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Self-Training Classification Framework with Spatial-Contextual Information for Local Climate Zones
Open AccessArticle

Improving Local Climate Zone Classification Using Incomplete Building Data and Sentinel 2 Images Based on Convolutional Neural Networks

School of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan 44919, Korea
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Author to whom correspondence should be addressed.
These authors contributed equally to the paper.
Remote Sens. 2020, 12(21), 3552; https://doi.org/10.3390/rs12213552
Received: 23 September 2020 / Revised: 21 October 2020 / Accepted: 26 October 2020 / Published: 30 October 2020
(This article belongs to the Special Issue Application of Remote Sensing in Urban Climatology)
Recent studies have enhanced the mapping performance of the local climate zone (LCZ), a standard framework for evaluating urban form and function for urban heat island research, through remote sensing (RS) images and deep learning classifiers such as convolutional neural networks (CNNs). The accuracy in the urban-type LCZ (LCZ1-10), however, remains relatively low because RS data cannot provide vertical or horizontal building components in detail. Geographic information system (GIS)-based building datasets can be used as primary sources in LCZ classification, but there is a limit to using them as input data for CNN due to their incompleteness. This study proposes novel methods to classify LCZ using Sentinel 2 images and incomplete building data based on a CNN classifier. We designed three schemes (S1, S2, and a scheme fusion; SF) for mapping 50 m LCZs in two megacities: Berlin and Seoul. S1 used only RS images, and S2 used RS and building components such as area and height (or the number of stories). SF combined two schemes (S1 and S2) based on three conditions, mainly focusing on the confidence level of the CNN classifier. When compared to S1, the overall accuracies for all LCZ classes (OA) and the urban-type LCZ (OAurb) of SF increased by about 4% and 7–9%, respectively, for the two study areas. This study shows that SF can compensate for the imperfections in the building data, which causes misclassifications in S2. The suggested approach can be excellent guidance to produce a high accuracy LCZ map for cities where building databases can be obtained, even if they are incomplete. View Full-Text
Keywords: local climate zone; urban climate; convolutional neural networks; building information; Sentinel local climate zone; urban climate; convolutional neural networks; building information; Sentinel
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MDPI and ACS Style

Yoo, C.; Lee, Y.; Cho, D.; Im, J.; Han, D. Improving Local Climate Zone Classification Using Incomplete Building Data and Sentinel 2 Images Based on Convolutional Neural Networks. Remote Sens. 2020, 12, 3552. https://doi.org/10.3390/rs12213552

AMA Style

Yoo C, Lee Y, Cho D, Im J, Han D. Improving Local Climate Zone Classification Using Incomplete Building Data and Sentinel 2 Images Based on Convolutional Neural Networks. Remote Sensing. 2020; 12(21):3552. https://doi.org/10.3390/rs12213552

Chicago/Turabian Style

Yoo, Cheolhee; Lee, Yeonsu; Cho, Dongjin; Im, Jungho; Han, Daehyeon. 2020. "Improving Local Climate Zone Classification Using Incomplete Building Data and Sentinel 2 Images Based on Convolutional Neural Networks" Remote Sens. 12, no. 21: 3552. https://doi.org/10.3390/rs12213552

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