<sec><title>Objective</title> Climate change has intensified global social risks and ecological crises. The increasing frequency of climate hazards such as heatwaves and extreme precipitation, combined with the persistent degradation of forests, soils, and other ecosystems, has become a critical constraint on the sustainable development of human societies. In response to climate change, the Intergovernmental Panel on Climate Change (IPCC) has proposed two overarching strategies: climate mitigation and climate adaptation. Climate adaptation refers to reducing climate-related risks by decreasing human vulnerability or safeguarding ecosystem service functions, and ecological restoration is widely regarded as one of the key approaches to climate adaptation. Ecological restoration can effectively enhance ecosystem services, such as increasing carbon sequestration, reducing surface temperature, and improving water conservation capacity—thereby demonstrating substantial potential for strengthening climate adaptability. However, although existing standards and practices of territorial ecological restoration repeatedly emphasize the integration of climate adaptation considerations, the specific types of climate adaptation within territorial ecological restoration, as well as the pathways and methods for identifying restoration areas and determining their priority with climate adaptation as a core objective, remain insufficiently defined. Therefore, clarifying climate adaptation types relevant to territorial ecological restoration and developing robust identification and priority-setting pathways and methods constitute an urgent research agenda. </sec><sec><title>Methods</title> Taking the Chengdu-Chongqing urban agglomeration as the study area, from an exposure−sensitivity−resilience perspective, three types of territorial ecological restoration zones for climate adaption are defined: climate-exposed restoration zone (CERZ), climate-sensitive restoration zone (CSRZ), and climate-resilient restoration zone (CRRZ). The research route of this study consists of three major steps. 1) Based on the concepts of CERZ, CSRZ, and CRRZ and previous research findings, sample datasets and a double-layer indicator system comprising a “core-characteristic factors” were established to construct the machine learning dataset. 2) An ensemble machine learning model integrating three algorithms—maximum entropy (Maxent), random forest (RF), and categorical boosting (CatBoost)—through logistic regression (LR) was developed. The model performance was evaluated, and the spatial extents and restoration priorities of CERZ, CSRZ, and CRRZ were identified. 3) Differentiated response strategies were proposed according to the identification results and characteristics of CERZ, CSRZ, and CRRZ. </sec><sec><title>Results</title> 1) Compared with the single-layer system, the double-layer framework improves the identification performance by approximately 5%, and the ensemble model improves it by around 3% compared with individual algorithms. 2) Both CERZ and CSRZ show more than 20% of areas at medium or higher restoration priority, indicating an urgent need for ecological restoration in the study area. Meanwhile, CRRZ with medium or higher restoration priority accounts for over 50% of the Chengdu-Chongqing urban agglomeration, suggesting that regional resilience enhancement remains a long-term and challenging task. The spatial patterns of restoration priority for the three zones exhibit the following characteristics. For CERZ, the proportions of restoration priority from high to low are 4.98%, 10.56%, 13.38%, 19.77% and 51.31%. Restoration areas above medium priority total 53,020.54 km², mainly distributed in the karst regions of southern Sichuan and the Three Gorges Reservoir Area in northeastern Chongqing. For CSRZ, the proportions from high to low are 4.63%, 9.49%, 12.74%, 36.31% and 36.83%. Restoration areas above medium priority total 49,252.83 km², exhibiting more fragmented patches and lower clustering than CERZ. Unlike CERZ, restoration priority increases within medium and large cities, especially in areas characterized by fragmented vegetation and dense river nets. For CRRZ, the proportions from high to low are 11.50%, 26.60%, 26.27%, 18.61%, 17.02%. Restoration areas above medium priority total 118,035.92 km², showing both concentrated and dispersed patterns across low-altitude plains and hilly regions in the central parts of the Chengdu-Chongqing urban agglomeration. 3) CERZ aims to integrate engineering, ecological, and economic measures to effectively reduce the direct damage caused by extreme climate events. CSRZ aims to take river basins or key ecological function areas as spatial units to promote the recovery of ecological functions and the construction of ecological networks. CRRZ aims to advance the coordinated development of a green economic system and green infrastructure networks. </sec><sec><title>Conclusion</title> By building double-layer indicator system and ensemble machine learning model, this study clearly reveals the differentiated patterns of territorial ecological restoration in the Chengdu-Chongqing urban agglomeration from an exposure−sensitivity−resilience perspective. The findings provide a replicable framework and technical reference for the scientific and rapid identification of the key areas and nodes of territorial ecological restoration, optimizing restoration layouts and resource allocation, and enhancing regional climate adaptability. In the future, our research may incorporate higher-resolution datasets of samples and indicators to further refine the identification and planning of territorial ecological restoration zones. </sec>

