatial distribution of the catering industry in Guangzhou. The catering industry in high-density areas is characterized by a muti-central agglomeration pattern, while the catering industry in low-density areas is characterized by central agglomeration with surrounding radiation. The local distribution of restaurants are related to population features, such as population density, population education level, and so on; 2) There are variations in the spatial correlation between the catering industry and its surrounding service industries across different catering density areas. Areas with high catering density have the strongest spatial correlation, while areas with moderate catering density have the weakest spatial correlation; 3) the influence of surrounding service industries on different types of catering industry also varies. In general, the spatial correlation strength from strong to weak is as follows: fast restaurants, dinner restaurants, snack bars, and cold beverage shops. The local spatial differences are similar but the spatial difference of dining restaurants is the most obvious; 4) The estimation results of the Spatial Durbin Error Model at the street-town scale show that transportation facilities services, shopping services, and population size have the most direct impact on the POI number of local catering industries, with obvious spatial spillover effects. Both the population size and surrounding service industries are the main factors that influence the spatial distribution of fast food restaurants, while dinner restaurants, snack bars, beverage shops, and other catering industries are easily affected by transportation facilities and shopping services. In general, from the perspective of spatial correlation, this study deepens the research on the location theory of service industries. It also provides references for the study of commercial geography and the optimization and adjustment of the spatial pattern of the catering industry in big cities.

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

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

OpenStreetMap (OSM) road network is an open-source dataset that is dedicated to providing people with a globe-free digital map resource, and it has been widely used in spatial analysis and applications such as route planning and vehicle navigation services. Meanwhile, in order to regulate traffic order and reduce heavy traffic congestion, the constraints of turning rules are generally implemented at intersections in urban areas. These constraints should be respected in the applications based on OSM road network. However, OSM road network lacks turning relationships at intersections, preventing its services from route planning and vehicle navigation. For this reason, to endow OSM road network structure with turning relationships, this study presents an intersection turning detection method based on map matching and string mapping, which takes advantage of crowdsourcing GNSS trajectory data in terms of its dynamic connection information at traffic intersections. Firstly, a structure detection method for OSM intersections is designed based on a top-down quadtree splitting idea, then the intersections with different sizes and complex and various shapes are reduced to the connection points. On this basis, the improved Hidden Markov Model (HMM) map matching algorithm is introduced to project low-frequency and high-noise trajectories onto OSM road segments. This algorithm considers the direction consistency between roads and trajectories, as well as the effective drift distance between two adjacent trajectory points, can identify trajectory sequences with semantic anomalies during the driving process. Secondly, to simplify turning relationship detection, a character encoding technology facing the intersection-related road segments is presented to map the trajectories crossing through intersections to the directional strings in turning process. The information enhancement method regarding the empty characters based on optimal path analysis is further designed to enhance turning trajectory support for short road segments. This approach helps restore the driving route information for low-frequency trajectories. Finally, the different turn modes of trajectories at the target intersection are mined by directly targeting the trajectory directional strings based on a string matching method, thus this study realizes turning information enhancement for OSM intersections referencing to the "consensus knowledge" of crowdsourcing trajectories. The complicated turning relationship identification for OSM intersections is transformed into the simple string matching. The experiment based on crowdsourcing trajectory data in Shanghai shows that the proposed method can detect turning relationships for OSM intersections with a precision rate of 90%, a recall rate of over 98%, and an F1-score of over 94%.

Grid is a data organization model for geographic environment digitization. Utilizing a grid model to model geographic environment is a basis for deducing, analyzing, and deciding important events from a global perspective. Traditional local projection-based grids have disadvantages including small range, limitation in gloal extrapolation, and large grid deformation. Most of existing new spherical grid models, namely Discrete Global Grid Systems (DGGS), only focus on the grid centers, ignoring roles of grid vertexs and edges. Therefore, the existing functions of DGGSs are limited to data modeling and are challenging in the caculation of location-based events. Herein, a unique description model for multi-structural elements (i.e. center, vertex, edge of a cell) of a hexagonal DGGS is established mathematically, and the coding and operation methods are designed accordingly. Then, geographic environment modeling with DGGS and route caculation experiments are designed to demonstrate the feasibility and superiority of our methods. The results show that the hexagonal DGGS proposed in this study has the advantages of small grid deformation and concentrated deformation distribution compared with the projection-based plane grids. The error rate of area, perimeter, and angle deformation of hexagonal grids is less than 7.5%, the error rate of area and perimeter deformation of Lambert Conformal Conic projection grids is more than 10% in mid and low latitude region, and the error rate of Mercator projection grids is more than 60% in high latitude region. Compared with the single cell model, multi structure elements can accurately express environmental information according to the event caculation requirements, which is more conducive to calculation of event results accurately. The methods proposed in this study have good application prospects in the field of geographic environment modeling and event caculation in a wide range and across the global scale.

