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The background features of the plots in remote sensing images are complex. The current plot segmentation methods cannot handle the fuzzy edge information well, and the segmentation accuracy is not ideal. This article uses the attention mechanism to process the land parcel features, and proposes a remote sensing land parcel segmentation network based on the global coordinate attention mechanism: GCAT-U-Net. This method embeds the global coordinate attention mechanism on the U-Net network, which strengthens the deep neural network's attention to important features in remote sensing image data. The experimental results on the public GID data set show that the model proposed in the article increases the accuracy from 0.9041 to 0.9227, which is 2% higher than the traditional U-Net network. The global coordinate attention mechanism that combines the importance of the feature itself and the feature location information is helpful for more accurate target positioning. Compared with the embedded single attention mechanism, the output of the global coordinate attention mechanism is clearer and the improvement effect is more significant.