Research

Vision:

FUSE Lab at the University of Hong Kong aims to leverage geospatial big data, data-model fusion, and advanced interdisciplinary approaches to investigate the interaction loops between urban environmental change, human activities, and public health, with the ultimate goal of contributing to sustainable and healthy cities.

Key Research Questions:

  1. How best to model past, present, and future urban environments and associated drivers?
  2. What new insights into human activity interactions with urban environments can be identified by observing spatiotemporal variabilities at the high spectral, spatial, and temporal resolution made possible by smart city sensing and advanced data science?
  3. What can we learn about built and natural environmental changes, human activities and public health from the novel data views arising from these new technologies?
  4. What are the pathways for urban environment improvement, land-use optimization, and sustainable, resilient, and healthy cities? 

Main Research Directions:

  1. Built and Natural Environmental Changes
  2. Human-Environment Spatiotemporal Interaction
  3. Impact of Environment and Human Activities on Public Health
  4. Urban Environment Improvement Theory and Adaptation Pathways

Research Grants:

  1. PI: Future Urbanity and Sustainable Environment Lab. HKU-100 Scholars Research Fund, The University of Hong Kong (2022-2025).
  2. PI: Modelling eye-level green exposure from remotely sensed bird-view observations with a novel 2D to 3D transformation framework. URC Small Equipment Grant, The University of Hong Kong (2022-2024).
  3. PI: Urban land use monitor (LUMonitor): Time-series mapping of essential urban land use categories in the Greater Bay Area. URC Seed Fund for Basic Research, The University of Hong Kong (2022-2024).
  4. PI: Integrated Assessment of Urban Climate-related Risks: Hazard, Exposure, and Vulnerability. URC Seed Fund for Strategic Interdisciplinary Research Scheme, The University of Hong Kong (2022-2025).
  5. PI: Understanding urban greenspace exposure across space and time in China: A novel integration of human-greenspace dynamics and environmental exposure models. Research Grant Council (RGC) of Hong Kong Early Career Scheme (ECS) (2023-2025).
  6. PI: Parcel-based urban land use classification using domain adaptations of sample, feature, and model. National Natural Science Foundation of China (NSFC) Young Scientists Fund (2023-2025).
  7. PI: Long-term dynamic monitoring of Land-Sea Interface (LSI) in the Greater Bay Area using remote sensing big data. Natural Science Fund of Guangdong Province General Scheme (2023-2025).
  8. Co-PI: iEarth in support of Sustainable Development Goals. International Research Center of Big Data for Sustainable Development Goals Domestic Node Cultivation Project. (2022-2023).
  9. Co-PI: Examining Relationships between Built Environment and Burden of Diseases: A study across Global, National, and Cities Scales. Seed Funding for Strategic Interdisciplinary Research Scheme, The University of Hong Kong (2022-2024).
  10. Co-I: Climate Change-induced Flooding Risks towards Urban Property Market. URC Seed Fund for Basic Research, The University of Hong Kong (2022-2024).