Related projects
Discover more projects across a range of sectors and discipline — from AI to cleantech to social innovation.
MyHEAT Inc. (industry partner) provides on-line tools/services to reduce urban waste energy. Currently, the company relies on sourced municipal, private, or publicly available GIS roof polygons which it combines with its proprietary high-resolution (H-Res) airborne thermal infrared (TIR) imagery to generate personalized rooftop heat-loss maps/metrics. Unfortunately, these GIS polygons are often incomplete, inaccurate and out-of-date. To mitigate these issues, this project proposes two main goals: (1) test and optimize two leading-edge Convolutional Neural Network (CNN) methods (SegNet and U-Net) for automatic and accurate rooftop delineation from MyHEAT’s existing TIR imagery, and (2) define the optimal TIR spatial resolution for CNN based rooftop delineation. The key benefits to MyHEAT include: (i) reduced data acquisition/processing costs as their optimal resolution TIR imagery will be the only data source required for heat-loss metrics, and (ii) speeding up their entire analytical pipe-line, as there will be no need to acquire, correct, or negotiate for sourced GIS roof data.
Geoffrey Hay;John Yackel;David Goldblum
Salar Ghaffarian
MyHEAT
Geography / Geology / Earth science
Professional, scientific and technical services
University of Calgary
Accelerate
Discover more projects across a range of sectors and discipline — from AI to cleantech to social innovation.
Find the perfect opportunity to put your academic skills and knowledge into practice!
Find ProjectsThe strong support from governments across Canada, international partners, universities, colleges, companies, and community organizations has enabled Mitacs to focus on the core idea that talent and partnerships power innovation — and innovation creates a better future.