Geoffrey Hay

Dr Geoffrey J. Hay is an Associate Professor in GIScience at the University of Calgary, Alberta, Canada, and Co-Founder and Chief Science Officer at MyHEAT.Inc.

Dr Hay has over 25 years experience in GIScience and is a recognized leader in GEographic-Object Based Image Analysis (GEOBIA [1]) - emphasis Energy and Environment. He has been an Alberta Ingenuity New Faculty Scholar (2005-2008) and an ISEEE Fellow (Institute for Sustainable Energy, Environment, and Economy: 2011-2013). He is the author of more than 240 scholarly works, including a co-authored book on GEOBIA (Springer, 2008) and 6 co/edited international remote sensing special issues on related topics (CJRS, 1999; JAG, 2005; PERS, 2010, Remote Sens, 2011 & 2014), including a 2018 special issue on GEOBIA in a Changing World for the International Journal of Geo-Information (IJGI). Dr Hay was the international conference Chair and host of GEOBIA 2008, a Co-Chair for the International Society for Photogrammetry and Remote Sensing (ISPRS) Working Group IV/4 - Virtual Globes and Context-Aware Visualization/Analysis (2009-2011) and a member of the Scientific Committees for GEOIBA 2010/2012/2014/2016/2018. Dr Hay is also a member of the editorial board of Sensors, gave a keynote presentation at GEOBIA 2018 in Montpellier France on GEOBIA commercial applications, and in Oct, 2018 gave an invited presentation at a Special Session of the UN Science-Policy- Business Forum on the Environment.

Dr Hay is actively engaged in developing and applying Geospatial Technologies to reduce urban GHGs and heat-loss by 'Making Energy Visible' with his award-winning university HEAT research program (Heat Energy Assessment Technologies).

In Nov 2013, Dr Hay's HEAT research team won the MIT Climate CoLab Grand Prize from over 400 crowd sourced contestants world-wide, and appeared in 25+ national/international media interviews. In April 2014, this research was commercialized as MyHEAT Inc. MyHEAT is a Calgary geomatics startup built on reasearch developed in Dr Hay's lab at UofC. MyHEAT's goal is to empower the Urban Energy Efficiency movement with a platform designed to help residents visualize, quantify and reduce the amount of invisible waste heat (a.k.a thermal energy) and associated GHGs leaving their homes, communities and cities. In Jan 2016, the MyHEAT team unveiled their first commercial product as www.myheat.ca. In Sept 2016, they completed 500,000+ detailed heat-loss images at the house, community and city level for 5 Alberta Municipalities. By the end of 2017 MyHEAT had web-enabled over 1.3 million detailed images of Canadian buildings' heat-loss (5.3M residents) in 21 cities and towns. During 2019 MyHEAT has collected and processed proprietary thermal images for 43+ cities, in 2 countries (Canada and the United States), covering 11,000+ sq.km and a population of about 9 million people - with more projects on the way in Canada, the United States and Europe.

The primary objective of Dr Hay's scientific program is to develop and apply innovative multiscale geographic object-based image-analysis (GEOBIA) approaches to enhance theory and understanding of landscape structure and dynamics at multiple scales, and provide innovative new tools and methods to better map, monitor, model and manage complex environments. Previous work has focused on using high-resolution multispectral imagery and small footprint lidar data for forestry and wetland applications. More recent areas of focus involve high-resolution thermal imaging to support Urban Energy Efficiency, and Low Carbon Communities (see MyHEAT).

• See our Research Lab for project details:

 
Refereed Journals (* - with PDFs/RAs, ^ - with Grad Students)  

