Abstract
In comparison to naturally developed cities, a new town is strategically built in a short period of time according to development plans. It is considered as an appropriate study area for analyzing the urban climate issues such as Local Climate Zone (LCZ) and Urban Heat Islands (UHIs) phenomenon that are differently generated according to urban planning and development. However, there are few research on comparative investigation of new towns based on urban planning due to several external variables such as environmental considerations and economic situations. In this study, we suggest comprehensive method for determining and comparing changes in LCZ distribution and UHI phenomenon in two new towns in South Korea  with different urban planning. The LCZ distribution for each new town was analyzed using Sentinel 1&2 imagery as the main material, and Convolutional Neural Networks (CNN) method, a one of the deep learning algorithms. In addition, the UHI phenomenon was analyzed using Landsat imagery and the constructed LCZ map. These results have the potential to improve knowledge of the thermal environmental implications of urbanization and give guidance for sustainable urban development and maintenance when combined with architectural evaluation models.
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