Abstract
Urbanization have been significantly reshaping the form of urban areas and natural landscapes, leading to complex urban morphologies. In 2012, the Local Climate Zone (LCZ) classification was proposed to address this issue and has since been widely adopted in urban climate studies globally. Despite its prevalence, literature on dynamic mapping of urban morphology remains sparse, making it difficult to delve into the study of urban renewal year by year. In this study, we compared different training scales, producing dynamic mappings of urban morphology with a spatial resolution of 100 meters spanning from 2000 to 2022 in major Chinese cities, based on the LCZ scheme. The results demonstrate strong inter-year consistency, and the accuracy of urban morphology change mapping is overall higher than 70%. Additionally, our results exhibit good alignment with other LCZ mapping datasets, more suitable for the current development situation in China, and effectively discriminate between building heights and densities across different LCZ types. This dataset holds significant potential for enhancing urban morphology monitoring and advancing urban climate research.