<sec><title>Objective</title> The low-altitude economy covers low-altitude manufacturing, flight operations, support services and comprehensive service industries, and has the characteristics of spatially three-dimensional, regional dependence, digital ecology, industrial integration and radiation driving. With the continuous development of digitization and informatization, these characteristics increasingly affect the interaction between low-altitude activities and urban and natural environments, which provides new ideas for the development of landscape architecture, and the coordinated development of the two is an important issue in the transformation of landscape architecture. </sec><sec><title>Methods</title> The knowledge map method was used to analyze the research status of environmental low-altitude economy at home and abroad. By combing and reading relevant literature at home and abroad, the research trends of low-altitude economy in logistics, aviation, ecological monitoring and other fields were systematically analyzed by reviewing literature research, case studies and comparative analysis. </sec><sec><title>Results</title> By analyzing the domestic and foreign research, the internal mechanism of the cross research between landscape architecture and low-altitude economy is revealed four aspects. In the aspect of technology application, low-altitude aircraft tend to be transformed from perception tools to "design intelligence" which can guide ecological design and space construction. In the aspect of spatial planning, landscape architecture will realize the spatial transformation from plane extension to three-dimensional reconstruction under the development of low-altitude economy. In the aspect of ecological impact, the research focus has changed from identifying environmental risks by using low-altitude facilities to systematic assessment and control of ecology. In the aspect of humanistic experience, the combination of low-altitude economy and cultural narrative has further stimulated the vitality of landscape architecture discipline. It is found that under the influence of low-altitude economy, the development of landscape architecture faces some new problems, such as imperfect policies and regulations, insufficient adaptability of spatial planning system, systematic lack of ecological protection, technical bottleneck restriction, homogenization dilemma of cultural and tourism integration, including lack of unified standards for low-altitude facility design, traditional two-dimensional planning being difficult to meet the needs of air-space coordination, ecological destruction caused by noise and habitat disturbance, lack of ecological protection system, etc. There are technical bottlenecks in data processing and flight stability; homogenization of tourism products, focusing on tourism over cultural innovation. In order to promote the coordinated development of the two, this paper puts forward the implementation path of the integration of low-altitude economy and landscape architecture: to ensure the adaptation of policies and regulations, to improve policies and regulations and technical standards, to formulate low-altitude greening design and ecological evaluation standards, and to lay a foundation for the integration of landscape architecture and low-altitude economy; Conduct spatial value evaluation, clarify the use, development subject and income distribution mechanism of each low-altitude area, promote the market-oriented operation of public resources, and use unmanned aerial vehicles to carry out ecological background analysis and evaluate the ecological carrying capacity of low-altitude activities; Conduct functional compound planning, make landscape architecture break through the limitation of traditional ground perspective, bring low-altitude airspace into the vertical space system of landscape architecture, add low-altitude related infrastructure in the garden, and realize efficient coordinated utilization of airspace and ground resources; In ecological service monitoring, we should increase the investment in R&amp;D of UAV technology, low-altitude aircraft noise reduction technology, flight safety guarantee technology, etc., improve the intelligent level of landscape architecture monitoring and management, and establish a new ecological assessment mode; Promote the integrated development of culture and tourism, break through the homogenization dilemma from three dimensions of cultural empowerment, spatial differentiation and experience depth, and integrate garden cultural elements into low-altitude experience links, to improve the overall operation effect. </sec><sec><title>Conclusion</title> Based on the research, we have drawn conclusions in three aspects. 1) The global low-altitude economy industry will become the next development hotspot, and it should accelerate the integration with transportation logistics, cultural tourism and other formats, expand more application scenarios, promote the integration of landscape architecture and related industries, and activate the new vitality of landscape architecture. 2) Green space and parks in cities will become important carriers for low-altitude transportation in the future, and planning and design of low-altitude composite public space will be the focus of research, and research will move from two-dimensional garden aesthetic space to three-dimensional traffic spatial pattern. 3) The development of unmanned aerial vehicle and related technologies provide refined intelligent solutions for intelligent garden management, and low-altitude monitoring data provide new tools for ecological value assessments of gardens, significantly improve ecological monitoring and management levels. </sec>

