标题： 基于量子辅助注意力U-Net的突尼斯建筑分割方法使用哨兵-1数据 显示英文标题

标题： A Quantum-assisted Attention U-Net for Building Segmentation over Tunis using Sentinel-1 Data

摘要： 在城市区域中进行建筑物分割在城市规划、灾害响应和人口地图绘制等领域至关重要。 然而，由于卫星图像的尺寸较大且分辨率较高，在密集城市区域准确分割建筑物仍然面临挑战。 本研究探讨了使用量子卷积预处理方法，以增强注意力U-Net模型在建筑物分割中的能力。 具体而言，本文专注于突尼斯的城市景观，利用哨兵-1合成孔径雷达（SAR）图像。 在此工作中，量子卷积被用来提取更具信息量的特征图，这些特征图能够捕捉雷达图像中的关键结构细节，对精确的建筑物分割有益。 初步结果表明，所提出的方法在测试准确性方面与标准的注意力U-Net模型相当，同时显著减少了网络参数。 这一结果与之前的研究成果一致，证实了量子卷积不仅保持了模型的准确性，还提高了计算效率。 这些有前景的结果突显了量子辅助深度学习框架在城市环境中大规模建筑物分割的潜力。 显示英文摘要

摘要： Building segmentation in urban areas is essential in fields such as urban planning, disaster response, and population mapping. Yet accurately segmenting buildings in dense urban regions presents challenges due to the large size and high resolution of satellite images. This study investigates the use of a Quanvolutional pre-processing to enhance the capability of the Attention U-Net model in the building segmentation. Specifically, this paper focuses on the urban landscape of Tunis, utilizing Sentinel-1 Synthetic Aperture Radar (SAR) imagery. In this work, Quanvolution was used to extract more informative feature maps that capture essential structural details in radar imagery, proving beneficial for accurate building segmentation. Preliminary results indicate that proposed methodology achieves comparable test accuracy to the standard Attention U-Net model while significantly reducing network parameters. This result aligns with findings from previous works, confirming that Quanvolution not only maintains model accuracy but also increases computational efficiency. These promising outcomes highlight the potential of quantum-assisted Deep Learning frameworks for large-scale building segmentation in urban environments.