A Quantum-assisted Attention U-Net for Building Segmentation over Tunis using Sentinel-1 Data
This work addresses building segmentation for urban planning and disaster response, but it is incremental as it builds on existing quantum-assisted methods.
The study tackled building segmentation in dense urban areas using Sentinel-1 SAR imagery of Tunis by integrating Quanvolution pre-processing with an Attention U-Net model, achieving comparable test accuracy to the standard model while significantly reducing network parameters.
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.