MyHEAT — Technology

Technology Overview

Making energy visible

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MyHEAT uses machine learning to create powerful, city-wide visualizations that show areas of energy loss in buildings.


map MyHEAT Maps

MyHEAT offers aerial thermal infrared imagery across entire cities. We quickly and economically collect high-fidelity, geometrically and radiometrically correct thermal infrared (TIR) imagery of entire cities.


Our innovative and novel technology is based on 6 years of award-winning, peer-reviewed research in Urban Thermal Remote Sensing from the University of Calgary.



flight Acquisition Standards

Through a strong collaborative relationship with the sensor developer, MyHEAT exceeds industry acquisition standards, including:

  1. 2-3 days of pre-acquisition camera (bore-sight) calibrations;
  2. the use of highly accurate onboard inertial navigation and measurement systems; and,
  3. strict environmental requirements for acquisitions

Data is collected with a plane flying over a city at night. Our nighttime data collection minimizes solar effects on building temperatures. It takes three nights to collect data for a city the size of Calgary, Alberta.



wifi_tethering Sensor Advantages

For our thermal data collection, MyHEAT builds on a world-class TIR sensor that integrates key benefits of traditional wide-area format digital cameras giving industry leading data fidelity and acquisition capabilities over traditional airborne cameras.


TIR sensors do not detect temperature, rather they detect emitted long-wave thermal radiation (i.e., relative temperature); which when ‘corrected’ to kinetic temperature can be used to present heat loss data.



verified_user Processing Pipeline

Collected data is then processed to reveal individual building’s heat loss details, as well as comparable energy efficiency metrics over a town or city. MyHEAT’s proprietary post-processing pipeline includes the ability to automatically correct for local changes in temperature, microclimate, and elevation.


This means all buildings are evaluated as if they were collected at a single instance in time, allowing heat loss to be compared over different dates as well as between homes, neighbourhoods, and cities.


The result is the creation of unique HEAT Maps and HEAT Ratings for all buildings across the entire city.

Heat Loss Map
Heat Rating

keyboard_arrow_rightHEAT Maps show potential heat loss areas from a bird’s-eye view. The thermal images indicate hot spots, or heat loss, in red and cooler areas in blue.

keyboard_arrow_rightHEAT Ratings offer a relative measure of how much heat a building is losing compared to others in the neighbourhood and city.

Heat Loss Map Thermal Layers

import_contacts Publications

Rahman, M.M., Hay, G.J., Couloigner, I., Hemachandran, B., and Bailin, J. 2015. A Comparison of Four Relative Radiometric Normalization (RRN) Techniques for Mosaicking H-Res Multi-Temporal Thermal Infrared (TIR) Flightlines of a Complex Urban Scene (PHOTO-D-14-00266). The ISPRS Journal of Photogrammetry and Remote Sensing, pp. 41. Rahman, M.M., Hay, G.J., Couloigner, I., Hemachandran, B., and Bailin, J. 2014. An Assessment of Polynomial Regression Techniques for the Relative Radiometric normalization (RRN) of High Resolution Multi-Temporal Airborne Thermal Infrared (TIR) Imagery. Remote Sensing Special Issue (ISSN 2072-4292): Recent Advances in Thermal Infrared Remote Sensing Remote Sens. 2014, 6(12), 11810-11828; doi:10.3390/rs61211810. Rahman, M.M., Hay, G.J., Couloigner I., and Hemachandran, B. Transforming image-objects into multiscale fields: A GEOBIA Approach to Mitigate Urban Microclimatic Variability within H-Res Thermal Infrared Airborne Flight-Lines. Remote Sens. 2014, 6, 9435-9457. Abdulkarim, B., Kamberov, R., and Hay, G.J. 2014. Supporting Urban Energy Efficiency with Volunteered Roof Information and the Google Maps API. Remote Sens. 6, no. 10: 9691-9711. Rahman, M.M, Hay, G.J., Couloigner, I., Hemachandran, B., Bailin, J., Zhang, Y., and Tam, A. 2013. Geographic Object-Based Mosaicing (OBM) of High-Resolution Thermal Airborne Imagery (TABI-1800) to Improve the Interpretation of Urban Image-Objects. IEEE Geoscience and Remote Sensing Letters – (GEOBIA 2012 Special Issue) Vol 10, NO. 4, July. 918-922. Hay G.J., Kyle, C., Hemachandran, B., Chen, G., Rahman, M.M., Fung, T.S., and Arvai, J.L. 2011. Geospatial Technologies to Improve Urban Energy Efficiency. Remote Sens. 3, no. 7: 1380-1405.