Prof. Qihao WENG, Associate Director of the Research Institute for Land and Space (RILS) and his team have developed an integrated time series mapping method to enhance the land use and land cover (LULC) mapping accuracy and frequency in cloud-prone areas. Other key PolyU members on the research team include Prof. Xiaoli DING, Director of RILS, and Dr Zhiwei LI, Research Assistant Professor in the Department of Land Surveying and Geo-Informatics.
The method incorporates spectral-indices-fused deep learning models and time series reconstruction techniques. The team applied the method to the cloud- and rain-prone Pearl River Delta (i.e., Guangdong–Hong Kong–Macao Greater Bay Area, GBA), and yielded an overall mapping accuracy of up to 87.01%, outperforming existing LULC products.
This method has the potential to generate seamless and near real-time maps for different regions in the world by using deep learning models trained on datasets collected globally. It can provide high-quality LULC data sets at different time intervals for various land and water dynamics in cloud- and rain-prone regions.
Read the full paper: https://shorturl.at/jyj1B
| Research Units | Research Institute for Land and Space |
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