RT Journal Article
T1 Improving 5G base station placement through precise rooftop detection using super-resolution diffusion models and satellite image analysis.
A1 García Aguilar, Iván
A1 Galeano-Brajones, Jesús
A1 Luna-Valero, Francisco
A1 Carmona Murillo, Javier
A1 Fernández-Rodríguez, Jose David
A1 Luque-Baena, Rafael Marcos
K1 Markov, Procesos de
K1 Aprendizaje automático (Inteligencia artificial)
K1 Redes neuronales (Informática)
AB The accurate deployment of 5 G base stations (BSs) in urban environments is essential for achieving optimal network performance. In these scenarios, the most common positions for installing BSs are rooftops, which, however, given the complex topography and diverse building structures, present significant challenges when identifying suitable locations. This paper proposes an enhanced method for rooftop detection, integrating diffusion models based on super-resolution with segmentation using convolutional neural networks. Starting from the input image, a super-resolution model is applied to generate sliding windows on which re-inference is performed, thereby improving both the resolution and prediction accuracy for this type of object. By refining these detections, the placement of 5 G base stations is undertaken in a practical, industrial way, thus allowing network operators to perform a more real-world network optimization. The results demonstrate a significant improvement in detection accuracy, directly contributing to more efficient 5 G base station deployment in densely populated urban areas. This methodology offers a scalable, adaptable, and effective solution based on the context of the images it applies to.
PB Springer
YR 2025
FD 2025
LK https://hdl.handle.net/10630/39074
UL https://hdl.handle.net/10630/39074
LA eng
NO García-Aguilar, I., Galeano-Brajones, J., Luna-Valero, F. et al. Improving 5 G base station placement through precise rooftop detection using super-resolution diffusion models and satellite image analysis. Nat Comput (2025). https://doi.org/10.1007/s11047-025-10030-z
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 4 mar 2026