Procesamiento de imágenes satelitales a través de algoritmos de aprendizaje profundo, uso del suelo y cobertura terrestre para la estimación de la demanda de tráfico 5G
Thesis
Mobile communication systems, also called International Mobile Telecommunications (IMT), have become integral to our daily lives, furnishing diverse telecom services that contribute significantly to social welfare. These systems have historically centred around voice and broadband needs. However, with the advent of 5G, the objectives have expanded significantly to encompass a broader spectrum of applications, including those tailored to industrial needs and the Internet of Things (IoT). While methodologies and international recommendations exist to guide the development of these systems, they often fall short of identifying the unique needs of 5G. Traditional estimation methods use historical data on population and traffic, but they overlook new possibilities enabled by 5G, such as ultra-reliable, low-latency communication and the Internet of Things (IoT). This results in serious limitations in estimating the potential traffic demand for 5G networks. This study introduces a novel approach, utilising Remote Sensing techniques, specifically Land Use and Land Cover methods, to understand the geographical context. These techniques offer a detailed estimation of geographical characteristics by remotely measuring reflected and emitted electromagnetic radiation. Integrating deep learning for image processing further adds value, as these algorithms have proven successful in classification, segmentation, object detection, image restoration, and enhancement. The work proposes applying these techniques, using the EuroSat image database, to enhance the planning process for 5G technologies in Colombia. The objective is to include geographical characteristics in the deployment planning, infer potential use cases, and significantly improve the analyses of demand, valuation, feasibility, and other necessary aspects for 5G development.
