[Background] The emergence of Large Language Models (LLMs) has driven a profound transformation in the interaction paradigm of Geographic Information System (GIS) spatial analysis. This evolution has progressed from the early mode in which users directly wrote code to drive the analysis, through an intermediate mode in which code was indirectly invoked via GUI toolchain configuration, and is now shifting toward a new paradigm of "natural language-code-spatial analysis." Under this paradigm, natural language and program code jointly take the stage as the media driving GIS spatial analysis. However, the two differ in the structural stability of execution, the clarity of spatial semantics, and the transmissibility of the analytical process; discerning which of them constitutes the ontology of contemporary GIS spatial analysis is therefore essential for clarifying the optimization direction of LLM-driven GIS spatial analysis. [Analysis] Against this background, this paper advances a systematic argument for the ontological positioning of GIS spatial analysis code. It first defines the conceptual connotation of GIS spatial analysis code, identifies five functional types, and characterizes the capability boundaries across four categories of execution platforms, namely local general-purpose programming environments, spatial resource-hosting cloud platforms, database-embedded environments, and knowledge graph environments. It then explicates the driving mechanism of code from three dimensions—structural stability, semantic anchoring, and knowledge transmissibility—and argues that code constitutes the minimum complete unit upon which the scientific validity of spatial analysis is established, thereby affirming its ontological status within the GIS spatial analysis system. [Progress and Prospects] Building on this foundation, the paper reviews recent research progress centered on GIS spatial analysis code in the LLM era, and dialectically discusses two categories of research directions. The first category comprises directions that are currently feasible yet have not been systematically investigated, including spatial-semantic structural perception, domain-adaptive learning, autonomous agent ecosystems, knowledge transfer and accumulation, and code localization and repair. The second category comprises directions that still lack foundational support but are urgently worth exploring, including data and knowledge governance, spatial computing capability, causal explanation capability, and geospatial representation models. [Purpose] Through a systematic analysis organized around the driving mechanism, this paper delineates the theoretical role and methodological significance of GIS spatial analysis code in the LLM era, elucidates the theoretical framework and capability boundaries of this field, and provides a theoretical reference for research on intelligent GIS.

[Objectives] The national strategy of "people-oriented new urbanization" places urgent demands on precisely understanding urban residents' space-time behavior patterns, driving a paradigm shift in Geographical Information Science (GIS) from a static, "place-centric" analytical framework towards a dynamic, "people-centric" one. This paper aims to address this strategic demand by establishing a theoretical and methodological framework, named "Behavioral Geocomputation". [Analysis] It first defines the core research object as continuous and proactive human spatiotemporal behavior with multi-dimensional semantic features in physical-virtual hybrid spaces. Building upon this, the paper establishes a comprehensive theoretical framework. Furthermore, by synthesizing the author's research practices, it reviews frontier advancements in key areas including spatiotemporal data modeling, behavioral object processing and mining, space-time behavior simulation, and people-centered urban system evaluation and optimization. These advancements of behavioral geocomputation thereby validates the feasibility and academic value of the proposed research domain. [Prospect] Finally, the paper envisions the broad prospects of behavioral geocomputation in integrating GeoAI, empowering related disciplines, and serving people-centered urban planning and management.

[Significance] In recent years, the emergence of Large Language Models (LLMs) has provided a new technological foundation for reconfiguring human-machine interaction centered on language. Traditional interaction models mainly rely on graphical interfaces and rule-based operations. In contrast, LLMs demonstrate strong capabilities in semantic understanding, task abstraction, and reasoning. This enables language to function as a sophisticated interface for organizing complex analytical processes. The integration of LLMs with spatial information technology is still at an experimental stage. However, related studies have begun to show their potential in the understanding, reasoning, and analysis of geospatial tasks. These studies suggest that LLMs may offer new opportunities for organizing domain knowledge. They also indicate potential for coordinating multi-step geospatial tasks. Together, these advances open up new directions for the intelligent development of geographic information systems. [Progress] To systematically explore the applicability of LLMs to complex geospatial tasks and the key challenges they face, this paper reviews academic literature related to geospatial language model (Geo-LLM) published since 2023. The reviewed studies represent recent attempts to integrate LLMs into different types of geospatial analysis scenarios. Specifically, this paper conducts the following work. First, it collects research cases of LLMs in geospatial tasks. Secondly, it elaborates on the capabilities and system functions of Geo-LLM. Then, it summarizes the key technologies for the collaboration between LLMs and domain knowledge, spatial data, and analysis tools. This also reflects the common strategies of Geo-LLM in integrating various resources in the workflow. These technologies include: enhancing the domain cognition ability of the model through the infusion of geographical knowledge; strengthening the data processing capability of the model by integrating spatial data and tools; and optimizing the reasoning and decision-making capabilities of the model in complex spatial analysis tasks through the design of reasoning chains and task planning mechanisms. [Prospects] Based on these findings, this paper further discusses the challenges and future prospects of deeply integrating LLMs with spatial technologies. This discussion is conducted from several perspectives, including multi-source data organization, agent workflow management, language model spatial cognition and reasoning, evaluation system, and geo-foundation model construction. This work focuses on the construction of geographically intelligent systems that are applicable, interpretable, and controllable. The aim is to enhance the deep integration between generative artificial intelligence and geographical tasks, with the expectation of further promoting the development of geospatial artificial intelligence.

