COUD: Continual Urbanization Detector for Time Series Building Change Detection

Abstract

Building change detection on remote sensing images is an important approach to monitoring the urban expansion and sustainable development of natural resources. In conventional building change detection tasks, only changed regions between two time phases are typically concerned. The relevance and trend of spatiotemporal changes between multiple time phases are neglected in most cases. In this article, we propose a two-stage continual urbanization detector (COUD) for time series urban building change detection task. The COUD method employs self-supervised pretraining for feature refinement, and performs optimization through temporal distillation approach. Consequently, multitemporal feature extraction and changing regions localization of urban building complexes are conducted. Considering the gap in available dataset for time series change detection task, we produce and release a time series dataset named “TSCD”. Chengdu region of China is selected as the study area in this research, which is partially covered by the proposed TSCD dataset. By applying the proposed COUD method to the selected study area for exploring the changing pattern from 2016 to 2022, a comprehensive analysis is conducted in conjunction with actual planning policies published by the management department. Extensive experimental results confirm the reliability of our proposed method.