Grant: Alliance Grants
University of Regina
$25,000.00
Wildfire early detection and measurement using computer vision and machine learning
FY 2023-24
Summary
The escalating frequency and severity of wildfires across North America, with 60,000 wildfires recorded in 2023 alone and affecting millions of acres, underscore the critical need for early detection and effective management. This collaborative effort aims to devise and implement technologies and methodologies for the early detection, measurement, and management of wildfires utilizing imaging sensors operating within both the visible and non-visible spectrums. Given Canada's diverse geography encompassing forests and grasslands, the timely detection of wildfires is paramount to curb their uncontrolled spread.The objective is to develop a cost-efficient imaging system that can be easily deployed and maintained, whether through ground-based watch towers, or aerial vehicles, for wildfire detection and monitoring. Conventional surveillance methods involving satellite imagery and piloted flights are often expensive, hazardous, and labor-intensive. An alternative approach involves a network of interconnected self-powered wildfire lookout stations (commonly known as watchtowers) installed in remote areas, complemented by aerial imaging using drones.
Program
Alliance Grants
Recipient
Location
Saskatchewan, Canada
Recipient Type
Academia
Type
Grant
Source
NSERC's Awards Data
As published
Application id
592322-2023
Name nom
Mehrandezh, Mehran M
Department département
Engineering and Applied Science, Faculty of
Organization id
17
Institution établissement
University of Regina
Province en
Saskatchewan
Province fr
Saskatchewan
Country en
CANADA
Country fr
CANADA
Fiscal year exercice financier
2023
Competition year année de concours
2023
Award amount
25000
Program id
ALLRP
Program name en
Alliance Grants
Program name fr
Subventions Alliance
Group en
RESEARCH PARTNERSHIPS
Group fr
PARTENARIATS DE RECHERCHE
Committee code
96
Committee name en
RPP Internal Decision Cttee
Committee name fr
Cté de décision interne - PPR
Area of application code
201
Area of application group en
Natural resources (economic aspects)
Area of application group fr
Ressources naturelles (aspects économiques)
Area of application en
Forestry (silviculture, forest management)
Area of application fr
Foresterie (sylviculture, aménagement forestier)
Research subject code
1301
Research subject group en
Forest engineering
Research subject group fr
Forest management
Research subject en
Génie forestier
Research subject fr
Aménagement forestier
Application title
Wildfire early detection and measurement using computer vision and machine learning
Keyword
computer vision; data mining; image-based georeferencing; image processing; machin eleraning; sensor fusion; sensor fussion; thermal imaging; Unmanned Aerial Vehicles; wildfire detection
Application summary
The escalating frequency and severity of wildfires across North America, with 60,000 wildfires recorded in 2023 alone and affecting millions of acres, underscore the critical need for early detection and effective management. This collaborative effort aims to devise and implement technologies and methodologies for the early detection, measurement, and management of wildfires utilizing imaging sensors operating within both the visible and non-visible spectrums. Given Canada's diverse geography encompassing forests and grasslands, the timely detection of wildfires is paramount to curb their uncontrolled spread.The objective is to develop a cost-efficient imaging system that can be easily deployed and maintained, whether through ground-based watch towers, or aerial vehicles, for wildfire detection and monitoring. Conventional surveillance methods involving satellite imagery and piloted flights are often expensive, hazardous, and labor-intensive. An alternative approach involves a network of interconnected self-powered wildfire lookout stations (commonly known as watchtowers) installed in remote areas, complemented by aerial imaging using drones.