[Significance] Natural resource monitoring primarily involves detecting and supervising changes in natural resources themselves and those caused by human activities, providing a basis for natural resource management and scientific decision. The coordination among various current monitoring methods needs to be further strengthened. There are issues such as an emphasis on macro-level monitoring, insufficient precision in monitoring local or key areas, and the inability to establish a unified spatial reference for real-scene monitoring and 3D modeling. [Progress] This article introduces the main components of the sky and earth intelligent perception and real-scene monitoring system for natural resources. Intelligent perception and real-scene monitoring of natural resources primarily rely on integrating multi-level and multi-scale monitoring methods such as satellite remote sensing, drone monitoring, ground-based perception monitoring and GeoAI to achieve real-time monitoring and precise analysis of natural resources and their changes. And also, it is discussed that several key technologies and applications, including remote sensing image multiple elements of natural resources change detection based on the improved spatiotemporal dual self-attention feature model Res2Net, drone inspection and monitoring mode combined with improved genetic method for flight path planning, tower-based video target detection and localization, as well as point deployment optimization, and real-time video and 3D geographic scene fusion and parameter calculation based on the view cone. [Prospect] In the future, with the further removal of barriers in natural resource business, the maturity of technologies such as weakly supervised/unsupervised remote sensing deep learning, multi-modal fusion and collaboration, and intelligent agents, it is expected to establish a unified monitoring system and intelligent perception platform for various elements of "mountains, rivers, forests, fields, lakes, grasslands, and sands".

[Significance] The 3D real-scene models are evolving from merely "representing reality" in the past to "connecting reality, cognizing reality, and even foreseeing reality" in the present. With the construction of the national 3D real scene of China, the integrated application paradigm of "3D Real-Scene +" has applied in numerous industries. As a critical part of national new infrastructure, 3D real-scene models hold significant value for scientific research in areas such as geospatial cognition, virtual geographic environments, and spatiotemporal computing, as well as for national strategies and societal needs such as the "Digital China" and the "Digital economy". [Analysis] This paper provides a systematic review of the conceptual framework, core methodology, application scenarios, and challenges of 3D real-scene models. First, this paper systematically reviews and synthesizes the concepts, connotations, and main types of 3D real-scene models, proposing the concepts of primary 3D real-scene models and secondary 3D real-scene models to facilitate their in-depth application. The research scope of 3D real-scene models is summarized across five levels: the data layer, model layer, platform layer, application layer, and theory-standard layer. Second, this paper comprehensively outlines the core methodology currently from the perspective of primary 3D real-scene modeling, secondary 3D real-scene modeling, and model optimization and assessment. The state-of-the-art techniques in 3D real-scene and their development directions were systematically summarized as well. Next, this paper systematically reviews the application scenarios of 3D real-scene models. The application scenarios were summarized into four dimensions of spatio-temporal base, information extraction, connecting reality, and industry empowerment. Industrial empowerment is the core purpose of 3D real scene models. The industrial empowerment can be achieved through four paths: business data visualization and fusion in 3D real scene models, business decision-making enhancement by 3D analysis, process business reengineering by online 3D platform, and AI agent decision-making. Finally, the main issues and challenges currently facing the 3D real-scene models are summarized from the perspectives of the paradigm transformation of geographic information cognitive, spatiotemporal dynamic modeling of complex scene, intelligent transformation, and in-depth application in industries. Additionally, the future development directions of 3D real-scene models are prospected. [Purpose] This paper aims to provide a comprehensive academic perspective and a systematic overview of technological developments for related research, assisting researchers and technical professionals in quickly grasping the up-to-date trends in the field of 3D real-scene models. It serves as a reference for technology R&D and for the integrated application of 3D real-scene models in industries, while also identifying potential breakthrough directions such as AI-enabled real-scene 3D construction, thereby inspiring innovative research and practical ideas.

[Objectives] The rapid advancement of Large Language Models (LLMs) has created new opportunities for intelligent geospatial data processing. However, current integration approaches between LLMs and Geographic Information Systems (GIS) still face key challenges, such as data privacy risks associated with cloud-only architectures, incomplete integration with native GIS toolchains, and the lack of standardized communication protocols for cross-platform interoperability. To address these limitations, this study proposed Smart-QGIS, an agent prototype system for geospatial data processing and mapping built on the Model Context Protocol (MCP). The system supports localized deployment while maintaining open protocol compatibility, enabling flexible integration with both local and cloud-based LLMs. The primary objective is to establish a secure, extensible, and functionally complete intelligent GIS framework that bridges natural language interaction and professional spatial analysis workflows. [Methods] Smart-QGIS was developed on the QGIS platform and uses MCP as a standardized communication bridge between LLMs and native GIS functional interfaces. The system enables end-to-end task execution, allowing users to convert natural language instructions directly into executable spatial analysis operations. It adopted a multi-process modular architecture consisting of five coordinated layers: a user interaction layer, a plugin mediation layer, an MCP communication layer, a local execution layer, and a model inference layer. This architecture ensures system scalability, functional extensibility, and operational stability, while supporting integrated workflows including data loading, spatial analysis, and cartographic visualization. [Results] System performance was evaluated using the vector administrative boundary of Shaanxi Province and digital elevation model raster data. Two model deployment strategies were tested, including a locally deployed open-source OpenAI-compatible GPT model via Ollama and a cloud-based Alibaba Qwen LLM. Through Smart-QGIS, representative GIS tasks such as data loading, clipping, slope calculation, layer visualization, and automated map production were executed interactively. Results demonstrated that Smart-QGIS can accurately interpret complex, multi-step instructions while maintaining an efficient response time, typically below 75 seconds. For routine geospatial processing and visualization tasks, system performance is generally equivalent to or exceeds that of typical professional GIS users, while cloud-based models show higher efficiency in complex task execution. [Conclusions] Overall, the MCP-based localized LLM-GIS integration framework demonstrated advantages in privacy protection, functional coverage, and protocol universality. The system significantly lowers the technical barrier for geospatial data processing, enabling non-specialist users to perform complex spatial analysis tasks efficiently. The proposed architecture provides a practical technical pathway toward building open, collaborative, and intelligent GIS ecosystems, with strong potential for applications in intelligent spatial decision support, automated geospatial data services, and next-generation human-AI collaborative geospatial analysis environments.

