The precise building extraction from high-resolution remote sensing images holds significant application for urban planning, resource management, and environmental conservation. In recent years, deep neural networks (DNNs) have garnered substantial attention for their adeptness in learning and extracting features, becoming integral to building extraction methodologies and yielding noteworthy performance outcomes. Nonetheless, prevailing DNN-based models for building extraction often overlook spatial information during the feature extraction phase. Additionally, many existing models employ a simplistic and direct approach in the feature fusion stage, potentially leading to spurious target detection and the amplification of internal noise. To address these concerns, we present a multi-scale attention network (MSANet) tailored for building extraction from high-resolution remote sensing images. In our approach, we initially extracted multi-scale building feature information, leveraging the multi-scale channel attention mechanism and multi-scale spatial attention mechanism. Subsequently, we employed adaptive hierarchical weighting processes on the extracted building features. Concurrently, we introduced a gating mechanism to facilitate the effective fusion of multi-scale features. The efficacy of the proposed MSANet was evaluated using the WHU aerial image dataset and the WHU satellite image dataset. The experimental results demonstrate compelling performance metrics, with the F1 scores registering at 93.76% and 77.64% on the WHU aerial imagery dataset and WHU satellite dataset II, respectively. Furthermore, the intersection over union (IoU) values stood at 88.25% and 63.46%, surpassing benchmarks set by DeepLabV3 and GSMC.

The information in remote sensing images often leads to incomplete building contours and suboptimal adaptability to complex building scenes. To address these issues, we propose a novel multi-scale network with dual attention mechanisms to extract clear building boundaries. The Squeeze-and-Excitation (SE) module is employed to bolster feature extraction, and the Atrous Spatial Pyramid Pooling (ASPP) module is integrated to capture multi-scale feature information. Then, in the decoding phase, channel grouping shuffle and dual attention mechanisms are synergistically integrated to exploit the interrelations and global dependencies of building features. Finally, a hybrid loss function is devised to address the class imbalance and thereby ensure more stable network training. Experimental evaluations on two high-resolution remote sensing datasets, Zimbabwe and Massachusetts, demonstrate that the proposed method markedly surpasses the performance of semantic segmentation networks such as PSPnet, U-net, and DAnet in terms of accuracy, recall, F1 score, and Mean Intersection over Union (MIoU), achieving an F1 score of up to 83.23% and an MIoU of 73.56%. This multi-scale attention network holds substantial promise for practical applications in building extraction.