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Aimed at the challenge of low accuracy of building segmentation caused by poor continuity of remote-sensing-image regions and blurred boundaries, a remote sensing building semantics segmentation algorithm based on multiscale regional consistent attention supervision is proposed. First, based on the Unet encoder&#x2013;decoder architecture, the proposed algorithm constructs the region attention network (ReA-Net), which employs a multiscale receptive field-guidance model to simultaneously focus on regional features and edge details of remote sensing image objects. Second, the self-attention mechanism is employed to establish the correlation representation of regional-level features of remote sensing images, and multiscale regional attention features of remote sensing images are obtained through weighted regional-level correlation mapping. Finally, to address the lack of spatial correlation constraints on the prediction of remote sensing images segmentation, a loss function with multiscale neighborhood consistency supervision is suggested to constrain the consistency of pixel label assignment related to a local region. Experimental results on WHU building dataset showed that intersection over union (IOU) reached 91.6&#x0025;, precision reached 95.61&#x0025;, recall reached 95.68&#x0025; recall, and <italic>F</italic>1-score reached 95.64&#x0025;; On the Massachusetts building dataset, IOU reached 74.77&#x0025; and precision reached 83.93&#x0025;, recall reached 87.53&#x0025;, and <italic>F</italic>1-score reached 85.69&#x0025;. Therefore, the proposed algorithm not only has a good segmentation effect but also has a strong robustness for remote sensing building image segmentation.

ReA-Net: A Multiscale Region Attention Network With Neighborhood Consistency Supervision for Building Extraction From Remote Sensing Image