Machine Learning Based Wildfire Detection Using Satellite Imagery and Drone Surveillance
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Authors
Xiong, Xingguo
Gupta, Navarun
El-Sayed, Ahmed
Issue Date
2025-04-04
Type
Other
Language
en_US
Keywords
Machine learning wildfire detection , Satellite imagery analysis , Drone surveillance monitoring
Alternative Title
Abstract
Wildfires pose significant threat to human safety, property, and ecosystems globally. In this research, we aim to develop an advanced wildfire detection system utilizing a combination of satellite imagery and drone surveillance, integrated through AI (machine learning) algorithms. By combining high-resolution NASA satellite imagery and real-time inputs from drones equipped with various sensors (flame /smoke sensor, CO, sensor, temperature/humidity sensor, GPS, etc.), the project intends to accurately identify and track potential wildfire hotspots. Both software and hardware approaches are used for wildfire detection. The achieved results demonstrate the effectiveness of the system for wildfire detection. This hybrid approach enhances the spatial and temporal resolution of detection methods, providing effective platform for emergency responders and contributing to efforts in disaster management and environmental monitoring.
Description
Department of Electrical and Computer Engineering
School of Engineering
UB Rise 2025