Continuous Urban Change Detection From Satellite Image Time Series With Temporal Feature Refinement and Multitask Integration

Sebastian Hafner, Heng Fang, Hossein Azizpour, Yifang Ban

Abstract: Urbanization advances at unprecedented rates, leading to negative environmental and societal impacts. Remote sensing can help mitigate these effects by supporting sustainable development strategies with accurate information on urban growth. Deep learning-based methods have achieved promising urban change detection results from optical satellite image pairs using convolutional neural networks (ConvNets), transformers, and a multitask learning setup. However, bi-temporal methods are limited for continuous urban change detection, i.e., the detection of changes in consecutive image pairs of satellite image time series (SITS), as they fail to fully exploit multitemporal data (>2 images). Existing multitemporal change detection methods, on the other hand, collapse the temporal dimension, restricting their ability to capture continuous urban changes. In addition, multitask learning methods lack integration approaches that combine change and segmentation outputs. To address these challenges, we propose a continuous urban change detection framework incorporating two key modules. The temporal feature refinement (TFR) module employs self-attention to improve ConvNet-based multitemporal building representations. The temporal dimension is preserved in the TFR module, enabling the detection of continuous changes. The multitask integration (MTI) module utilizes Markov networks to find an optimal building map time series based on segmentation and dense change outputs. The proposed framework effectively identifies urban changes based on high-resolution SITS acquired by the PlanetScope constellation ( $F1$ score 0.551), Gaofen-2 ( $F1$ score 0.440), and WorldView-2 ( $F1$ score 0.543). Moreover, our experiments on three challenging datasets demonstrate the effectiveness of the proposed framework compared to bi-temporal and multitemporal urban change detection and segmentation methods. The code is available on GitHub: https://github.com/SebastianHafner/ContUrbanCD

External IDs:dblp:journals/tgrs/HafnerFAB25