<sec><title>Objective</title> As urban design faces increasing demands for contextual responsiveness, iterative optimization, and data-informed reasoning, integrating artificial intelligence into the design process has gained renewed relevance. Among emerging technologies, generative artificial intelligence (GAI) shows strong potential for automating content creation and simulating spatial configurations. This research provides a comprehensive review of recent developments in the application of GAI to urban design. The research identifies representative technical pathways, their respective intervention stages, and the functional mechanisms by which generative models are reshaping the design workflow. This research presents a structured, theory-informed synthesis of how different generative models contribute to tasks such as intention modeling, spatial reasoning, and performance-driven design. Building on design thinking and a descriptive lens informed by the technology acceptance model (TAM), the research examines how model type, data modality, and task characteristics affect GAI’s functional role, usability, and acceptance. Particular attention is given to mapping deployment forms, from isolated tools to coordinated multi-model workflows, and to characterizing cross-cutting challenges of controllability, transparency, and contextual adaptability in urban design settings. </sec><sec><title>Methods</title> Following the PRISMA protocol, the research conducts a multi-stage literature review combining automated search and expert screening. A total of 125 peer-reviewed articles and high-impact preprints are selected from Web of Science, CNKI, arXiv, and selected industry sources, covering the period from 2014 to July 2025. Search terms such as “generative AI”, “AIGC”, “GAN”, “diffusion model”, “variational autoencoder”, “autoregressive model”, “large language model”, and urban-related keywords are used in various combinations. Based on the collected literature, four types of generative models are summarized as image-driven, language-driven, structure-driven, and feedback-optimized models, according to their application characteristics in urban design tasks. These types are aligned with four stages of the design process: preliminary analysis, scheme generation, evaluation and decision-making, and outcome expression. On this basis, a two-dimensional framework to examine how different GAI pathways intervene across tasks is formed. To refine the mapping, each design stage is further broken down into three representative sub-tasks. Preliminary analysis includes public demand analysis, urban data enhancement, case/task framing, and spatial element recognition. Scheme generation covers design intention modeling, spatial layout generation, and 3D form construction. The evaluation and decision-making stage includes multi-objective optimization, scheme evaluation, and scenario prediction. The final expression stage involves textual documentation, 2D representation, and visual rendering. A quantitative analysis is also conducted to show the distribution of model types over design stages, identify common combinations, and trace the evolution of research focus over time. TAM informs a descriptive synthesis of perceived usefulness (PU) and perceived ease of use (PEU) across model types to illuminate adoption patterns. </sec><sec><title>Results</title> The findings reveal that GAI models are increasingly integrated into urban design workflows but exhibit uneven adoption across task types and modalities. Image-driven models dominate in both early-stage analysis and final visual representation due to their high interpretability, usability, and compatibility with existing design practices. Language-driven models are commonly used in public demand analysis, participatory planning, and scenario scripting, enabled by the rise of large language models (LLMs) such as ChatGPT and DeepSeek. Structure-driven models, though less prevalent, show promise in generating street networks, land-parcel layouts, and spatial typologies using graph-based logic. Feedback-optimized models, which rely on reinforcement learning, evolutionary algorithms, and performance simulation are the least adopted, but demonstrate strong potential in multi-objective optimization and iterative decision-making. Recent research indicates an increasing use of multi-model workflows, such as text-to-image pipelines integrated with urban simulation or feedback loops. While GAI applications increasingly support design iteration, their adoption is heavily influenced by the controllability, explainability, and contextual adaptability of models. PU and PEU vary significantly by model type, with image-driven models rated highest and structure-driven and feedback-optimized models facing usability challenges due to complexity and low transparency. </sec><sec><title>Conclusion</title> Although GAI has demonstrated broad applicability across the urban design process, current implementations are largely procedural and auxiliary in nature. Most models recombine existing inputs rather than construct original logic, and few possess autonomous reasoning or normative awareness. This limits their role to content augmentation rather than conceptual guidance in design development. Moreover, issues such as opaque decision logic, lack of domain-specific knowledge embedding, and poor adaptability to local planning norms hinder practical adoption. Addressing these challenges requires multi-level efforts: 1) Construct structured, regionally grounded urban design datasets; 2) improve model interpretability, controllability, and responsiveness to professional input; and 3) develop modular, multi-model systems that support seamless interaction across design stages. Human – AI collaboration mechanisms — especially those based on iterative prompts and semantic feedback-must be enhanced to enable AI not just as a tool, but as an active design partner. This review offers a comprehensive reference for scholars and practitioners seeking to understand how GAI is reshaping the logic, structure, and agency of urban design. </sec>

