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

TL;DR

This paper introduces a quantum-assisted preprocessing method that enhances an Attention U-Net model for building segmentation in Sentinel-1 SAR images of Tunis, achieving similar accuracy with fewer parameters and improved efficiency.

Contribution

The study presents a novel quantum-assisted preprocessing technique that improves model efficiency without sacrificing accuracy in urban building segmentation.

Findings

Achieved comparable accuracy to standard models.

Significantly reduced network parameters.

Enhanced computational efficiency.

Abstract

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.