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

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

The measure of similarity of the building shape is crucial to the cartographic generalization process. Its research provides information on the contour of the building as a foundation for map analysis and the identification of spatial elements. Moreover, it is applied in many aspects, such as shape matching, shape retrieval, building simplification and building selection. With the development of neural networks, graph contrastive learning learns more discriminative representations by comparing positive samples from the same graph with negative samples from different graphs. Based on the advantages of the graph contrastive learning model,the study proposes a building shape similarity measurement model with the support of graph contrastive learning model, which aims to train a graph encoder to narrow the difference between positive samples and increase the gap between negative samples.The contrastive loss function and graph augmentation strategy are used to implement this operation. The following is the model's implementation process. Firstly, the vector building shapes are converted to the graph data structure and the point and edge features of the shapes are extracted.Secondly, two distinct views are generated as input to the encoder by applying various augmentation means, such as node dropping, edge removing, edge adding, and feature masking, to each graph. After that, the augmented graphs are then given to the graph encoder, which establishes each graph's feature encoding through the training process. Finally, the shape classification is achieved by a nonlinear classifier, and the extracted shape coding can be used to study shape similarities. The results indicated the shape classification accuracy of 96.7% using OSM shape data as training and testing samples. Furthermore, feature and node direction analysis, graph augmentation analysis, and parameter sensitivity analysis were carried out.The experimental results show that the classification accuracy rates of the HU moment method, Fourier method, and GCAE method are 22.9%, 44.4%, and 92.5%, respectively. Therefore, the method proposed in this paper outperforms traditional methods and deep learning in shape recognition capability.With a 95.7% shape classification accuracy, three areas of Hong Kong were chosen for shape matching and shape classification. And conducted shape matching tests on 9 typical shapes, finding that the similarity values of similar shapes were much greater than those of dissimilar shapes, consistent with visual perception.The graph contrastive learning model has effectively enhanced the recognition capability of complex shapes, providing technical support for applications such as cartographic generalization, spatial queries, shape matching, and shape retrieval.

To meet the challenges posed by the "fragmented" nature of ecological governance, it is crucial to have a holistic understanding of the vulnerability of basin ecosystems and the underlying patterns of their evolution for comprehensive ecological management. The Ulansuhai Basin, serving as a pilot site for a representative ecological conservation and restoration project, has experienced a shift from traditional "lake management" to a more systemic "basin management". This study selected the Ulansuhai Basin as the study area and established a "Sensitivity-Recovery-Pressure" index system for ecological vulnerability assessment. Using the Google Earth Engine cloud platform, methods including Mann-Kendall test, Sen+Mann-Kendall trend analysis, and transformation trajectory were applied to analyse the spatiotemporal evolution of ecological vulnerability in the Ulansuhai Basin from 2000 to 2020. Moreover, geographical detectors were utilized to further investigate the causes of ecological vulnerability in the Ulansuhai Basin, and to identify the primary driving factors. The results indicated that the ecological vulnerability of the Ulansuhai Basin exhibited a “low in the middle, high on both sides” pattern, e.g., the western region of the Ulan Buh Desert and the eastern Ula Mountain region had relatively higher ecological vulnerability, while the central Hetao irrigation area had lower ecological vulnerability. From 2000 to 2020, the basin’s ecological vulnerability grading index increased from 2.44 to 2.59, indicating a slight decline in ecological vulnerability. Abrupt changes of vulnerability were observed in 2000, 2009, 2013, 2017, and 2020, with approximately 59.82% of the basin area maintaining stable ecological vulnerability grades throughout the assessment period. The transformation trajectory method showed that the decline of vulnerability peaked in 2013 and then began to slow after 2017. Additionally, surface aridity was identified as a key driving factor of ecological vulnerability in the Ulansuhai Basin, and the interaction of multiple factors showed a stronger explanatory power for ecological vulnerability than a single factor. Generally, land cover type exhibited the most significant explanatory power, followed by meteorological and economic types. This study analyzed the long-term spatiotemporal changes and causes of ecological vulnerability in basin ecosystems, providing a scientific basis for the assessment and governance of ecological conservation and restoration projects in ecologically vulnerable regions.

