Beyond Attention: Convolutional Global Context for Remote Sensing Change Detection

Remote sensing change detection is caught in a long-standingtrade-off: reliable suppression of pseudo changes requires global contex-tual reasoning, while attention-based models incur prohibitive quadraticcomplexity, hindering deployment on resource-constrained UAV and satel-lite platforms. This paper proposes ChangeGCC, a fully convolutional,linear-time framework that delivers efficient global context for change de-tection. ChangeGCC core features two key components: Globally Condi-tioned Convolution that enable convolutional global routing, and a dual-temporal fusion scheme that enhances semantic consistency across scaleswhile highlighting genuine structural changes. Extensive experiments onmultiple benchmarks show that ChangeGCC achieves state-of-the-artperformance, reaching 85.28% IoU and 92.06% F1 on the LEVIR-CD benchmark, while substantially reducing parameters, FLOPs, andinference latency compared to attention-based counterparts.