Mapping and analyzing the local climate zones in China’s 32 major cities using Landsat imagery based on a novel convolutional neural network

Abstract

The Local Climate Zone (LCZ) scheme provides researchers with a standard method to monitor the Urban Heat Island (UHI) effect and conduct temperature studies. How to generate reliable LCZ maps has therefore become a research focus. In recent years, researchers have attempted to use Landsat imagery to delineate LCZs and generate maps worldwide based on the World Urban Database and Access Portal Tools (WUDAPT). However, the mapping results obtained by the WUDAPT method are not satisfactory. In this paper, to generate more accurate LCZ maps, we propose a novel Convolutional Neural Network (CNN) model (namely, LCZ-CNN), which is designed to cope with the issues of LCZ classification using Landsat imagery. Furthermore, in this study, we applied the LCZ-CNN model to generate LCZ mapping results for China's 32 major cities distributed in various climatic zones, achieving a significantly better accuracy than the traditional classification strategies and a satisfactory computational efficiency. The proposed LCZ-CNN model achieved satisfactory classification accuracies in all 32 cities, and the Overall Accuracies (OAs) of more than half of the cities were higher than 80%. We also designed a series of experiments to comprehensively analyze the proposed LCZ-CNN model, with regard to the transferability of the network and the effectiveness of multiseasonal information. It was found that the first convolutional stage, corresponding to low-level features, shows better transferability than the second and third convolutional stages, which extract high-level and more image-or task-oriented features. It was also confirmed that the multi-seasonal information can improve the accuracy of LCZ classification. The thermal characteristics of the different LCZ classes were also analyzed based on the mapping results for China's 32 major cities, and the experimental results confirmed the close relationship between the LCZ classes and the magnitude of the Land Surface Temperature (LST).

Multicascaded Feature Fusion-Based Deep Learning Network for Local Climate Zone Classification Based on the So2Sat LCZ42 Benchmark Dataset

“…In contrast, RS-based techniques often can provide large observation data to implement LCZ mapping [4], [5]. Numerous studies are currently being carried out to classify LCZs using remote sensing image products acquired by the Landsat [6], [7] and Sentinel [8], [9] series of satellites. Additionally, some openaccess RS platforms, including the World Urban Database and Portal (WUDAPT) [10] and Google Earth Engine (GEE) [11], have already become common tools for classifying LCZ [12].…”

“…Users can use WUDAPT to annotate training sets of online high-resolution RS images and achieve mapping by using a random forest (RF) algorithm. In addition, some studies have been carried out on large-scale LCZ mapping supported by 2 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing remote sensing imagery [13], [14], [15].…”

“…Retaining valid object information is a challenge for CNNs. According to existing studies, most CNN-based approaches drew LCZs by migrating/self-built semantic segmentation models, such as [6], [7], [20], [22]. However, these techniques still adopt the basic pattern of a continuous cascade of semantic segmentation CNNs; i.e., these techniques build networks by using multiple convolution-pooling connections.…”

Fusion of the Multisource Datasets for Flood Extent Mapping Based on Ensemble Convolutional Neural Network (CNN) Model

“…Recently, climate and human impact on the ecosystem trigger natural hazards such as drought, floods, and wildfire with higher frequency [1][2][3]. Among these types of disasters, floods are considered as the most devastating and costly disaster worldwide [4,5].…”

Risk Assessment of High Voltage Power Lines Crossing Forest Areas – A Case Study of Wildfires

“…However, the use of large-scale fire warning for power safety protection fails to address fire hazards around transmission corridors in a timely and accurate manner. Traditionally, inspections and early warming of high voltage (HV) PTC mainly rely on laborious and dangerous human work using aerial-and ground-based devices (Huang et al, 2021). Those monitoring techniques are not efficient nor effective in terms of data capturing and mining.…”