Low efficiency of earth kilns used in the carbonising process of wood to make charcoal has been reported as one of the sources of increasing charcoal wastes in the global south. However, the potential link and approaches of converting charcoal wastes-to-valuable energy and for the environmental health is not well known in Africa. Promoting local community capacity engagement in the production and reutilisation of recycled charcoal wastes at the households’ level is one of important measures to maintain environmental services for sustainability since households make decisions on the type of energy used. This paper, presents an approach of converting charcoal wastes to fuel energy for rural households and environmental health in Kilosa District, Tanzania. To achieve the objective of this research, the primary data were collected through interviews held with 298 randomly selected households, Focus Group Discussions and observations. IBM SPSS statistics version 20 Cross tab tools were used in the data analysis. Results revealed that the conversion of charcoal wastes-to-fuel energy approach used in this research demonstrates the ability of recyclable briquettes made from the locally available charcoal pollutants collected at different stages from earth kilns, to selling centers, improves tree harvest behaviour, adds another fuel energy source through reutilisation, and ultimate reduces pollution at the local level. Thus, the study provides a basis for policymakers to adopt charcoal wastes recycling strategies to address matters related to energy and ultimately enhances environmental health for sustainable development in Tanzania and beyond.

Urban trees are valuable resources for urban areas as they have the capacity to reduce ambient temperatures, mitigate urban heat island effects and reduce runoff of rainwater playing an important role in mitigating the impacts of climate change by reducing atmospheric carbon dioxide (CO2). It also helps to reduce aerial suspended particulate matter, add visual appeal to the urban landscape sequestrating a significant amount of carbon from ambient atmospheric CO2. Carbon storage by urban trees in the ring road area of the Kathmandu Valley was quantified to assess the magnitude and role of urban forests in relation to mitigate the impact of global climate change. A total of 40 sample plots were placed randomly for the detailed carbon assessment. Aboveground and belowground carbon pools were considered in the detailed assessment. Furthermore, quality assurance (QA) and quality control (QC) were maintained through regular monitoring and capacity building of the field crews while collecting the bio-physical data. The assessment recorded a total of 33 different species of plants in the avenue’s plantation sites in ring road. The mean seedling, sapling, and tree density was found to be 2149585 and 185 per hectare. The mean carbon stock per hectare in the avenue plantation of the ring road area was 24.03 tC ha1 and the existing total carbon stock was 7785.72 tC in 2021. Likewise, the total baseline carbon dioxide equivalent (tCO2e) in the avenue plantation was found to be 28573.60 tCO2e. The carbon dioxide emission from the transport sector in the ring road area in a full movement scenario was 312888.00 tCO2e per annum, while the net emissions was 42547 tCO2e. There was a deficit of carbon dioxide in terms of stock by avenue plantations of 14000.8 tCO2e.This study indicates that the existing urban forest plantation is unable to sequestrate or offset the carbon dioxide that is emitted through the transportation sector. Consequently, open spaces like riverbanks and any other public lands, in which urban forests could be developed has to be planned for the green infrastructure and plantation of the multipurpose trees. The distinct values of forests in and around urban areas have to be recognized in the specific policies and plans for the sustainable management of urban and peri-urban forests to meet the adverse impact of global climate change. In addition, this study provides insights for decision-makers to better understand the role of urban forests and make sustainable management plans for urban forests in the cities like in Kathmandu Valley.