Remote sensing images contain rich semantic information and play an important role in landslide disaster monitoring. Traditional landslide recognition is mainly based on remote sensing visual interpretation and human-computer interaction recognition, which is time and labor consuming, with strong subjectivity and low extraction accuracy. Semantic segmentation, as an important task in deep learning, has played an important role in automatic recognition tasks using remote sensing images due to its end-to-end, pixel-level classification capability and has great potential in automatic recognition of landslides. The existing semantic segmentation models for landslides using remote sensing images usually lack the feature information of multi-scale ground objects, and the boundary will be blurred with the increase of network depth. In this paper, Attention combined with Encoder-Decoder Network (AED-Net) is proposed for landslide recognition. A shallow feature extraction network is used to alleviate the boundary ambiguity caused by deep neural network. Multi-scale feature extraction capability of convolution pool pyramid structure in void space is utilized. Combined with the feature restoring ability of the encoder-decoder structure, the boundary information is restored, and the channel attention mechanism is used to enhance the key feature learning ability of the model. The focal-loss function is used to alleviate the imbalance of positive and negative samples. In our study, firstly, the GID-5 data set is used to conduct comparative tests on the expansion rate setting of void convolutions and the selection of channel attention mechanism in the model to get the optimal solution. Then, the feature weight is transferred to the semantic segmentation task for landslide disaster by using transfer learning method, and the hyperparameter discussion and ablation experiment are carried out. The resulting model achieves the optimal segmentation performance on the landslide disaster data set of Bijie City, with a Pixel Accuracy (PA) of 95.58%, the Mean Pixel Accuracy (MPA) of 89.24%, and the Mean Intersection over Union (MIoU) of 82.68%. Compared with classical semantic segmentation networks such as PSP-Net, Attention U-Net, DeeplabV3+ with ECA attention mechanism, and semantic segmentation models such as PA-Fov and LandsNet for classfifying landslide disasters, the pixel accuracy of our model increases by 0.73%~1.97%. The average pixel accuracy of all categories increases by 1.0%~2.84%, and the average interaction ratio increases by 2.25%~5.11%. Moreover, the edge information of landslide image is smoother and the multi-scale landslide segmentation accuracy is better than other deep learning models, which demonstrates the effectiveness of the proposed model in semantic segmentation of landslides from remote sensing images.

The degradation of cultivated land has profound implications for regional economic development, ecological security, and food security. Notably, the Yellow River Delta (YRD) is one of China's major grain-producing regions. Studying the degradation status and clarifying the driving mechanism of cultivated land in the YRD is crucial for ensuring regional food security and promoting cultivated land protection. Based on the MODIS NDVI data from 2001 to 2021, this paper uses the Breaks For Additive Seasonal and Trend (BFAST) method to explore the degradation status of cultivated land in the YRD. The study also uses the Geographic Detector to investigate the impact and driving mechanism of various natural and human factors and their interactions on the spatial differentiation of the degradation status of cultivated land at different scales, aiming to provide decision support for cropland protection and human activity regulation in the YRD. The results show that: (1) Compared to the linear trend, the non-linear trend method has higher accuracy in detecting the degradation status of cultivated land. It not only detects the overall trend of cultivated land changes but also detects phase-change information in long-term changes, which can comprehensively and accurately evaluate the degradation status of cultivated land; (2) The BFAST detection results indicate that 31.75% of cultivated land in the YRD is undergoing degradation, 61.06% is expirencing improvoment, and 7.19% exhibits non-significant trend. Most of the cultivated land in the YRD exhibits short-term fluctuations due to external disturbances in the long-term change process, e.g., 31.31% of these fluctuations are interrupted decreases, mainly distributed in areas such as Gubei Reservoir, the western side of the old Yellow River, and the southeastern coast. They show a spatial distribution pattern that gradually decreases and becomes more scattered from the coastal areas to the inland. The interruption increase accounted for 55.13%, mainly distributed in the southern region of the Yellow River, freshwater river areas, and near reservoirs, showing a large-scale concentrated distribution pattern; (3) The spatial differentiation of the degradation status of cultivated land at different scales in the YRD is primarily driven by land use, with significantly higher influence from interactive effect of two factors compared to their isolated effects. Specifically, land use ∩ elevation and distance to the sea ∩ land use are the dominant interactive factors affecting the degradation and improvement status of cultivated land, and the spatial differentiation of the degradation status of cultivated land in the YRD results from the dominant human-driven factors and the collaborative effect of natural factors.

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

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