[Significance] In the context of Open Science, the continuous emergence of open data has greatly expanded the available resources. However, due to the scattered, heterogeneous, and multi-semantic nature of these data, significant challenges remain for in-depth data mining and knowledge discovery. The Earth's surface system, characterized by strong inter-sphere interactions and intensive human activities, generates particularly rich scientific data. Data mining and knowledge discovery in this domain are at the forefront of global scientific research and a focal point of international competition. [Progress] This paper presents a systematic, full-chain study of key technologies for the discovery, management, mining, model sharing, and platform integration of scientific data related to the Earth's surface system. Using ontology updating and alignment methods, a large-scale scientific data catalog and associated network have been constructed, improving the accuracy and efficiency of data-sharing assessment. By integrating cutting-edge technologies such as cloud computing and container virtualization, intelligent service tools have been developed to enable efficient processing and information extraction from massive remote sensing datasets, thereby advancing standardized approaches for multi-source data management. High-precision parameter products of the Earth's surface system have been generated by fusing remote sensing big data with intelligent algorithms, supporting the efficient mining and analysis of spatiotemporal evolution patterns. The challenge of sharing and computing scientific models has been addressed through innovative heterogeneous model containerization technologies. Furthermore, a collaborative analysis and comprehensive service environment has been established with online computing capacity, applied to representative cases such as ecological barrier construction on the Mongolian Plateau and sustainable development in the Yangtze River Delta urban agglomeration. [Prospect] Building on these advancements, this paper highlights emerging research and development trends in Earth surface system science, emphasizing the progression of data mining and knowledge discovery towards FAIR principles, enhanced intelligence, productization, modeling, and scenario-based applications.

[Objective] Street space perception is a critical dimension of the human-environment relationship and a vital metric for urban quality assessment. However, traditional street perception methodologies face significant limitations: questionnaire surveys are resource-intensive and lack spatial coverage, while conventional computer vision approaches often focus on low-level visual features, failing to capture high-level semantic information and the "why" behind subjective evaluations. Focusing on the area within Beijing's Fifth Ring Road, this study aims to overcome these "black box" limitations by developing a novel, Large Language Model (LLM)-driven multimodal analytical framework. The objective is to systematically characterize street-space perceptions, quantify subjective experiences, and interpret the underlying semantic drivers of urban environmental quality. [Methods] The study established a cascaded analytical pipeline integrating geospatial big data with advanced GeoAI techniques. First, 122 264 street-view images were collected at 50-meter intervals along the OpenStreetMap (OSM) road network using the Baidu Time Machine to ensure temporal consistency. Second, guided by the Triple Bottom Line (TBL) theory of sustainable development, a structured prompt system covering "Ecology-Society-Economy" dimensions was constructed. The TongyiQianwen Qwen2-VL-72B model was employed to interpret these images, generating detailed semantic descriptions and emotion labels. Third, to quantify these qualitative descriptors, a BERT model combined with a bi-directional Long Short-Term Memory (Bi-LSTM) network was trained to convert textual data into fine-grained continuous perception scores. Finally, the study aggregated these scores at the Traffic Analysis Zone (TAZ) scale to analyze spatial patterns using global and local Moran's I, while employing semantic mining techniques—including TF-IDF, Co-word networks, Latent Dirichlet Allocation (LDA) topic modeling, and Textual Knowledge Graphs—to deconstruct the semantic structure of positive and negative perceptions. [Results] The spatial analysis revealed a significant "center-periphery" decreasing gradient and strong spatial clustering in street space perception. Positiveperception zones were predominantly concentrated within the Second and Third Ring Roads, spatially correlating with historical preservation districts and mature commercial hubs. Semantic analysis indicated that these areas are driven by a "synergistic effect" of positive keywords such as "red walls" "greenery" "commercial vitality" and "cultural symbols". Conversely, negativeperception zones were clustered in the peripheral areas and transition zones. Notably, the study identified a "short-board effect" in negative perception areas, where the overall quality was disproportionately dragged down by specific negative semantic drivers like "ruins," "exposed soil," "construction waste," and "industrial noise," rather than a general lack of aesthetics. The LDA model further distilled four key thematic drivers influencing perception: natural ecology, functional efficiency, historical-cultural attributes, and commercial vitality. [Conclusions] This study demonstrates that integrating Multimodal Large Language Models with street-view data effectively bridges the gap between objective built-environment features and subjective human perception. Unlike traditional methods, this framework not only identifies where perception is low but explains why through interpretable semantic evidence. The research confirms that urban perception is non-linear, where eliminating negative "short-board" factors (e.g., disorder, pollution) is often more critical than aesthetic enhancement for improving low-quality spaces. The proposed framework offers a scalable, low-cost, and explainable technical pathway for micro-scale urban diagnostics, providing actionable insights for precision urban renewal and fine-grained spatial governance.

