Urban Change Detection Using a Dual-Task Siamese Network and Semi-Supervised Learning

In this study, a Semi-Supervised Learning (SSL) method for improved urban change detection from bi-temporal image pairs is presented. The proposed method employs a Dual-Task Siamese Difference network that not only predicts changes with the difference decoder, but also segments buildings for both images with a semantic decoder. First, the architecture was modified to produce a second change prediction derived from the semantic predictions. Second, SSL was used to improve supervised change detection. For unlabeled data, we designed a loss that encourages the network to predict consistent changes across the two change outputs. The proposed method was tested on urban change detection using the SpaceNet7 dataset. SSL achieved improved results compared to three fully supervised benchmarks. Code for the paper is available at https://github.com/SebastianHafner/SiameseSSL.git.