Delimiting ecological space scientifically and making reasonable predictions of the spatial-temporal trend of changes in the dominant ecosystem service functions (ESFs) are the basis of constructing an ecological protection pattern of territorial space, which has important theoretical significance and application value. At present, most research on the identification, functional partitioning and pattern reconstruction of ecological space refers to the current ESFs and their structural information, which ignores the spatial-temporal dynamic nature of the comprehensive and dominant ESFs, and does not seriously consider the change simulation in the dominant ESFs of the future ecological space. This affects the rationality of constructing an ecological space protection pattern to some extent. In this study, we propose an ecological space delimitation method based on the dynamic change characteristics of the ESFs, realize the identification of the ecological space range in Qionglai city and solve the problem of ignoring the spatial-temporal changes of ESFs in current research. On this basis, we also apply the Markov-CA model to integrate the spatial-temporal change characteristics of the dominant ESFs, successfully realize the simulation of the spatial-temporal changes in the dominant ESFs in Qionglai city’s ecological space in 2025, find a suitable method for simulating ecological spatial-temporal changes and also provide a basis for constructing a reasonable ecological space protection pattern. This study finds that the comprehensive quantity of ESF and its annual rate of change in Qionglai city show obvious dynamics, which confirms the necessity of considering the dynamic characteristics of ESFs when identifying ecological space. The areas of ecological space in Qionglai city represent 98307 ha by using the ecological space identification method proposed in this study, which is consistent with the ecological spatial distribution in the local ecological civilization construction plan. This confirms the reliability of the ecological space identification method based on the dynamic characteristics of the ESFs. The results also show that the dominant ESFs in Qionglai city represented strong non-stationary characteristics during 2003-2019, which showed that we should fully consider the influence of the dynamics in the dominant ESFs on the future ESF pattern during the process of constructing the ecological spatial protection pattern. The Markov-CA model realized the simulation of spatial-temporal changes in the dominant ESFs with a high precision Kappa coefficient of above 0.95, which illustrated the feasibility of using this model to simulate the future dominant ESF spatial pattern. The simulation results showed that the dominant ESFs in Qionglai will still undergo mutual conversions during 2019-2025 due to the effect of the their non-stationary nature. The ecological space will still maintain the three dominant ESFs of primary product production, climate regulation and hydrological regulation in 2025, but their areas will change to 32793 ha, 52490 ha and 13024 ha, respectively. This study can serve as a scientific reference for the delimitation of the ecological conservation redline, ecological function regionalization and the construction of an ecological spatial protection pattern.

Abstract: By studying the structural characteristics and carbon storage of the mangrove island ecosystem in the 
Beibu Gulf, this study provides a scientific basis for mangrove ecological compensation in the coastal areas of 
Guangxi, South China Sea. On the basis of the unmanned aerial vehicle remote sensing images and a sample plot 
survey, the object-oriented multi-scale segmentation algorithm is used to extract the mangrove community type information, and one-way analysis of variance is conducted to analyse the structural characteristics of the mangrove 
community. The carbon storage and carbon density of different mangrove ecosystems were obtained based on the 
allometric growth equation of mangrove plants. The analysis yielded four main results. (1) The island group covers 
about 27.10 ha, 41.32% (11.20 ha) of which represents mangrove areas. The mangrove forest is widely distributed 
in the tidal flats around the islands. (2) The main mangrove types were Aegiceras corniculatum, Kandelia obovata + 
Aegiceras corniculatum, Avicennia marina + Aegiceras corniculatum and Avicennia marina communities. (3) 
Amongst the mangrove plants, Avicennia marina had the highest biomass (18.52 kg plant–1), followed by Kandelia 
obovata (7.84 kg plant–1) and Aegiceras corniculatum (3.85 kg plant–1). (4) The mangrove carbon density difference 
was significant. Kandelia obovata had the highest carbon density (148.03 t ha–1), followed by Avicennia marina
(104.79 t ha–1) and Aegiceras corniculatum (99.24 t ha–1). The carbon storage of the mangrove island ecosystem 
was 1194.70 t, which was higher than in other areas with the same latitude. The carbon sequestration capacity of 
the mangrove was relatively strong.

Enhancing amenity is a key pathway for rural tourism destinations to transform "lucid waters and lush mountains" into "invaluable assets", and an important component in promoting the construction of beautiful, livable, and prosperous villages. Based on the rural regional system theory, experience economy theory, and supply-demand theory, this paper constructs a theoretical framework of "natural resource endowment—amenity perception—rural sustainable development". Through empirical research on three typical case sites of different types in Jiangxi province, and by comprehensively applying exploratory and confirmatory factor analysis, the paper reveals the influencing mechanisms of amenity in rural tourism destinations and proposes improvement pathways accordingly. Key findings include: (1) Rural tourism destinations amenity is shaped by both objective factors (natural environment, facilities, local development, and culture) and subjective factors (personal perception, technology application), with the natural environment playing a core supporting role. (2) The formation of rural tourism destinations amenity is a dynamic process where three factors—foundational support, endogenous momentum, and exogenous momentum—interact to achieve a equilibrium through supply-demand interactions, reflecting the complexity and systemic nature of human-land relationships. (3) Enhancing rural tourism destinations amenity requires following the path of "element flow—structural optimization—functional manifestation". Through coordinated resource allocation, supply-demand optimization, and value realization, this approach drives rural areas toward high-quality living and tourism spaces. The study provides theoretical foundations and practical guidance for the sustainable development of rural tourism destinations.