[Objectives] Geographic Knowledge Graph (GeoKG) employs knowledge graph techniques to represent geographic knowledge as a computer-interpretable, reusable, and inferable knowledge network. However, due to the sparsity of geographic information distribution and outdated updates, GeoKGs are often incomplete, which restricts their breadth and depth of application. Geographic knowledge graph completion techniques are needed to address this incompleteness. Nevertheless, existing knowledge graph completion methods fail to fully account for the semantic information within GeoKGs and the distance-decaying effect governing interactions among geographic entities, resulting in an embedding space that inadequately captures the true distribution of geographic entities and relations, thereby limiting completion performance. [Methods] To address this issue, this study proposes a Distance-Decaying Effect-Aware Geographic Knowledge Graph Completion method (DDGKGC). The method first captures semantic information and distance-related features between entities and relations through a semantic information aggregation module and a distance-decaying effect-aware module. Then, a dual-attention mechanism-based representation learning module adaptively learns neighborhood information of entities and relations to derive their embeddings. Finally, the ConvE scoring function is used for prediction, and the results are applied to complete the GeoKGs. [Results] To comprehensively evaluate model performance,this study conducts comparative experiments, ablation studies, and multi-dimensional validation analyses on the self-constructed datasets Multi-Geo, CityDirection, and CountyDistance, as well as the public dataset Countries-S3. Experimental results demonstrate that DDGKGC achieves outstanding performance across multiple metrics including MRR, Hits@1, Hits@3, and Hits@10. Particularly in terms of MRR, which comprehensively reflects model performance, DDGKGC outperforms the baseline methods by 4%, 3.1%, 1.8%, and 5.2% on the four datasets, respectively. Moreover, through multi-dimensional validation and analysis, it is proven that DDGKGC can more effectively model the spatial and semantic relationships among geographic entities, thereby enhancing the accuracy and geographic plausibility of completion results. [Conclusions] The results demonstrate that the proposed method not only effectively enhances the performance of the geographic knowledge graph completion task but also exhibits strong generalization capability and application potential. Furthermore, it provides reliable support for the advanced application of GeoKGs.

[Significance] Urban digital twin models simulate comprehensive urban scenes by digitally mapping physical entities through real-time data integration. These models serve as visual, real-time representations of urban dynamics within smart cities, incorporating technologies such as the Internet of Things (IoT), spatial information systems, artificial intelligence, and others. Building on the Physical-Social-Information (PSI) three-dimensional framework, this paper reviews the current research progress of urban digital twin models and innovatively proposes a four-dimensional coupling framework: Physical-Social-Information-Time (PSIT). [Progress] The main research findings are as follows: (1) Since the introduction of digital twin technology into urban research in 2017, related literature has grown rapidly, with theoretical foundations and functional design frameworks gradually maturing. Urban digital twin models have initially been developed along three dimensions, PSI, including the digital mapping of geographic entities, spatial analysis of human activities, and the fusion and mining of geographic big data. (2) To more accurately reflect the real urban operations, current models require breakthroughs in data, technology, and algorithms. The PSI framework tends to overemphasize spatial features while oversimplifying the temporal dimension, lacking a representation of the spatiotemporal differentiation inherent in urban systems. (3) Recognizing the critical role of spatiotemporal coupling in urban modeling, this paper elevates time from a background variable to an independent dimension. This is based on the unidirectional nature of time, the temporal constraints on social behavior, the allometric time scales of urban element evolution, and the time-dependent mechanisms behind system phase transitions. Accordingly, the PSIT four-dimensional coupling framework is proposed to enhance the logic of urban system evolution and advance the theoretical paradigm of urban digital twin modeling. The CitySPS platform is presented as a case study for detailed illustration. [Prospect] The PSIT four-dimensional coupling framework offers the potential for more precise simulation and accurate prediction in digital urban spaces, representing a promising direction for future "intelligent" urban governance.

[Objectives] To systematically investigate the key pathways and application models for leveraging intelligent technologies in urban-rural integrated planning. It seeks to explore potential responses to the complex challenges arising from China's strategic developmental shift towards the renewal and quality enhancement of its existing urban and rural stock. In this new era, traditional planning methodologies, which often rely on static data and experience-driven decision-making, face significant limitations in addressing the intricate stakeholder relationships, intertwined land uses, and dynamic nature of established built environments. This review, therefore, explores how a more intelligent, data-informed approach could better support contemporary planning objectives. [Discussion] Addressing challenges such as the precise identification of multi-source features, the issue of persistent data silos, and the need for in-depth cognition of complex human-environment relationships, this paper reviews and proposes a closed-loop "Perception-Fusion-Cognition-Planning" conceptual framework. This framework is intended to guide the application of intelligent digitalization across the planning lifecycle. It begins with Intelligent Perception, which suggests integrating multi-modal data from sources like high-altitude cameras and drones with AI-driven algorithms for the automated semantic analysis and dynamic monitoring of urban-rural elements. This is followed by Spatio-temporal Data Fusion, which focuses on standardizing and integrating these heterogeneous data streams onto a unified baseline, creating a consistent and reliable digital foundation for analysis. The third stage, Cognition, employs knowledge graph technology to transform the integrated data into deep, systemic insights by explicitly modeling the implicit relationships between spatial entities, socio-economic factors, and regulatory policies. Finally, the Planning and Governance stage describes how an integrated enabling platform can translate these insights into actionable decision support, facilitating scenario analysis and collaborative workflows. A case study focused on farmland protection in Zengcheng District, Guangzhou, is presented to illustrate the potential and feasibility of this integrated technological pathway. The case demonstrates how the framework can be applied to achieve end-to-end governance, from automated change detection to policy-based reasoning and targeted enforcement. [Prospect] This systematic framework offers a potential conceptual approach for addressing the multifaceted challenges inherent in the intelligent planning for urban-rural integration. It is hoped that this work can help facilitate a paradigm shift in planning from traditional, experience-driven methods toward modern, data-informed "platform-based governance," characterized by more dynamic, evidence-based, and collaborative processes. By providing both theoretical references and practical guidance, this review aims to contribute to the ongoing efforts to modernize spatial governance capabilities, thereby supporting the overarching goals of sustainable and high-quality integrated development in China.

