FUSE Lab 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:
- How best to model past, present, and future urban environments and associated drivers?
- 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?
- 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?
- What are the pathways for urban environment improvement, land-use optimisation, and sustainable, resilient, and healthy cities?
Main Research Directions:
- Urban Environmental Changes
- Human-Environment Spatiotemporal Interaction
- Impact of Environment and Human Activities on Public Health
- Urban Environment Improvement Theory and Adaptation Pathways