1. Griffith^, D.C.; Hay, G.J. Integrating GEOBIA, Machine Learning, and Volunteered Geographic Information to Map Vegetation over Rooftops. ISPRS Int. J. Geo-Inf. 2018, 7, 462
2. Chen, G., Weng, Q., Hay, G.J., and He, Y. (2018). Geographic Object-based Image Analysis (GEOBIA): Emerging trends and future opportunities. GIScience & Remote Sensing. Vol 55, Issue 2, pp 159-182. March
3. Sims, A.W., Robinson, C.E., Smart, C.C., Voogt, J.A., Hay, G.J., Lundholm, J.T., Powers B. and O’Carroll, D.M., 2016. Retention Performance of Green Roofs in Three Different Climate Regions, Journal of Hydrology. Volume 542, November, Pages 115-124. DOI: 10.1016/j.jhydrol.2016.08.055.
4. Rahman^, M. M., Hay, G. J., Couloigner^*, I., Hemachandaran^, B., Bailin, J. 2015. A comparison of four relative radiometric normalization (RRN) techniques for mosaicking H-res multi-temporal thermal infrared (TIR) flight-lines of a complex urban scene (PHOTO-D-14-00266). The ISPRS Journal of Photogrammetry and Remote Sensing Volume 106, August 2015, Pages 82–94. (http://www.sciencedirect.com/science/article/pii/S0924271615001392).
5. Rahman^, M. M., Hay, G. J., Couloigner*, I., Hemachandaran^, B., 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.
6. Rahman^, M. M., Hay, G. J, Couloigner^* I., Hemachandaran^, 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 (http://www.mdpi.com/2072-4292/6/10/9435)
7. Abdulkarim^, B; Kamberov^, R; Hay, G. J. 2014. "Supporting Urban Energy Efficiency with Volunteered Roof Information and the Google Maps API." Remote Sens. 6, no. 10: 9691-9711. (http://www.mdpi.com/2072-4292/6/10/9691)
8. Rahman^, M. M., Hay, G. J., Couloigner*, I., Hemachandaran^, B., Bailin, J. 2014. A comparison of four relative radiometric normalization (RRN) techniques for mosaicking H-res multi-temporal thermal infrared (TIR) flight-lines of a complex urban scene (PHOTO-D-14-00266). The ISPRS Journal of Photogrammetry and Remote Sensing [Accepted with revisions on August 27, 2014].  pp. 41
9. Blaschke, T., G. J. Hay, K. Maggi, S. Lang, P. Hofmann, E. Addink; R.Q. Feitosa, F. V.D. Meer,  H.V.D. Werff, F.V.Coillie, 2014. Geographic Object-Based Image Analysis, towards a new paradigm. ISPRS Journal of Photogrammetry and Remote Sensing. Volume 87, January 2014, Pages 180-191. DOI:10.1016/j.isprsjprs.2013.09.014
10. Rahman^, M. M, G. J. Hay, I. Couloigner*, B. Hemachandran^, J. Bailin, Y. Zhang^ and A. Tam. 2012. 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.
11. Chen^, G., Hay, G.J., Carvalho*, L.M.T., and Wulder, M. 2012. Object Based Change Detection. International Journal of Remote Sensing. Vol.33, No.14, 4434-4457. 
12. Powers^, R., G. J. Hay, G. Chen^. 2012. How wetland type and area differ through scale: A case study of Alberta's Boreal Plains. Remote Sensing of Environment. volume 117, pp. 135 - 145.
13. Hay G.J., Kyle^ C., Hemachandran^ B., Chen^ G., Rahman^ M.M., Fung T.S., Arvai J.L. 2011. "Geospatial Technologies to Improve Urban Energy Efficiency." Remote Sens. 3, no. 7: 1380-1405.
14. Blaschke, T., Hay, G.J., Weng, Q., and Resch. B. 2011. Collective Sensing: Integrating Geospatial Technologies to Understand Urban Systems: An Overview Remote Sens. 3, no. 7. 1743-1776. 
15. Chen^, G. and G.J. Hay, 2011. An airborne lidar sampling strategy to model forest canopy height from Quickbird imagery and GEOBIA. Remote Sensing of Environment. 115: 1532-1542.
16. Chen^, G., Hay, G.J., and St-Onge, B. 2011. A GEOBIA framework to estimate forest parameters from lidar transects, Quickbird imagery and machine learning: a case study in Quebec, Canada. International Journal of Applied Earth Observation and Geoinformation. In Press. Corrected Proof Available online 14 June, 2011, DOI:10.1016/j.jag.2011.05.010.
17. Chen^, G., K. Zhao, G. J. McDermid and G. J. Hay (2011). The influence of sampling density on geographically weighted regression: a case study using forest canopy height and optical data. International Journal of Remote Sensing. Accepted 26 March. TRES-PAP-2010-0686. In Press.
18. Chen^, G., Hay, G.J., Castilla*, G., St-Onge, B., and Powers, R. 2011. A multiscale geographic object-based image analysis (GEOBIA) to estimate lidar-measured forest canopy height using Quickbird imagery. International Journal of Geographic Information Science, 25:877-893.
