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Article

Outdoor Dataset for Flying a UAV an Appropriate Altitude

1
Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia
2
Faculty of Computer Science and Engineering, University of Jeddah, Jeddah 23218, Saudi Arabia
*
Author to whom correspondence should be addressed.
Drones 2025, 9(6), 406; https://doi.org/10.3390/drones9060406
Submission received: 24 April 2025 / Revised: 29 May 2025 / Accepted: 29 May 2025 / Published: 31 May 2025

Abstract

The increasing popularity of drones for Internet of Things (IoT) applications has led to significant research interest in autonomous navigation within unknown and dynamic environments. Researchers are utilizing supervised learning techniques that rely on image datasets to train drones for autonomous navigation, which are typically used for rescue, surveillance, and medical aid delivery. Current datasets lack data that allow drones to navigate in a 3D environment; most of these data are dedicated to self-driving cars or navigation inside buildings. Therefore, this study presents an image dataset for training drones for 3D navigation. We developed an algorithm to capture these data from multiple worlds on the Gazebo simulator using a quadcopter. This dataset includes images of obstacles at various flight altitudes and images of the horizon to assist a drone in flying at an appropriate altitude, which allows it to avoid obstacles and prevents it from flying unnecessarily high. We used deep learning (DL) to develop a model to classify and predict the image types. Eleven experiments performed with the Gazebo simulator using a drone and a convolution neural network (CNN) proved the database’s effectiveness in avoiding different types of obstacles while maintaining an appropriate altitude and the drone’s ability to navigate in a 3D environment.
Keywords: CNN; Gazebo simulator; autonomous navigation; dataset; drone; unmanned aerial vehicle; 3D; ROS CNN; Gazebo simulator; autonomous navigation; dataset; drone; unmanned aerial vehicle; 3D; ROS

Share and Cite

MDPI and ACS Style

Alotaibi, T.; Jambi, K.; Khemakhem, M.; Eassa, F.; Bourennani, F. Outdoor Dataset for Flying a UAV an Appropriate Altitude. Drones 2025, 9, 406. https://doi.org/10.3390/drones9060406

AMA Style

Alotaibi T, Jambi K, Khemakhem M, Eassa F, Bourennani F. Outdoor Dataset for Flying a UAV an Appropriate Altitude. Drones. 2025; 9(6):406. https://doi.org/10.3390/drones9060406

Chicago/Turabian Style

Alotaibi, Theyab, Kamal Jambi, Maher Khemakhem, Fathy Eassa, and Farid Bourennani. 2025. "Outdoor Dataset for Flying a UAV an Appropriate Altitude" Drones 9, no. 6: 406. https://doi.org/10.3390/drones9060406

APA Style

Alotaibi, T., Jambi, K., Khemakhem, M., Eassa, F., & Bourennani, F. (2025). Outdoor Dataset for Flying a UAV an Appropriate Altitude. Drones, 9(6), 406. https://doi.org/10.3390/drones9060406

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