  In this study, a Semi-Supervised Learning (SSL) method for improving urban
change detection from bi-temporal image pairs was presented. The proposed
method adapted a Dual-Task Siamese Difference network that not only predicts
changes with the difference decoder, but also segments buildings for both
images with a semantics decoder. First, the architecture was modified to
produce a second change prediction derived from the semantics predictions.
Second, SSL was adopted to improve supervised change detection. For unlabeled
data, we introduced 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.
