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Two out of three people will be living in urban areas by 2050, as projectedby the United Nations, emphasizing the need for sustainable urban developmentand monitoring. Common urban footprint data provide high-resolution cityextents but lack essential information on the distribution, pattern, andcharacteristics. The Local Climate Zone (LCZ) offers an efficient andstandardized framework that can delineate the internal structure andcharacteristics of urban areas. Global-scale LCZ mapping has been explored, butare limited by low accuracy, variable labeling quality, or domain adaptationchallenges. Instead, this study developed a custom LCZ data to map key Koreancities using a multi-scale convolutional neural network. Results demonstratedthat using a novel, custom LCZ data with deep learning can generate moreaccurate LCZ map results compared to conventional community-based LCZ mappingwith machine learning as well as transfer learning of the global So2Satdataset.

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