[Background] In recent years, confronted with emerging phenomena and new challenges in cartographic practice, an increasing number of scholars have called for a critical reassessment of existing paradigms in cartography, aiming to address both the disciplinary challenges and societal demands arising from technological transformations. [Objectives and Methods] Following a research approach that integrates critical inheritance and innovative transcendence, this study employs theoretical deduction to first review and synthesize existing paradigms in cartography. It then analyzes the predicaments encountered during the structural transformation of mapping practices and, finally, proposes a new paradigm for the discipline. [Results] Traditional cartographic research tends to equate “maps” with practices defined by specific professional norms, thereby endowing cartography with distinct characteristics of professional map-making in its knowledge sources, focal concerns, and practical pathways, ultimately forming what may be termed the professional map-making paradigm. However, this paradigm increasingly reveals two prominent dilemmas. On the one hand, the professional map-making paradigm struggles to capture the complexity and fluidity of maps as they are embedded in everyday social life, often resulting in theoretical lag and explanatory failure when addressing new forms of cartographic practices and their associated meaning-making mechanisms. On the other hand, the paradigm tends to operate within a closed cycle of internal knowledge reproduction, lacking substantial theoretical innovation and failing to cultivate problem-oriented thinking, thereby weakening its capacity to guide and regulate cartographical practice. In response, this study, grounded in a networked and relational understanding of the world, proposes a social practice paradigm for cartography. This paradigm conceptualizes maps as social practices embedded within social networks and linked to social actors, emphasizing the unique meanings and social values that maps generate in connecting individuals with the external world. [Conclusions] The social practice paradigm advocates for a transcendent perspective in understanding and interpreting maps, incorporates interdisciplinary integration and pluralistic methodological approaches, and promotes the coordination of local experiences with global perspectives. This paradigm not only deepens holistic understanding of mapping practices but also offers new theoretical resources and analytical frameworks for cartography to address pressing issues in contemporary digital, intelligent, and networked societies. Future research should systematically analyze the research context and theoretical foundations of cartography within the social practice paradigm. A conceptual, discursive, knowledge, theoretical, and methodological system, distinct from the professional map-making paradigm, needs to be gradually established to address the challenges faced by cartographic practice in complex and evolving contexts. The fundamental goal is to expand the boundaries of cartographic research and intellectual resources, transcending existing disciplinary categories and knowledge frameworks.

[Significance] As a critical pathway to achieving Sustainable Development Goal (SDG) 2, “Zero Hunger,” and ensuring long-term ecological sustainability, the concept and practice of sustainable agriculture are undergoing a paradigm shift toward data-driven and system-oriented approaches. In recent years, Big Earth Data—comprising remote sensing, geospatial, meteorological, and agricultural Internet of Things (IoT) data—has emerged as a foundational driver for agricultural monitoring, decision support, and technological innovation in sustainable development. [Analysis] Given the interdisciplinary, multi-stakeholder, cross-regional, and evolving-goal nature of sustainable agriculture, this study begins by systematically reviewing the conceptual evolution of the term. It highlights the multidimensional implications and diverse practical pathways of sustainable agriculture, noting its growing role as a core component of global development strategies. On this basis, the paper proposes a new, data-oriented and operational interpretation of sustainable agriculture. The study then establishes an analytical framework—“data-Technology”—to clarify the pivotal role of Big Earth Data in supporting sustainable agriculture. It examines the evolution of core datasets and key technical methods across three periods: before 2015, 2015—2019, and 2020 to the present. The applications reviewed include agricultural resource monitoring, multi-scale crop condition assessment, and evaluations of agriculture's environmental impacts. The findings suggest that sustainable agriculture, enabled by Big Earth Data, is rapidly shifting from a paradigm of "observational analysis" to one of "intelligent decision-making." Furthermore, the study conducts a comparative assessment of China, the United States, and the European Union across four critical dimensions: data infrastructure; technological advancement and application; scientific research capacity; and policy support. While China has made significant progress in all four areas—with strengths in remote sensing capabilities, rapid technological rollout and demonstration, substantial research output, and clearly defined policy directives—it continues to face challenges in data ecosystem development, original algorithm innovation, commercialization of scientific outputs, and the alignment of standards and incentive mechanisms. Finally, in light of the current needs for sustainable agricultural development, this study systematically analyzes the major challenges facing Big Earth Data from four aspects: data acquisition capacity, intelligent processing methods, application promotion and services, and data governance and ethical security. In response, it proposes multi-level strategies covering standardization, model optimization, improvements to service systems, and protection of data rights, with the aim of providing a reference pathway for the efficient utilization and sustainable development of agricultural big data in the future. [Prospect] The article aims to analyze the data-driven transformation pathway of sustainable agriculture and provide a systematic reference for its green, inclusive, and intelligent development.