Traditional villages face challenges such as inefficient resource utilization and insufficient stakeholder coordination, which hinders the transformation of cultural and tourism value. Based on the theories of resource integration and value co-creation, this study takes the Goulan Yao village as a case to explore an embedded path for cultural and tourism integration to drive common prosperity. The findings reveal that: (1) Within the "resource integration-value co-creation-common prosperity" development framework, resource integration serves as the foundation for embedding cultural tourism, while common prosperity is the outcome of value co-creation, with the three elements progressing in a logical sequence. (2) The Yao village follows a "recognition-allocation-utilization" logic by deeply cultivating local cultural resources and incorporating modern elements, thereby achieving the industrial transformation of cultural tourism resources into economic capital. (3) The government, enterprises, villagers, and tourists engage in complementary actions and coupled elements within the value co-creation network, jointly fostering multi-dimensional value, including cultural, economic, and social benefits. This study reveals the intrinsic logic of cultural and tourism integration in traditional villages for common prosperity, enriches the theoretical framework of rural common prosperity, and provides both theoretical and practical references for the development of similar tourism-driven traditional villages.

As a living world cultural heritage site exemplifying "harmonious coexistence between man and nature", the Cultural Landscape of the Old Tea Forests of Jingmai Mountain embodies the ecological wisdom and farming culture of the Bulang and Dai ethnic groups. Since its inscription, it has rapidly integrated into tourism, branding, and market systems, serving as a model for ecological civilization and rural revitalization, and a frontier topic for living heritage governance innovation. Based on fieldwork and multi-source data analysis, this paper addresses practical challenges in its protection, management and utilization. Protection focuses on the sustainability of tea-forest symbiotic, local knowledge transmission, and multi- stakeholders engagement. Management concerns governance optimization, talent development, equitable development, and landuse regulation. Utilization involves tea production standardization, brand building, heritage-tourism value co-creation, and global communication. These dimensions are interdependent: protection sets ecological and cultural boundaries, management provides a collaborative framework, and utilization drives sustainable momentum. To achieve post-inscription sustainable development, four priorities are proposed: (1) Theoretical and methodological integration for heritage conservation, using complex ecosystem, living heritage, and multistakeholder collaboration theories. (2) Policy coordination and adaptive governance to enhance efficiency via top-down linkage and bottom-up feedback. (3) Utilization aligned with national strategies, implementing ecological civilization through redline protection and brand building, and boosting rural revitalization via tea-culture-tourism integration and community self-governance. (4) Value co-creation and global dialogue as pathways to realize the glocal expression of local cultural symbols. Through theoretical innovation, technological empowerment, institutional improvement and international cooperation, Jingmai Mountain will sustain its heritage values and provide a Chinese-style, universally applicable governance model for the conservation of living cultural heritage worldwide, thereby contributing to the advancement of human civilization.

This paper takes the "Civilization Watch Project" in Shanxi province as the object of study, and explores the role of adopting and "rationally utilizing" lower-tier cultural heritage buildings in promoting the sustainable development of these cultural resources. By combining multi-center governance theory with data from semi-structured interviews, a evolutionary game model between the adopter and the property owner is constructed. This model is used to determine the long-term optimal strategy for adopting cultural heritage buildings under the context of rational utilization. Simulation analysis using MATLAB software is conducted to examine how changes in various parameters affect the outcomes of this game. The research findings indicate that a stable strategy of "protection and utilization through cooperation" can motivate various stakeholders in society to participate in the adoption of cultural heritage buildings. Changes in social benefits can influence the adopters, while changes in economic benefits can encourage the property owners to cooperate more promptly with the adopters. The economic benefits associated with "free-riding" have a significant impact on the property owners, whereas government subsidies can only alleviate short-term difficulties and do not have a substantial effect on establishing a long-term, positive relationship for the protection and utilization of cultural heritage buildings. Finally, this paper proposes policy recommendations to promote the participation of social forces in the sustainable development of cultural heritage buildings.