<sec><title>Objective</title> As urban construction evolves into an era of stock development, Shenzhen has seen an increase in the number of parks to more than 1,320. This growth marks a significant shift from an emphasis on hardware construction to a focus on soft services. However, challenges such as the mismatch between park service supply and the diverse demands of citizens, as well as insufficient management efficiency, have become increasingly evident. To address these issues, strategies such as “scenario-based service optimization” and “data-driven management collaboration” can be adopted. These approaches aim to break the cycle of “emphasizing construction over operation” in parks, thereby promoting high-quality operational services and effective management within Shenzhen’s parks. </sec><sec><title>Methods</title> The research pathway is delineated as “demand analysis – service optimization – management collaboration”. This research utilizes data from the <italic>Big Data Survey Report on</italic> “<italic>Open and Shared Scenarios</italic>” <italic>of Urban Park Green Spaces in Shenzhen</italic> (2023), as well as on-site research data from various parks. A cluster analysis is conducted to identify high-frequency activity scenarios among citizens. By combining functional and spatial analyses of the parks under research, the research proposes a quantitative model to assess the gap in park service demand. This model critically analyzes the discrepancies between the existing layout of park facilities and the actual needs of visitors. To enhance park service efficiency and improve visitor experiences, several factors are taken into account when determining the scale of services. These include park location, functional characteristics, site size, visitor flow, and demographics. The analysis integrates existing supporting service facilities to create a “minimum service unit” calculation model. This model identifies strategically located stations within the parks that provide essential daily services and consumption experiences for citizens. By ensuring these stations cover all main activity areas, the research aims to guarantee that visitors can access necessary services within a reasonable walking distance. This approach facilitates precise configuration and dynamic allocation of service facilities, ultimately enhancing the overall service quality of the parks. </sec><sec><title>Results</title> The core of this research is centered around the strategies for collaborative park service optimization and management. From a hardware perspective, service upgrades are achieved through a combination of layout optimization, scale adjustments, and elastic supply strategies for service facilities. On the software side, three primary strategies are proposed, focusing on the perspectives of construction management departments, users, and operational service institutions. These strategies include enhancing data governance levels, optimizing the management of activity scenarios, and innovating policy mechanisms. These strategies can help establish a closed-loop linkage among public demand, service supply, and management response. </sec><sec><title>Conclusion</title> As parks serve as the core carriers of urban public space, it is essential for them to respond proactively to citizens’ needs. This can be achieved through the deep integration of service scenarios with intelligent management systems. By employing the strategies for collaborative improvement of service management, a bridge can be constructed among the public, the market, and the government. This approach not only improves the efficiency and quality of park services but also alleviates financial pressures on government bodies. Moreover, leveraging market-oriented innovations to improve service quality can provide both theoretical support and practical models for the sustainable development of parks in Shenzhen and other high-density cities. This transition encourages the upgrading of parks from merely “space supply” to a model of “value co-creation”. In this context, parks can evolve into dynamic spaces that not only fulfill recreational needs but also serve as integral components of urban life that promote social interaction, environmental sustainability, and community well-being. By aligning the interests of various stakeholders, including citizens, local businesses, and governmental agencies, we can foster a collaborative ecosystem that enhances the overall quality of urban life. To facilitate this transition, continuous feedback mechanisms must be established. Regular surveys and data collection can help park managers stay attuned to the changing needs and preferences of visitors, and this ongoing dialogue between the two will ensure that parks remain relevant and responsive to the community they serve. In conclusion, the development of parks in Shenzhen requires a multifaceted approach that embraces both technological advancements and community engagement. By prioritizing service optimization and effective management collaboration, we can create parks that not only meet current demands but also anticipate future needs. This proactive stance will help ensure that parks in Shenzhen thrive not only as green spaces but also as vibrant hubs of social and cultural activities, ultimately contributing to the city’s long-term sustainability and livability. Through these efforts, we can redefine the role of urban parks, transforming them into essential elements that contribute positively to the urban fabric, thus fostering a sense of community and enhancing the quality of life for all citizens. </sec>