Deploying 5G networks in urban areas is crucial for meeting
the increasing demand for high-speed, low-latency wireless communications. However, the complex topography and diverse building structures in urban environments have challenges in identifying suitable locations for base stations. This research explores leveraging deep learning
neural networks to analyze satellite imagery, creating a predictive tool
for identifying potential rooftop locations. Integrating these predictions
into a user-friendly desktop application simplifies the site selection process, reducing the need for costly and labor-intensive site visits in 5G
network deployment. This approach democratizes the deployment process, making it accessible to a broader audience. The combination of
advanced technology and satellite imagery offers a promising solution to
efficiently deploy 5G base stations in urban landscapes, contributing to
the widespread adoption of this technology in densely populated areas
and advancing 5G connectivity globally.