National economic security is essential for a nation's survival and development. The accelerating evolution of deglobalization imposes new demands on research in this area. This paper systematically reviews the evolution of research hotspots, research trajectories, and research frameworks in the field of national economic security, both domestically and internationally, using a combination of qualitative and quantitative methods based on the CNKI and WOS databases, and puts forward future prospects. Research since 1990 can be divided into three stages. The research hotspots at different stages and in different countries show national and stage differences, but are closely linked to economic globalization, national games, and emergencies. Based on the "pressure-state-response" framework, this paper summarizes the main lines, basic logic, and evolutionary paths of national economic security research under the interaction of threats and security. The collaborative evolution of threat and security sub-fields achieves horizontal expansion and presents national and stage differences. In security assessment, differing perceptions and perspectives on national economic security lead scholars to vary in their selection of assessment methodologies, indicators, and the construction of indicator systems. Synthesizing existing literature, this paper summarizes the "3+5" security guarantee system framework of security guarantee measures, points out the national and stage differences in guarantee measures, and generates "spillover" effects. Finally, based on the linkage of multiple risks, the game of multiple subjects, the interdisciplinary research framework, and the construction of national economic security theory, future prospects are put forward.

[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.

With the acceleration of urbanization, a New Urban-Rural Amphibiousness phenomenon has emerged, characterized by groups that facilitate the bidirectional flow of key development factors across urban and rural areas. However, research on the strategic interactions among multiple actors and the integrated development mechanisms of the "agriculture and culture-tourism+" model from the urban-rural amphibiousness perspective remains limited. Taking Houguan village in Fujian province as a case, this study develops an Improved Multi-Layer Network Model based on Actor-Network Theory (ANT), which overcomes the limitations of single-theory approaches to reveal the interconnections among the village's agriculture, culture-tourism and education network. Drawing on symbiosis theory, this study elucidates the integrated development mechanism of the "agriculture, culture-tourism and education" model. The findings indicate that: (1) Houguan village has developed a multi-layer integrated mutual feedback system encompassing agriculture, culture-tourism and education. Cross-layer connections formed through dynamic interactions among actors such as the village committee, New Urban-Rural Amphibiousness groups, government agencies, and college students, which strengthen resource integration across industries. (2) Actors across all layers jointly act on the obligatory passage. Among them, the New Urban-Rural Amphibiousness groups serve as the main force, and together with the village committee, government, college students, and other participants, they constitute a stable "one superpower with multiple strong players" network pattern, ensuring the smooth operation of industrial integration. (3) This network exhibits a high global overlap rate, strong network resilience, and a high inter-layer correlation index, which enhances the network's ability to withstand external disturbances and promotes efficient resource allocation across layers. (4) Houguan village's circular symbiotic structure facilitates interactive collaboration among different groups across various industries and enables synergistic effects across layers. This structure directly contributes to the formation of an organically integrated development mechanism "agriculture and culture-tourism+ education". The findings aim to offer practical guidance and theoretical support for advancing the "agriculture and culture-tourism" pathway to rural revitalization.

