  Semantic Change Detection (SCD) refers to the task of simultaneously
extracting the changed areas and the semantic categories (before and after the
changes) in Remote Sensing Images (RSIs). This is more meaningful than Binary
Change Detection (BCD) since it enables detailed change analysis in the
observed areas. Previous works established triple-branch Convolutional Neural
Network (CNN) architectures as the paradigm for SCD. However, it remains
challenging to exploit semantic information with a limited amount of change
samples. In this work, we investigate to jointly consider the spatio-temporal
dependencies to improve the accuracy of SCD. First, we propose a Semantic
Change Transformer (SCanFormer) to explicitly model the 'from-to' semantic
transitions between the bi-temporal RSIs. Then, we introduce a semantic
learning scheme to leverage the spatio-temporal constraints, which are coherent
to the SCD task, to guide the learning of semantic changes. The resulting
network (SCanNet) significantly outperforms the baseline method in terms of
both detection of critical semantic changes and semantic consistency in the
obtained bi-temporal results. It achieves the SOTA accuracy on two benchmark
datasets for the SCD.