[Objectives] Global climate change, accelerating sea-level rise, and intensifying anthropogenic pressures are rendering the intricate human-land-sea nexus within coastal zones increasingly complex, sensitive, and vulnerable. This growing challenge underscores the urgent need for integrated coastal research frameworks capable of synthesizing environmental sensing, dynamic process simulation, and scenario projection. Addressing this critical gap, Digital Twin (DT) technology emerges as a transformative paradigm. By integrating multi-source data, sophisticated models, and domain knowledge into intelligent systems, DT offers unprecedented potential for creating precise virtual replicas and enabling intelligent management of complex coastal socio-ecological systems. [Analysis] This paper systematically analyzes the state of coastal zone digitalization, highlighting the pressing need for robust digital frameworks that can effectively represent and analyze the strong coupling between natural processes and human activities under multifaceted pressures. Building on this foundation, we propose a novel conceptual framework and implementation pathway for constructing a Digital Twin Coastal Zone (DTCZ). This framework explicitly positions land-sea interface processes as the foundational scenario and centers on human-land-sea feedback mechanisms as the core analytical thread. The proposed DTCZ system architecture is articulated across four pivotal dimensions: (1) Comprehensive information integration and knowledge aggregation; (2) Simulation of natural processes integrated with coupled human-nature decision support; (3) Synergistic short-term forecasting and long-term monitoring capabilities; and (4) Realistic multidimensional representation enabling intelligent interaction. We critically discuss the key technological enablers supporting this vision, encompassing coastal data governance and fusion, multi-scale scenario modeling, predictive analytics for critical coastal elements, persistent long-term monitoring strategies, and the development of the integrated DTCZ platform itself. At its core, the envisioned DTCZ leverages spatiotemporally fused multi-source data as its foundation and prioritizes enhanced scenario simulation and intervention capabilities. [Prospects] This framework is designed to overcome the limitations, such as fragmented data and limited predictive power, that constrain traditional coastal digital systems. By significantly advancing the computational tractability and overall manageability of coastal systems, the DTCZ paradigm offers a powerful new methodological tool and operational framework. It holds strong potential for supporting sustainable coastal development and modernizing governance structures in the face of ongoing climate change, providing a robust platform for evidence-based planning and adaptive management.

[Significance] As a representative of new-quality productivity, the low-altitude economy is gradually emerging as a new engine for economic growth. This economy is based on the development and utilization of low-altitude airspace resources. While bringing development opportunities to geospatial information technology, it also poses entirely new challenges. [Progress and Analysis] In this paper, we introduce the division of low-altitude airspace resources and highlight typical drone application scenarios in the context of the low-altitude economy. Subsequently, we analyze the broad application prospects of geospatial information technology in key areas of the low-altitude economy, including the refined utilization of airspace resources, the construction of low-altitude environments, the planning, construction, and operation of new air traffic infrastructure, as well as the safe and efficient operation and regulatory oversight of drones. We emphasize that the geospatial information industry will benefit from development opportunities such as the integration and innovation of emerging scientific and technological advancements, growing market demand, policy support, industrial guidance, and industrial upgrading and transformation. [Prospect] Finally, we briefly address the challenges geospatial information technology must overcome to meet the development needs of the low-altitude economy. These include advancements in spatio-temporal dimension elevation, map and location-based services, high-frequency and rapid data acquisition systems, all-time and all-domain capabilities, and ubiquitous intelligent technologies. These areas will also serve as future directions for development and breakthroughs in geospatial information technology.

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

[Objectives] All meaningful forms of human discourse are rhetorical, and the purpose of rhetoric is to enable communication and foster sympathy between parties with certain views. Narrative maps are essentially a discursive practice for communicating information and exchanging ideas, characterized by the strategic use of rhetoric to construct persuasive discourse and achieve the goal of "agreement" or "persuasion". In the current era, where visual dominance is increasingly prominent, rhetoric has garnered growing attention in cartography. This turn not only addresses core issues in narrative map research but also provides a realistic path for enriching and reconstructing the existing knowledge of modern cartography. However, the academic community has yet to establish a systematic framework, leaving three key issues unresolved: (1) How to conceptualize the rhetoric of narrative maps? (2) How to categorize the rhetoric of narrative maps? (3) What is the working mechanism of rhetoric in narrative maps? [Methods] To address these research gaps, this article, firstly, follows the research paradigm of rhetoric to clarify the essence of rhetoric in narrative maps, and defines it as: "During the design process of narrative maps, cartographers use certain visualization strategies to facilitate the representation of events, thereby weaving explicit narrative intentions into the mapping space in an implicit way to create persuasive discourse or emotional agreement for viewers." Secondly, a classification criterion is proposed based on the differences between content semantic representation and logical semantic representation. Two major categories, semantic rhetoric and structural rhetoric, along with 24 minor classes, are divided for rhetoric of narrative map. Semantic rhetoric mainly focuses on enhancing the understanding of content, expressing the connotation and imaginative tension of map "text". Structural rhetoric aims to emphasize the logic semantic relationships in narrative discourse, presenting the narrative logic of events. Semantic rhetoric often manifests as the design of visual symbols to describe events, serving as the "visual punctum" of narrative maps. Structural rhetoric typically involves adjusting the arrangement and structure of different event units, functioning as the "visual stadium" of narrative maps. Next, the mechanism of rhetoric in narrative maps is explored from four aspects: the dimensions of rhetoric, the hierarchy of rhetoric, the integrated use of rhetoric, and the applicability principles of rhetoric. Finally, this study demonstrates the applicability of the proposed theoretical framework through a case study of "Jiangnan Canal", illustrating how the framework can facilitate narrative map design. [Conclusions] This paper lays a theoretical foundation for narrative map research and contributes to the theoretical innovation of contemporary cartography.

