Closely aligned with the national strategies for Digital China and next-generation smart city development, this track draws on core technologies including space-air-ground integrated remote sensing, BeiDou positioning, spatiotemporal big data, and AI-enabled intelligent analysis. It focuses on four high-priority practical scenarios: 15-minute convenient living circles, resilient cities, urban lifeline safety, and refined urban governance. With an emphasis on improving public well-being, revitalizing urban spaces, strengthening safety safeguards, and enhancing governance efficiency, the track seeks implementable, reusable, and scalable digital solutions and innovative application systems. Submissions should demonstrate practical relevance, innovation, and deployability, thereby supporting the modernization of urban governance.

This track focuses on the integration of artificial intelligence with urban spatiotemporal big-data analytics. It addresses major urban-governance challenges such as traffic congestion, environmental pollution, structural imbalance, and insufficient resilience, with the objectives of intelligent anticipation, collaborative computing, and precise intervention. AI technologies are used to empower smart city development, operation, and maintenance. Participants are encouraged to conduct application-oriented research and development in scenarios such as urban operational situation awareness, public-safety supervision, and ecological and environmental protection, drawing on frontier technologies including space-air-ground sensing, urban foundation models, and spatiotemporal agents. The track values technological foresight and scalability and will place particular emphasis on multi-source spatiotemporal data fusion, the construction of large-model and intelligent-system architectures, and adaptability to application scenarios.

This track focuses on dynamic urban monitoring and smart city development through the deep integration of multispectral remote sensing, InSAR, spatiotemporal big data, and artificial intelligence. Target scenarios include urban expansion, land subsidence, urban health assessment, and digital twins. Participating teams are encouraged to use multi-source space-air-ground data, including long time-series remote-sensing imagery, time-series SAR data, street-view imagery, and IoT data, together with AI-assisted analytics and attribution models to accurately identify the spatiotemporal characteristics of urban change, quantitatively reveal driving factors and their coupling mechanisms, and explore end-to-end solutions spanning status perception, health diagnosis, digital reconstruction, and intelligent decision-making. The track values academic depth and potential for broader application. It assesses capabilities in multi-source data fusion, time-series analysis, intelligent modeling, and practical scenario implementation, with the aim of using space-air-ground information technology to support urban safety and sustainable development.

Open to teams from vocational colleges, universities, and non-academic organizations, this track focuses on the integrated application of BIM with CIM, GIS, real-scene 3D, IoT, AI, and space-air-ground data. It targets smart city scenarios such as urban renewal, infrastructure operation and maintenance, resilience and safety, and green and low-carbon development. Participants are encouraged to produce deployable, demonstrable, and scalable digital-twin models, simulation analyses, and operational application solutions.

Against the backdrop of urban development increasingly centered on existing built-up areas, this track focuses on digital practices for urban renewal. Target scenarios include the regeneration of underutilized spaces, revitalization of historic districts and industrial heritage, and improvement of public-space quality. Participating teams are encouraged to integrate multi-source data such as remote-sensing imagery, street-view imagery, points of interest (POIs), pedestrian-flow heat maps, and real-scene 3D data, together with space-air-ground information technologies including AI-based recognition, GIS spatial analysis, and digital twins. Teams shall conduct spatial diagnosis, value assessment, and renewal planning and develop digital and intelligent urban-renewal solutions that are innovative, practical, and scalable.

Since its inclusion in the national transportation development plan in 2021, the low-altitude economy has been regarded as a strategic emerging pillar industry. However, its development has been slowed by the central challenge of collision risk. This issue is especially acute in cities, where demand for low-altitude applications is greatest but large-scale operations have yet to take off. This track focuses on the deep integration of the low-altitude economy with smart cities and on core technological breakthroughs and scenario-based applications that use surveying, mapping, and remote sensing to support the safe and efficient development of urban low-altitude operations.

This track focuses on integrated innovation involving GIS, artificial intelligence, UAV aerial surveying, and real-scene 3D technologies in typical smart city scenarios such as disaster emergency response, urban governance, and spatial analysis. Participating teams are encouraged to draw on geographic information science and frontier technologies such as deep learning, multimodal large models, and intelligent 3D interpretation to conduct algorithm development, system construction, and comprehensive application practice. The goal is to create technically robust, practical, and deployable GIS innovation solutions that fully demonstrate the value of space-air-ground integrated geographic information technology in urban digital development.

As a new factor of production, data elements are a core engine for citywide intelligent decision-making. However, data silos and insufficient value extraction remain major constraints on cities’ transition toward precise and intelligent governance. This track focuses on the deep integration of data elements with smart cities, promotes a shift from experience-based decision-making to data-driven intelligence in transportation, industry, public safety, and other scenarios, and encourages emerging models such as data-asset operations and city-level digital-twin decision-making. Participants are encouraged to conduct key technological research and application innovation in data-element governance and scenario-based value-added services.