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MyHEAT — Technology

Technology Overview

A summary of how our technology works.

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

wifi_tethering Sensor Advantages

MyHEAT technology quickly, and economically, collects large area, high-fidelity, geometrically and radiometrically correct thermal infrared (TIR) imagery. These data are then processed with peer-reviewed algorithms to reveal individual buildings’ heat loss details, as well as comparable energy efficiency metrics over a town or city. For thermal data collection, MyHEAT builds on a world-class Canadian TIR sensor. It integrates key benefits of traditional push-broom and wide-area format digital cameras within a (patented) cooled push-frame platform to enjoy industry leading data fidelity and acquisition capabilities over traditional airborne cameras. The sensor has several advantages, including: (i) 1800 pixel wide swaths, (ii) sampling of heat-loss from every house in the scene 10-20 times vs. 1-3 times for wide-format cameras, (iii) a cryogenically cooled sensor with a thermal resolution of 0.05°C vs 0.1°C for uncooled bolometers, (iv) TIR data collection in the 3-5μm range, resulting in better urban haze and atmospheric penetration than the 8-11μm range of traditional cameras, and (v) massive mosaic coverage of up to 120km2 per hour, at a 50cm spatial resolution (160 knots). Thus, under ideal conditions, large municipal areas like Calgary (825km2) can be imaged with great detail, in two evenings.

flight Acquisition Standards

Through a strong collaborative relationship with the sensor developer, MyHEAT exceeds industry acquisition standards, including: (i) 2-3 days of pre-acquisition camera (bore-sight) calibrations, (ii) the use of highly accurate on-board inertial navigation and measurement systems (INS/IMU), (iii) night-time heat loss surveys (between 11pm and 4 am to minimize solar effects on building temperatures), (iv) spring and/or fall acquisitions (as low environmental temperatures provide higher-contrast with urban heat-loss), (v) strict environmental requirements for acquisitions under cool, dry, low-wind speed conditions, (vi) a 30% overlap between municipal flight lines as well as a collection across all flight-lines to fully evaluate temperature changes over the acquisition, (vii) in-situ temperature data for cross-calibration, as well as (viii) the collection of temperature data (24 hours before, during and after the acquisition) from a network of web-enabled weather stations.

verified_user Processing Advantages

The push-frame nature of this sensor results in the mosaicking of a relatively small number of adjacent flight lines, rather than 1000’s of individual digital photographs. Furthermore, MyHEAT’s proprietary post-processing pipeline includes an industry first—the ability to automatically correct for local changes in temperature, microclimate and elevation (every 5m over the whole scene). Thus, all houses in the scene are evaluated as if they were collected at a single instance in time rather than in multiple flight lines. This allows for urban heat-loss to be compared over different dates as well as between houses, communities and cities. By automatically applying (patent pending) object-based fitting algorithms and proprietary image processing procedures to the TIR data (and related color air photography and GIS building polygons), we define roof materials, the amount of vegetation over roofs, and generate emissivity corrected true kinetic temperatures. 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: (i) locate hotspots (i.e., the hottest location on roof tops), (ii) create detailed Heat Loss Maps and (iii) generate comparative Heat Loss Ratings at the house, community and city level. Heat Loss Ratings provide a comparative value of heat loss from each home, ranking them from low to high heat loss (i.e. 1-10). Specifically, they are an integrated value based on proprietary thermal attributes as seen by the sensor including but not limited to the temperature range, average and total heat leaving each home (normalized for local microclimatic variability over time). When correlated with energy consumption for over 300K homes, we are able to create a score that also has a 64% correlation with energy consumption over the entire year.

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.