[Objectives] The outdoor pedestrian navigation road network is a vital component of maps and a crucial basis for outdoor activity route planning and navigation. It plays a significant role in promoting outdoor travel development and ensuring safety management. However, existing research on road network generation mainly focuses on the construction of urban vehicular navigation networks, with relatively less emphasis on hiking navigation road networks in complex outdoor environments. Moreover, existing methods primarily emphasize the extraction of two-dimensional geometric information of roads, while the reconstruction of real three-dimensional geometric and topological structures remains underdeveloped. [Methods] To address these limitations, this study proposes a method for constructing the three-dimensional outdoor pedestrian navigation road network maps using crowdsourced trajectory data. This approach leverages a road network generation layer and an elevation extraction layer to extract the two-dimensional structure and three-dimensional elevation information of the road network. In the road network generation layer, a trajectory density stratification strategy is adopted to construct the two-dimensional vector road network. In the elevation extraction layer, elevation estimation and optimization are performed to generate an elevation grid raster map, which is then matched with the two-dimensional road network to produce the three-dimensional hiking navigation road network. [Results] To demonstrate the effectiveness of the proposed approach, experiments were conducted using 1 170 outdoor trajectories collected in 2021 from Yuelu Mountain Scenic Area in Changsha through an online outdoor website. The constructed outdoor three-dimensional hiking road network map achieved an average positional offset of 4.201 meters in two-dimensional space and an average elevation estimation error of 7.656 meters. The results demonstrate that the proposed method effectively handles outdoor trajectory data with high noise and varied trajectory density distribution differences, generating high-quality three-dimensional hiking road network maps. [Conclusions] Compared to traditional outdoor two-dimensional road networks, the three-dimensional navigation road networks constructed this study provide more comprehensive and accurate map information, facilitating improved pedestrian path planning and navigation services in complex outdoor environments.

Geographic Information Science (GIS) is not only the demand for the development of the discipline itself, but also the technical method to support the exploration of the frontiers of geography, earth system science and future geography, and the supporting technology to serve the national strategy and social development. In view of the intrinsic law of the development of geographic information science, the extrinsic drive of the development of related disciplines, and the pull of new technologies such as Artificial Intelligence (AI), this paper firstly analyses the development process of GIS and explores its development law from six dimensions, such as description content, expression dimension, expression mode, analysis method and service mode, etc.; then, on the basis of interpreting the original intention and goal of the development of geography, a geography discipline system oriented to the "physical-humanistic-informational" triadic world is proposed, the research object of information geography is discussed, and a conceptual model integrating the seven elements of information and seven dimensions of geographic descriptions is put forward; then, the development trend of geographic information science is analysed from three aspects, including geography from the perspective of information science, information geography from the perspective of geography, and geo-linguistics from the perspective of linguistics, information geography from the perspective of geography, and geolinguistics from the perspective of linguistics, the development trend of geographic information discipline is analysed. Finally, the paper summarises the possible directions and points of development of GIS, geography in the information age, geo-scenario, and geo-big model. We hope that our work can contribute to enriching the understanding of geographic information disciplines, promoting the development of geographic information related sciences, and enhancing the ability of the discipline to support national development needs and serve society.

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

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

Human mobility data play a crucial role in many real-world applications such as infectious diseases, transportation, and public safety. The development of modern Information and Communication Technologies (ICT) has made it easier to collect large-scale individual-level human mobility data, however, the availability and usability of the raw data are still significantly limited due to privacy concerns, as well as issues of data redundancy, missing, and noise. Generating synthetic human mobility data through modeling approaches to statistically approximate the real data is a promising solution. From the data perspective, the generated human mobility data can serve as a substitute for real data, mitigating concerns about personal privacy and data security, and enhance the low-quality real data. From the modeling perspective, the constructed models for human mobility data generation can be used for scenario simulations and mechanism exploration. The human mobility data generation tasks include individual trajectory data generation and collective mobility data generation, and the research methods primarily consist of mechanistic models and machine learning models. This article firstly provides a systematic review of the research progress in human mobility data generation and then summarizes its development trends and challenges. It can be observed that mechanistic-model-based methods are predominantly studied in the field of statistical physics, while machine-learning-based methods are primarily studied in the field of computer science. Although the two types of models have complementary advantages, they are still developing independently. The article suggests that future research in human mobility data generation should focus on: 1) exploring and revealing the underlying mechanisms of human mobility behavior from a multidisciplinary perspective; 2) designing hybrid approaches by coupling machine learning and mechanistic models; 3) leveraging cutting-edge generative Artificial Intelligence (AI) and Large Language Model (LLM) technologies; 4) improving the models' spatial generalization and transfer-learning capabilities; 5) controlling the costs of model training and implementation; and 6) designing reasonable evaluation metrics and balancing data utility with privacy-preserving effectiveness. The article asserts that human mobility processes are typical phenomenon of human-environment interactions. On the one hand, research in Geographic Information Science (GIS) field should integrate with theories and technologies from other disciplines such as computer science, statistical physics, complexity science, transportation, and others. While on the other hand, research in GIS field should harness the unique characteristics of GIS by explicitly incorporating geographic spatial effects, including spatial dependency, distance decay, spatial heterogeneity, scale, and more into the modeling process to enhance the rationality and performance of the human mobility data generation models.