The rural revitalization strategy drives rural living space to undergo a transformation from "survival carrier" to "quality space". However, existing studies have limitations such as neglecting top-down forces in the structural dimension, having unclear definitions of core fields, and insufficient integration of spatial systems. Therefore, this article follows the logical sequence of "conceptual model - multidimensional features - interaction mechanism - production mechanism" to explore the construction of a totality conceptual model and a systematic research framework for rural living spaces. The main conclusions are as follows: (1) Rural living space is the overall projection of multi-subject social relations and spatial practices in geographical space. Drawing on the socio-spatial dialectic, three-fold model of rural space and the theory of daily life practice, this paper constructs a totality conceptual model of rural living space. This model integrates the attributes of spatiality, sociality, and historicity; encompasses multiple spatial types within the multi-level county-town-village-home system; carries diverse functions such as living, employment, consumption, leisure, education, and healthcare; and incorporates the triple structure and dynamic interactions among material space, formal representation, and daily activities. (2) Based on the behavioral logic embedded in rural residents' daily activities, this study proposes the county as the core field of rural living space, highlighting the need for systematic coordination between the overall settlement environment at the village level and multi-level categorical living spaces across the county scale. (3) The production of rural living space is essentially a process whereby, under the combined influence of multi-dimensional interactions and multiple driving forces, the relational forms and coordination dynamics within the tripartite structure of formal representation-material space-daily activities undergo transformation, driving changes in both the systemic equilibrium and the functional nature of the space. Accordingly, this study proposes a research framework that progresses in layers, encompassing multidimensional characteristics, interactive mechanisms, and production mechanisms. Studying rural living space from the perspective of totality and dynamic production helps enrich the theory of rural space and provides academic support for the comprehensive revitalization of rural areas and the development of livable living spaces.

[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.

The cropland scale operation is an important development direction of cropland utilization in China. This interview conducts an in-depth discussion on the definition, influencing factors and development pathways of cropland scale operation, with the main contents outlined as follows. Firstly, the concept of "five-in-one" cropland scale operation was proposed. The concept covers various connotations, characterizing cropland scale operation from five dimensions: area expansion, the concentration of cropland property rights, the organization of cropland operation, the specialization of production activities, and the agglomeration of landscapes. These dimensions are interrelated and influence each other. Among them, the concentration of cropland management rights serve as the foundation of other dimensions, which promotes the large-scale area, organization of operation, the specialization of production activities, and the agglomeration of landscapes. Secondly, the factors influencing the cropland scale operation were analyzed. From the perspective of per capita income, in order to keep up with the continuously increasing income of urban residents, farmers will mainly achieve this by increasing the scale of cropland operation. From this perspective, the rapid development of the urban economy determines that the cropland scale operation is a long-term development trend. However, the development of the cropland scale operation is also a complex and nonlinear process, and there are many factors affecting the operation, such as agricultural subsidies, technological development, changes in agricultural product prices, and the entry of elderly labor force. In addition, land resource endowment is also a key factor affecting the cropland scale operation. The large differences between the south and north parts of China clearly illustrate this point. Thirdly, the pathway of the cropland scale operation was analyzed. The operation is a combination of the "bottom-up" approach of farmers and the "top-down" approach of the governments at different levles. The "bottom-up" approach of farmers is the main pathway for promoting the cropland scale operation by the land transfers, but most of the transfers are spontaneous and disorderly among small-scale farmers, with high transaction costs, frequent breaches of agreements, and insufficient socialized land management services. The governments mainly influence the process of the cropland scale operation. Lastly, the future pattern of cropland use in China was discussed. Due to the various reasons, different-sized cropland use entities in China will coexist for a long time. (1) The different entities may have vast differences in their demand for cropland size. For example, the cropland scale of agricultural enterprises is definitely much larger than that of elderly laborers. The opportunity cost of elderly laborers is lower, and a smaller cropland scale can meet their farming needs. The small-scale agriculture by elderly laborers delays the process of large-scale management. (2) The cropland scale operation will vary greatly across regions (such as southern and northern regions, mountainous and plain areas).

[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.

As China's urbanization enters a stage focused on enhancing the quality of existing urban stock, urban regeneration has emerged as a pivotal strategy for fostering high-quality development and the construction of a modern people-centric city. This paper aims to systematically explore the theoretical and practical pathways for advancing the modern people-centric city through urban regeneration. It begins by examining the development concepts and practical experiences of leading international cities to provide a reference for localized implementation. The study then traces the historical evolution of China's urban development priority from "production-centered" to "people-centered", underscoring the historical inevitability of this paradigm shift. Building on this analysis, the core connotations of the "modern people-centered city" are elaborated. The core section of the paper systematically outlines the pathways through which urban regeneration promotes the multidimensional goals of innovation, livability, beauty, resilience, civility, and smartness. These pathways include: comprehensive improvement guided by livability and civility; functional revitalization driven by innovation and smartness; resilience rebuilding based on security and beauty; and area-wide coordination through systematic and integrated approaches. Finally, the study proposes a systematic set of policy recommendations, offering a theoretically grounded and practically relevant framework for high-quality development of a modern people-centric city through urban regeneration.

