Activated Three Stages Building Change Detection Network

Current Building Change Detection (BCD) methods suffer from several drawbacks, including inadequate multi-scale feature interaction, blurred boundaries, and excessive computational costs. To address these challenges, ActPML is proposed which includes: 1) at the previous stage, channel-wise feature fusion via learnable cross-stage alignment, followed by a Global Ac-tivation Layer for dynamic feature filtering, 2) at the middle stage, the fea-tures derived from the previous stage are refined through a Global-Local Feature Aggregator and a Self Deep Fusion Enhancer, respectively, 3) at the late stage, the Global-Local Self Enhancer is utilized to guide the decoder in producing the change map. ActPML achieves state-of-the-art performance on the LEVIR-CD and WHU-CD datasets, with F1-scores reaching 92.14% and 94.43%. Additionally, generalization capability was further validated on the CLCD dataset. Moreover, the proposed Global Activation Layer signifi-cantly reduces computational overhead compared to KANs, requiring sub-stantially less runtime. Our code can be seen at https://anonymous.4open.science/r/ActPML-EEB6.