README.txt

Dataset Title:
UAV-Based Image Dataset for Cellular Base Station Detection

Project:
6G-GRADA: Graph Analytics for Self-Organizing 6G Cellular Systems Based on Drones

Description:
This dataset contains aerial images of cellular base stations captured using an Unmanned Aerial Vehicle (UAV). The images are intended for training and evaluating computer vision models, specifically YOLO (You Only Look Once) architectures, for automatic detection of base station infrastructure.

The dataset supports research in mobile network monitoring, infrastructure identification, and autonomous systems for 5G/6G environments.

Contents:
- Image files in JPEG (.jpg) format

Data Format:
- Images: JPEG (.jpg)

Methodology:
Images were captured using a UAV equipped with a high-resolution camera. Data acquisition was performed in controlled and authorised environments.

Intended Use:
This dataset is intended for:
- Training object detection models (YOLO and similar architectures)
- Evaluating detection performance in aerial imagery
- Research in mobile network infrastructure recognition
- Development of autonomous inspection and monitoring systems

Data Volume:
The dataset size is 300 MB.

Ethical Considerations:
The dataset does not contain identifiable individuals. Images focus exclusively on cellular infrastructure. Data collection was conducted in compliance with applicable regulations.

Limitations:
- Variability in lighting, angle, and environmental conditions may affect detection performance
- Dataset may not cover all types of base station configurations
- Performance of trained models may depend on dataset diversity

License:
This dataset is intended to be shared under an open license (e.g., Creative Commons Attribution CC BY), unless otherwise specified.

Authors:
6G-GRADA Research Team

Contact:
For further information, please contact the project team.

Date:
04-05-2026