Cyberspace surveying and mapping has become a hot research topic of widespread concern across various fields. Its core task involves surveying the components of cyberspace, analyzing the laws of cyberspace phenomena, and mapping the structure of cyberspace. Research on cyberspace surveying and mapping faces issues such as diverse conceptual terminologies which is lack of unified research frameworks, unclear understanding of elements and laws, non-standardized methods of cyberspace map expression, and the absence of unified standards. Based on systematically reviewing the current status of cyberspace surveying and mapping research across fields, a common understanding of the essence of cyberspace has been analyzed. Starting from the spatial, geographical, and cultural characteristics of cyberspace, the features and advantages of studying and utilizing cyberspace from the perspective of mapping geography are dissected. A research framework for cyberspace surveying and mapping is proposed, focusing on the core content and key technologies of "surveying " and "mapping" in cyberspace, and explaining its relationship with 3D Real Scene, Digital Twins and Metaverse. Cyberspace surveying has been divided into narrow and broad senses, pointing out the lack of holistic measurement of cyberspace features and the lack of research on measuring the phenomena and patterns of human activity in cyberspace. From the perspective of cyberspace cognitive needs, a conceptual model and classification system for cyberspace maps have been proposed. Focusing on the cyberspace coordinate system, "geo-cyber" correlation mapping, and methods of expressing cyberspace maps, the key technologies for creating cyberspace maps are described in detail, and the methods of representing cyberspace maps and their applicability are systematically analyzed. Finally, key scientific questions and critical technologies that need focused research, such as the top-level concepts of cyberspace, cyberspace modeling methods, theories and methods of cyberspace maps, and the design of application scenarios for cyberspace maps, are discussed.

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

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

In recent years, with the continuous development and rapid iteration of emerging technologies such as mobile communication, big data, the Internet of Things (IoT), Artificial Intelligence (AI), digital twins, and autonomous driving, new smart cities have become a significant frontier in the field of Geographic Information Systems (GIS) applications. Digital twin cities represent a complex integrated technological system that underpins the development of next-generation smart cities. Intelligent, holistic mapping for digital twin cities relies on comprehensive urban sensing, and the interactive control of urban sensing facilities plays a pivotal role in achieving the seamless integration of the physical and digital aspects of digital twin cities, fostering the convergence of entities within the urban environment. Describing spatiotemporal entities of the real world through a spatiotemporal data model, as well as modeling the behavioral capabilities of these entities using spatiotemporal object behavior, represents not only an innovative extension of GIS spatiotemporal data models but also addresses the practical requirements of triadic fusion and interactive analysis of human, machine, and object components with the development of digital twin city. As a crucial facet of urban infrastructure, urban sensing facilities epitomize distinctive spatiotemporal entities. Current research into the interactive control of these facilities is predominantly concentrated within the domains of the IoT, Virtual Reality/Augmented Reality (VR/AR), and GIS. However, these domains often lack research pertaining to interactive control of urban sensing facilities within the GIS-based digital realm. To tackle these issues, a viable approach involves mapping the direct physical control processes of humans over objects in the Internet of Things domain to the realm of GIS. Specifically, this involves using a GIS spatiotemporal data model to abstractly represent urban sensing facilities in the real world as spatiotemporal entities. These entities are then expressed as spatiotemporal objects within a spatial information system. Subsequently, the changes or actions of these facility spatiotemporal entities are uniformly abstracted as the behavioral capabilities of these spatiotemporal facility objects. Ultimately, the interaction control of these sensing facilities by humans is transformed into a process where humans invoke the behavioral capabilities of facility spatiotemporal objects, resulting in specific outcomes. Based on the aforementioned idea, this study employs a multi-granular spatiotemporal object data model to construct behavior capabilities for urban sensing facilities. Building upon this foundation, a spatiotemporal object behavior-driven approach for interactive control of urban sensing facilities with virtual-reality integration is introduced. By constructing a "quintuple" model for interactive control of facility objects, this approach facilitates users in engaging in interactive control through a reciprocal linkage between virtual scenarios and physical facilities. This mechanism effectively translates the process of urban sensing facility interaction control based on direct communication commands into the digital world, providing theoretical and technical support for the intelligent and interactive analytical applications of sensing facilities within digital twin cities. Experimental results substantiate the effectiveness and feasibility of the proposed method for interactive control of urban sensing facilities.

This study aims to explore the complex spatiotemporal patterns of bicycle-sharing trips, reveal the influence of urban factors on the OD of bicycle-sharing trips, and improve the accuracy of OD prediction. Combining the theory of urban computing, urban factors such as the epidemic, months, weather conditions (minimum temperature, maximum temperature, and wind speed), and whether it is a weekday along with the length information of non-motorized lanes are selected to construct a bicycle-sharing demand prediction model (USTARN) that integrates urban computing and spatiotemporal attention residual network. USTARN first captures the spatiotemporal dependence of sharing bicycle flow through spatial area division and time series slicing, then combines the attention mechanism for deep residual learning, and finally adjusts the deep residual prediction results according to the urban factor prediction results to improve the model performance. Using the big data from bicycle orders and urban factor datasets in Shenzhen obtained from the government data open platform, this study visualizes the spatiotemporal distribution patterns of bicycle-sharing trips and analyzes their influencing factors using the Python development environment. The OD data set is divided into training set, verification set, and test set in a 7: 1:2 ratio, and the model training, model parameter adaptive adjustment, and model result comparison are carried out, respectively. The results show that the average error of the USTARN model for OD prediction of bike-sharing trips is 7.68%, which is 5.93%, 7.55%, and 6.07% lower than that of the STARN model without urban computing and the traditional CNN model, which is good at data feature extraction, and the BiLSTM model, which is good at dealing with bi-directional time-series data, respectively. The USTARN model fully reflects the influence of time, space, epidemic, weather, and other factors on the OD of bike-sharing trips. Our results have theoretical guiding significance for the accurate prediction of bike-sharing trip OD, which can provide a scientific basis for urban non-motorized roadway planning and have practical application value for the promotion of bike-sharing travel mode and solving the 'last mile' problem of residents travel.