Urban Remote Sensing, Machine Learning, Satellite Image Processing

Local climate zone classification using a multi-scale, multi-level attention network

Local Climate Zones (LCZ) offer a climate-aware and standardized classification scheme composed of 17 urban and natural landscape classes. Recent deep learning-based LCZ classification studies have adopted a scene classification approach with …

Mapping Inaccessible Areas Using Deep Learning based Semantic Segmentation of VHR Satellite Images with OpenStreetMap Data

Remote sensing is crucial for mapping and developing geospatial information of inaccessible areas. In particular, supervised classification or semantic segmentation of very high resolution (VHR) satellite images are used to extract key features such …

Developing High Quality Training Samples for Deep Learning Based Local Climate Classification in Korea

Two out of three people will be living in urban areas by 2050, as projected by the United Nations, emphasizing the need for sustainable urban development and monitoring. Common urban footprint data provide high-resolution city extents but lack …