[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".

Geography centers on the study of integrated human-environment systems, focusing on the interaction between human activities and the geographical environment. In recent years, the exploration of theories and methodologies related to resilient cities has gradually become a research hotspot in urban geography. However, existing studies on resilient cities within the field of urban geography often rely on the adaptation and transfer of external concepts, lacking integration with the core foundations rooted in the geographic perspective. In response, this article reviewed the developmental trajectory of research on resilient cities, analyzed the geographic connotations of resilient city research, and put forward the following propositions: 1) From the perspective of urban geography, resilient city research should center on the disaster risk-constrained human-environment relationships. 2) The coupled system formed by "human activities-resource endowments-disaster impacts" constitutes the principal framework for investigating resilient cities within urban geography. 3) The interactions between human activities, resource endowments, and disaster impacts across multiple temporal and spatial scales often exhibit complexities, nonlinearities, and instabilities comparable to those of the "three-body problem" in gravitational physics. 4) Both the short-term, sudden disturbances and the long-term, gradual evolution within the human-environment-disaster system can be constrained and regulated through territorial spatial governance measures. This article provides a preliminary exploration of the theory of disaster risk-constrained human-environment relationships, and presents an analysis and verification using the Zhengzhou "7·20" extreme rainstorm disaster event as a case study, aiming to provide a theoretical reference for promoting high-quality and sustainable urban development in China.

[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.

Precipitation is a key disaster-causing factor of flood disasters. Analyzing the spatiotemporal differentiation characteristics and influencing factors of precipitation in the sub-basins of the Haihe River and along the heavy-haul railways is of great significance for ensuring regional flood risk reduction and maintaining railway transportation safety. Based on the daily precipitation data (1960-2023) from the China Meteorological Administration, this study selected four indices—annual precipitation, extreme precipitation (R95pTOT), maximum one-day precipitation (Rx1day), and heavy precipitation days (R10)—to analyze the spatiotemporal differentiation characteristics of average and extreme precipitation along the heavy-haul railways in the Haihe River Basin. The Sen's slope estimator, Mann-Kendall test, and GIS spatial analysis were employed to examine precipitation trends and patterns. Then, the optimal parameters-based geographical detector method was employed to quantify the driving forces of topographic factors (elevation, slope, aspect), land-use change, and Normalized Difference Vegetation Index (NDVI) on precipitation spatial heterogeneity. The influence of atmospheric circulation factors on precipitation characteristics in the study area was also examined. Finally, based on the historical flood records, the risk of disasters induced by rainfall was evaluated. The results indicate that over the 64 years from 1960 to 2023, the R95pTOT and Rx1day in the study area showed a decreasing trend, while the annual precipitation and R10 had increased. All the sub-basins exhibited a two-phase variation characteristic in precipitation, with both average and extreme precipitation demonstrating a significant upward trend after 1997. Notable differences were observed between the western mountainous and eastern plain sub-basins. The spatial heterogeneity of precipitation indices generally followed a "high in the east, low in the west" and "increasing in the west, decreasing in the east" pattern. Terrain-related factors, particularly elevation and slope, exhibited a globally explanatory power in the spatial variation of precipitation characteristics across sub-basins of the Haihe River Basin, with elevation demonstrating the highest overall influence and the significance was markedly higher for the zones along the heavy-haul railways than for Haihe River sub-basins. The Western Pacific Subtropical High represented the primary atmospheric circulation factor influencing precipitation characteristics in the Haihe River sub-basins. Under the background of climate change, significant flood risks exist along railway corridors. These findings provide a scientific reference for flood prevention and disaster mitigation in the Haihe River Basin and for the heavy-haul railway projects.

[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.