A University-level Interdisciplinary Platform
The center connects artificial intelligence, transportation, remote sensing, urban science, and data science to build cross-school research capacity.
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.
National StrategyServing new infrastructure and urban digital transformation
Bay Area ScenariosSupporting low-altitude economy, smart cities, and intelligent equipment
Interdisciplinary ResearchConnecting AI, GIS, remote sensing, navigation, and robotics
Center Positioning
The center is driven by interdisciplinary integration, real-world scenarios, and high-impact research outputs. It focuses on strategic application domains such as urban governance, intelligent transportation, embodied intelligence, low-altitude economy, and smart manufacturing.
An Incubation Platform for Future Key Laboratories
It consolidates research directions, teams, platforms, and outputs around core spatio-temporal intelligence problems.
An Innovation Source for the Greater Bay Area
The center studies sensing, modeling, simulation, and decision support for complex urban systems in Shenzhen and the Greater Bay Area.
A Vehicle for Collaborative Translation
It bridges government, industry, and university research to translate spatio-temporal intelligence into urban, mobility, and industrial applications.
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
Autonomous Sensing, Navigation, and Positioning
Developing precise positioning, autonomous navigation, and autonomous surveying methods for low-altitude economy, inspection, emergency mapping, and intelligent equipment.
Low-altitude economy · Autonomous navigation · Emergency mappingUrban Spatial Intelligence and Foundation Models
Integrating urban spatio-temporal big data, AI, and multimodal foundation models to improve urban cognition, prediction, optimization, and decision support.
Urban foundation models · Multimodal fusion · Decision supportEmbodied Spatial Intelligence and Autonomous Operation
Studying environment understanding, task decision-making, and safe autonomous operations for rail transit, underground space, enclosed space, and complex facility inspection.
Embodied intelligence · Scene understanding · Autonomous operationRelated News
Platform
Jinzhou Cao was appointed Executive Director of STIC
The center entered its platform-building phase, organizing research infrastructure, interdisciplinary teams, and application scenarios.
Research
UrbanMMCL was published in ISPRS Journal
The study introduces a multimodal and multi-graph self-supervised contrastive learning framework for urban region representation.
Research
Sat2Flow was published at AAAI 2026
The work explores structure-aware diffusion for generating human flows from satellite imagery, bridging remote sensing and urban flow modeling.
Project
Guangdong Natural Science Foundation project started
The project studies multimodal deep features for urban spatial representation and intelligent decision-making.
Research
ST-camba was published in Information Fusion
The paper extends the center's technical foundation in spatio-temporal multimodal fusion and urban dynamics understanding.