Scholarlink

<p>The accurate deployment of 5&#xa0;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&#xa0;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&#xa0;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.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Improving 5 G base station placement through precise rooftop detection using super-resolution diffusion models and satellite image analysis

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年6月18日

查看全文
  • Iván García-Aguilar,
  • Jesús Galeano-Brajones,
  • Francisco Luna-Valero,
  • Javier Carmona-Murillo,
  • Jose David Fernández-Rodríguez,
  • Rafael Marcos Luque-Baena

摘要

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.