Based in Shenzhen, serving the Greater Bay Area, and reaching across China, the center is a cross-disciplinary innovation platform for basic research, technology development, translational applications, and talent training.
Background and Rationale
Spatio-temporal intelligence is an engine for new productive forces built on spatio-temporal sensing, cognition, and decision-making. It supports connected systems, digital twins, low-altitude economy, autonomous driving, embodied intelligence, and smart cities. National strategies increasingly recognize spatio-temporal information and positioning/navigation services as important new infrastructure, while Shenzhen’s “20+8” industrial clusters provide dense application scenarios for AI, robotics, aerospace, intelligent connected vehicles, and smart cities.
The center aligns with Shenzhen Technology University’s mission as a new research-oriented university of applied sciences: applying knowledge through scholarship and advancing scholarship through applications. It connects national needs, Greater Bay Area industrial scenarios, and emerging interdisciplinary research across AI, geospatial information, remote sensing, navigation, robotics, and intelligent equipment.
Positioning and Development Goals
The center is positioned as a key university-level interdisciplinary research platform, an incubation platform for a future Guangdong provincial key laboratory, a source of spatio-temporal intelligence innovation in the Greater Bay Area, and a vehicle for industry-university-research collaboration. Its development is driven by interdisciplinary integration, real-world scenarios, and high-impact research outputs.
Organization and Team Foundation
The center adopts a director-responsibility model and establishes an academic advisory committee. With academic guidance, team collaboration, project-driven research, and open sharing mechanisms, it builds an operating system that connects internal university teams, external partners, and industry collaborations. The center is planned around three teams: multi-source fusion positioning, urban spatio-temporal intelligence, and embodied intelligent sensing.
Academic Guidance
Academician Qingquan Li is invited as Chief Scientific Advisor to provide strategic consultation and academic guidance.
Core Team
The core team covers urban computing, embodied intelligence, SAR interferometry, real-time positioning, geospatial big data, and AI modeling.
Open Collaboration
The center collaborates with high-level external teams on spatio-temporal big data, spatial intelligence, autonomous surveying, and traffic analytics.
Three Research Directions
Developing precise positioning, autonomous navigation, and autonomous surveying methods for low-altitude economy, inspection, emergency mapping, and intelligent equipment.
Integrating urban spatio-temporal big data, AI, and multimodal foundation models to improve urban cognition, prediction, optimization, and decision support.
Studying environment understanding, task decision-making, and safe autonomous operations for rail transit, underground space, enclosed space, and complex facility inspection.
2026 Key Work
In 2026, the center will advance its launch through organization, platform construction, research programs, and scenario-driven applications.
Organization
Complete the launch, form the advisory committee, establish regular academic exchange, and refine governance for projects, equipment, IP, safety, and finance.
Research
Organize project applications around the three research directions, target 2-3 provincial/ministerial or higher-level projects, and cultivate high-impact awards and publications.
Translation
Engage leading enterprises in city governance, intelligent inspection, low-altitude operations, and spatial sensing to build demonstrative applications.
Facilities
Build GPU computing, storage, and network infrastructure, plus sensor calibration, intelligent sensing testbeds, and embodied intelligence simulation environments.
Related News
- Apr. 2026: Associate Professor Jinzhou Cao was appointed Executive Director of the Spatiotemporal Intelligence Research Center.
- Jun. 2026: Center-related paper UrbanMMCL was published in ISPRS Journal of Photogrammetry and Remote Sensing.
- Feb. 2026: Center-related paper Sat2Flow was published at AAAI 2026.
- Jan. 2026: A Guangdong General Program project on multimodal deep features for urban spatial representation and intelligent decision-making started.
- Dec. 2025: Invited talks on urban foundation models were delivered at SIAT, Chinese Academy of Sciences, and Peking University Shenzhen Graduate School.
- Nov. 2025: Center-related paper ST-camba was published in Information Fusion.
- Nov. 2024: A Shenzhen General Program project on multimodal representation learning for urban agglomeration monitoring and evaluation was awarded.