Developing High Quality Training Samples for Deep Learning Based Local Climate Zone Classification in Korea
This work addresses the need for sustainable urban development and monitoring by improving LCZ classification accuracy in Korea, though it is incremental as it builds on existing deep learning approaches with custom data.
This study tackled the problem of low accuracy and variable labeling quality in global-scale Local Climate Zone (LCZ) mapping by developing custom LCZ data for Korean cities using a multi-scale convolutional neural network, resulting in more accurate LCZ maps compared to conventional methods and transfer learning from the global So2Sat dataset.
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 essential information on the distribution, pattern, and characteristics. The Local Climate Zone (LCZ) offers an efficient and standardized framework that can delineate the internal structure and characteristics of urban areas. Global-scale LCZ mapping has been explored, but are limited by low accuracy, variable labeling quality, or domain adaptation challenges. Instead, this study developed a custom LCZ data to map key Korean cities using a multi-scale convolutional neural network. Results demonstrated that using a novel, custom LCZ data with deep learning can generate more accurate LCZ map results compared to conventional community-based LCZ mapping with machine learning as well as transfer learning of the global So2Sat dataset.