期刊
ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION
卷 11, 期 8, 页码 -出版社
MDPI
DOI: 10.3390/ijgi11080420
关键词
local climate zones; patch-based classification; multi-seasonal images; WUDAPT; population distribution pattern; Chinese cities
资金
- National Natural Science Foundation of China [42171304, 41701374]
- Fundamental Research Funds for the Central Universities
This study presents a patch-based classification framework using chessboard segmentation and multi-seasonal images for LCZ mapping, and constructs a high-quality sample dataset. Compared with existing methods, our framework improves the accuracy by approximately 10% and 9% for overall LCZ types and urban LCZ types, respectively. Additionally, the analysis of population distribution reveals a hierarchical effect of population density among different built-up LCZs in cities of different sizes.
Accurate urban morphology provided by Local Climate Zones (LCZ), a universal surface classification scheme, offers opportunities for studies of urban heat risk, urban ventilation, and transport planning. In recent years, researchers have attempted to generate LCZ maps worldwide with the World Urban Database and Access Portal Tools (WUDAPT). However, the accuracy of LCZ mapping is not satisfactory and cannot fulfill the quality demands of practical usage. Here, we constructed a high-quality sample dataset from Chinese cities and presented a patch-based classification framework that employs chessboard segmentation and multi-seasonal images for LCZ mapping. Compared with the latest WUDAPT method, the overall accuracy for all LCZ types (OA) and urban LCZ types (OA(u)) of our framework increased by about 10% and 9%, respectively. Furthermore, based on the analysis of population distribution, we first gave the population density of different built-up LCZs of Chinese cities and found a hierarchical effect of population density among built-up LCZs in different size cities. In summary, this study could serve as a valuable reference for producing high-quality LCZ maps and understanding population distribution patterns in built-up LCZ types.
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