Work

AI-Driven Wildfire Detection with Integrated Air Quality and Machine Learning Vision Systems Powered by an Accelerator for Early Action

Year: 2025

Type: conference-paper

Abstract: Wildfires are causing tremendous threats to human lives, infrastructure, the environment, and human health, entail innovative approaches for prompt detection and control.This research presents a state-of-the-art wildfire detection system that integrates an AI accelerator with air quality data and machine learning systems to identify potential fire danger.Utilizing realtime ecosystem data, including humidity, PM, VOC, RH/T, and NO, the system predicts the likelihood of wildfire ignitions.Upon a potential wildfire identification, the AI accelerator activates vision-based systems.Combining the power of AI accelerator and Google Colab, this approach achieves an efficient, and scalable solution for wildfire detection.By integrating air quality monitoring and AI-driven vision systems, this research provides a scalable, real-time wildfire detection solution.

DOI: 10.18260/1-2-1115-55000

Authors: Mina Gaber Wahba Ibrahim, Xingguo Xiong, Navarun Gupta, Ahmed El-Sayed El-Sayed

Institution: University of Bridgeport

Language: English

Topic: Fire Detection and Safety Systems

Subfield: Safety, Risk, Reliability and Quality

Field: Engineering

Domain: Physical Sciences

SDGs: Climate action, Industry, innovation and infrastructure

Open Access status: gold

Funders: National Aeronautics and Space Administration, Connecticut Space Grant College Consortium, Nuclear Safety and Security Commission

Awards: G540792204, G6057698366