| Summary: | With rapid urbanization in recent decades, more and more urban renewal has taken place in China. Meanwhile, the early developed areas without change have become old towns, which need special attention in future city planning. However, other than field surveys, there is no specific method to identify old towns. To fill this gap, we used time-series image stacks established from Landsat Surface Reflectance Tier 1 data on the Google Earth Engine (GEE) platform, facilitated by Global Urban Boundary (GUB), Essential Urban Land Use Categories (EULUC) and Global Artificial Impervious Area (GAIA) data. The LandTrendr change detection algorithm was applied to extract detailed information from 14 band/index trajectories. These features were then used as inputs to two methods of old town identification: statistical thresholding and random forest classification. We assessed these two methods in a rapidly developing large city, Hangzhou, and subsequently obtained overall accuracies of 81.33% and 90.67%, respectively. Red band, NIR band and related indices show higher importance in random forest classification, and the magnitude feature plays an outstanding role. The final map of Hangzhou during the 2000–2018 period shows that the old towns were concentrated in the downtown region near West Lake within the urban boundaries in 2000, and far fewer than the renewed areas. The results could serve as references in the provincial and national planning of future urban developments.
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