Image-based Early Detection System for Wildfires

TL;DR

This paper introduces an image-based wildfire detection system utilizing machine learning to identify smoke early, enabling prompt alerts and potentially preventing large-scale wildfires amidst climate change challenges.

Contribution

The paper presents a novel machine learning-driven wildfire smoke detection system that operates in real-time and has been deployed in the USA for continuous monitoring.

Findings

High true detection rate of wildfire smoke

Low false detection rate

Faster detection than human observers

Abstract

Wildfires are a disastrous phenomenon which cause damage to land, loss of property, air pollution, and even loss of human life. Due to the warmer and drier conditions created by climate change, more severe and uncontrollable wildfires are expected to occur in the coming years. This could lead to a global wildfire crisis and have dire consequences on our planet. Hence, it has become imperative to use technology to help prevent the spread of wildfires. One way to prevent the spread of wildfires before they become too large is to perform early detection i.e, detecting the smoke before the actual fire starts. In this paper, we present our Wildfire Detection and Alert System which use machine learning to detect wildfire smoke with a high degree of accuracy and can send immediate alerts to users. Our technology is currently being used in the USA to monitor data coming in from hundreds of cameras daily. We show that our system has a high true detection rate and a low false detection rate. Our performance evaluation study also shows that on an average our system detects wildfire smoke faster than an actual person.