Research
Intelligent Fusion of Multisource Remote Sensing Data for Dynamic Land Use Mapping and Flood Risk Assessment
Research Objectives
To develop advanced methods for intelligent reconstruction and fusion of multisource remote sensing data.
To generate monthly dynamic land use maps with 10 m resolution in the Greater Bay Area (GBA).
To build a near-real-time dynamic assessment scheme for flood risks in the GBA.
Research Work
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A Deep Learning-Based Sky View Factor (SVF) Estimation Using Satellite Imagery Over Global Urban Areas
Research Objectives
To develop the deep learning-based sky view factor (SVF) estimation algorithm using satellite imagery.
To map a seamless 10 m resolution SVF for global urban areas.
To explore the use of the developed SVF for conducting urban climate studies.
Research Work
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Understanding the urban heat island phenomenon over Asian megacities situated over different climatic zone to develop a proper mitigation strategy to improve thermal comfort outdoors
Research Objectives
Quantifying the response of urban heat islands to urbanization and assessing the linkage of human modification to change in land surface temperature.
Investigating the long-term trend of surface and canopy urban heat islands and its relation to the formation of heatwaves.
Examining the effect of greenery and surface albedo in improving the outdoor thermal comfort as a mitigation strategy to urban heat islands.
Research Work
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Geo-location-aware Split-modal Representation learning for Global Urban Mapping
Research Objectives
To develop intelligent strategy for multimodal feature extraction from unlabelled remote sensing data.
To derive a highly generalizable model utilizing geo-location information.
To generate global seamless urban map with 10 m resolution.
Research Work
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