 Global Attention Gate GAG is a novel method for semantic segmentation of remote sensing images. It uses a Hadamard product to connect the uppermost layer of the decoder part with each Global Attention Gate, allowing it to make full use of both contextual and multi-scale features. This allows the model to focus on specific patterns and achieve higher accuracy than existing methods. This article was authored by Zhong Chen, Jun Zhao, and He Deng.