Early Warning Forest Fire Detection System
THE QUESTION
Can an AI-enabled fire detection system prevent wildfires through machine-learning and quickly notify authorities when a fire is detected?
LOCATION: Pakistan
SECTOR: Climate and Environment
TECH: AI
TIMELINE: December 2021 - March 2023
PIONEERS: Javeria Afzal & Nadeem Ahmad
PARTNERS: WWF Pakistan & Lahore University of Management Sciences (LUMS)
The Challenge
According the the UN FAO, 2.2% or approximately 1,687,000 hectares of Pakistan is forested. Forest fires have become more frequent in Pakistan due to drier and warmer conditions caused by climate change, with 17,879 fire incidents between 2001-2019. Despite the frequent forest fires and the damages caused to livelihoods of forest dependent communities, the infrastructure for fire protection is weak. Traditional forest fire control methods in Pakistan rely on forest lines to prevent the spread of fire, but this does not alert fire and forest departments of the fire. In most cases, local communities surrounding the forest often try to extinguish the fire themselves, with the spread of fire lasting for days at a time. These fires threaten not only the health of the ecosystem, but also the health of local communities.
The Idea
This pilot will use thermal sensors and machine learning approaches to develop an early warning system to prevent and manage wildfires. The developed automated system will detect forest fires before they begin to spread by alerting local authorities quickly, but will also identify hotspots that are vulnerable to catching fire through combining data from thermal sensors, imaging, weather forecasts and meteorological readings. If successful, this pilot will prevent and quickly manage wildfires to save forests, communities, habitats and livelihoods.
Read more
Learn about the pilot’s first sprint — “To Sprint or not to Sprint?: The first leg of our Forest Fire project”
Read about how the pilot has worked to engage the local community — “Sprinting towards Inclusive Conservation: Bringing the Community Onboard”
Learn about early technology developments the pilot has made — “Exploring Technology-Driven Approaches for Early Detection and Response to Forest Fires: The LUMS Perspective”
Explore the challenges, insights, and future roadmap of the pilot — “Embracing IoT and AI for Forest Protection: Lessons Learned and the Path Forward for the Forest Fire Detection Early Warning System”
Read about the pilot’s final sprint — “A Bumpy Sprint to the Finish Line”