Comparison between three convolutional neural networks for local climate zone classification using Google Earth Images: A case study of the Fujian Delta in China

Abstract

Local Climate Zone (LCZ) is a significant classification system of urban form and function, which can reflect the 3-dimensional urban information specifically. However, previous studies of LCZ lack class expansion, comparison of classification accuracy of different CNNs, and results post-processing methods. Therefore, using very-high-resolution (VHR) images (2.2 m resolution) to expand LCZ classes, we compare three different biclassified convolutional neurel networks (CNNs),namely MobileNet-Segnet (MS), MobileNet-Unet (MU) and MobileNet-Pspnet (MP), and select optimal CNN to classify Fujian Delta images. Then, we combine “Vote-Filter-Overlay” methods to remove misidentified patches and smooth boundaries for biclassified LCZ maps. The study results show that: (1) The 2.2 m resolution VHR image can expand the LCZ class from 17 to 20 classes. (2) Different CNNs have diverse sensitivity to each LCZ, the more distinctive texture characteristics of LCZs, the higher their identification rate. Among the three CNNs, MP is the best model for LCZ (2,4,8,9,10, B,C,D,G) and MU is the optimal models for LCZ (1,3,5,6,11,A,F,H,I). (3) “Vote-Filter-Overlay” method can remove misidentified patches and noise and make the LCZ map more in line with actual urban form and functions. (4) Fujian Delta urban areas form a continuous urban belt along the southeastern coast, while the villages and forests distribute in the northwestern. Many small patches of vegetation and water, which can serve as potential urban ecological corridors, were found in the urban core area. Fujian Delta urban are dominated by compact LCZ (1–3), and Xiamen has the highest proportion of LCZ (1), especially in Xiamen island. The results of this study will provide a reference for LCZ classification and basic data for urban morphology.