Use-Net: Satellite Data-Based Framework for Optimizing Billboard Placement in Urban Areas

In this study, we address the key objective of identifying the optimal region within an extensive geographical area for strategic billboard placement, ensuring effective communication with diverse audiences. The challenge lies in finding suitable spaces for billboard placement within a confined geographical area. One potential solution involves extracting urban areas from satellite images to enhance the efficiency of identifying appropriate billboard spaces. To address this, we propose a novel framework named USE-NET, leveraging satellite data to comprehensively cover large geographic regions for the precise identification of optimal billboard locations. Our proposed framework integrates an enhanced UNet model with the Squeeze-and-Excitation (SE) attention method to optimize the identification process based on satellite data. Comparative evaluations against alternative deep learning models, utilizing various metrics, underscore the efficacy of our approach. Experimental results demonstrate a testing accuracy of 87.21%, showcasing a notable improvement of at least 4% compared to Link-Net, MaNet, and UNet models. To enhance the reproducibility of this research, the code of this paper is made available at: https://github.com/sukritidhang/USE-Net_optimalbillboardlocation