Prof. WENG Qihao, Associate Director of the Research Institute for Land and Space (RILS), Director of the Research Centre for Artificial Intelligence in Geomatics, Member of the Otto Poon Charitable Foundation Smart Cities Research Institute (SCRI) and Chair Professor of Geomatics and Artificial Intelligence, and his research team have published a study titled “A deep learning-based super-resolution method for building height estimation at 2.5 m spatial resolution in the Northern Hemisphere” in the journal Remote Sensing of Environment (www.sciencedirect.com/science/article/pii/S0034425724002591).
Building height is an important indicator for assessing the level of urban development along the vertical dimension. Existing large-scale building height estimation methods focusing on coarse spatial resolution cannot reveal height variations across buildings in urban areas.
The team proposed a deep learning-based super-resolution method to generate building height maps. The researchers created an open building height dataset with 45,000 samples covering 301 cities in the Northern Hemisphere, including China, the conterminous United States, and Europe. The dataset has great potential for use in high-resolution database updating, urban planning, and natural disaster assessment. The study has also provided a new perspective on the application of cutting-edge satellite imaging technology in urban observation, measurement, monitoring, and management.
