Large-scale high-resolution (HR) land cover mapping is essential in monitoring the Earth’s surface and addressing critical challenges facing humanity. While weakly supervised methods help to mitigate the scarcity of HR annotations across wide geographic areas, existing approaches struggle with feature extraction instability. To address this issue, this study proposes AECA-FBMamba, an efficient weakly supervised framework that enhances model perception by stabilizing feature transitions during encoding. Specifically, this work introduces the Adaptive Environment Channel Alignment (AECA) module at the input stage, processing independently grouped color channels to enhance robust channel-wise feature extraction. Additionally, we incorporate the Feature Bridging Mamba (FBMamba) module, which enables smooth receptive field reduction, effectively addressing feature alignment issues when integrating local contexts into global representations. The proposed AECA-FBMamba achieved a 65.27% mIoU on the Chesapeake Bay dataset and a 56.96% mIoU on the Poland dataset. Experiments conducted on these two large-scale datasets demonstrate the method’s effectiveness in automatically updating high-resolution (HR) land cover maps using low-resolution (LR) historical annotations. This framework advances weakly supervised learning in remote sensing and offers solutions for large-scale land cover mapping applications.