19. Chen^, G. and G.J. Hay. 2011. A support vector regression approach to estimate forest biophysical parameters at the object level using airborne lidar transects and Quickbird data. Photogrammetric Engineering and Remote Sensing, 77: 733-741.
20. Hay G.J. and Blaschke, T. 2010. Forward: Special Issue on Geographic Object-Based Image Analysis (GEOBIA), Photogrammetric Engineering and Remote Sensing. Vol. 76, No 2, February, pp. 121-122.
21. Steiniger*, S., and G.J. Hay, 2009. Free and Open Source Geographic Information Tools for Landscape Ecology: A Review. Ecological Informatics. Volume 4, Issue 4, September. pp 183-195.
22. Castilla*, G., R. Guthrie and G.J. Hay. 2009. The Landcover Change Mapper (LCM) and its applications to timber harvest monitoring in Western Canada. Special Issue on Landcover Change Detection for Photogrammetric Engineering & Remote Sensing, Vol. 75, No 8. pp 941-950.
23. Ben-Arie^, J.R, G.J. Hay., R.P. Powers^, G. Castilla*, B. St-Onge. 2009. Development of a Pit Filling Algorithm for LiDAR Canopy Height Models. Computers & Geosciences. Volume 35, Issue 9. pp 1940-1949.
24. Castilla, G*., K. Larkin^, J. Linke and G.J. Hay, 2009. The impact of thematic resolution on the patch-mosaic model of natural landscapes. Landscape Ecology Vol 24: p 15-23
25. Castilla, G*, G. J., Hay and J. R., Ruiz. 2008. Size-constrained Region Merging (SCRM): An Automated Delineation Tool for Assisted Photointerpretation. Photogrammetric Engineering & Remote Sensing. Vol.74, No.4. April. pp 409-419.
26. Wulder, M.A., J.C. White, G.J. Hay, and G. Castilla*, 2008. Towards automated segmentation of forest inventory polygons on high spatial resolution satellite imagery , The Forestry Chronicle. Vol. 84, No. 2, pp. 221- 230.
27. Castilla, G* and G.J. Hay, 2006. Uncertainties in land use data. Hydrology and Earth System Sciences Discussions. Vol 3. pp 3439-3472.
28. Hay, G. J., 2005. Bridging Scales and Epistemologies: An Introduction. International Journal of Applied Earth Observation and Geoinformation. Vol 7. pp.249-252.
29. Hay, G. J., G., Castilla*, M. A. Wulder and J. R. Ruiz. 2005. An automated object-based approach for the multiscale image segmentation of forest scenes. International Journal of Applied Earth Observation and Geoinformation. Vol 7, pp. 339-359.
30. Stewart, S. A., G. J. Hay, P. L. Rosin and T .J. Wynn. 2004. Multiscale Structure in Sedimentary Basins. Journal of Basin Research, Vol 16, 183-197.
31. Hall, O., G. J. Hay, A. Bouchard, and D. J. Marceau, 2004. Detecting dominant landscape objects through multiple scales: An integration of object-specific methods and watershed segmentation. Landscape Ecology, Vol. 19, No. 1: 59-76.
32. Hall, O., G. J. Hay, 2003. A Multiscale Object-specific Approach to Digital Change Detection. International Journal of Applied Earth Observation and Geoinformation, Vol. 4/4: 311-327.
33. Hay, G. J., T. Blaschke, D. J. Marceau, and A. Bouchard, 2003. A comparison of three image-object methods for the multiscale analysis of landscape structure. ISPRS Journal of Photogrammetry and Remote Sensing, Volume 57, Issues 5-6, April 2003, Pages 327-345. Vol 57. 327-345.
34. Hay, G. J., P. Dube, A. Bouchard, and D. J. Marceau, 2002. A Scale-Space Primer for Exploring and Quantifying Complex Landscapes. Ecological Modelling. Vol. 153, No. 1-2: 27- 49.
35. Hay, G. J., D. J. Marceau, P. Dube, and A. Bouchard, 2001. A Multiscale Framework for Landscape Analysis: Object-Specific Analysis and Upscaling. Landscape Ecology. Vol.16, No.6: 471 - 490.
36. D.J. Marceau, and G. J. Hay, 1999. Remote Sensing Contributions to the Scale Issue, Canadian Journal of Remote Sens. Vol 25, No. 4: 357-366.
37. D.J. Marceau, and G. J. Hay, 1999. Scaling and Modelling in Forestry: Applications in Remote Sensing and GIS. Canadian Journal of Remote Sens. Vol 25, No.4: 342-346.
38. Hay, G. J., K. O. Niemann, and D. G. Goodenough, 1997. Spatial Thresholds, Image-Objects and Upscaling: A Multi-Scale Evaluation. Remote Sensing of Environment, 62: 1-19.
39. Hay, G. J., K. O. Niemann, and G. McLean, 1996. An Object-Specific Image-Texture Analysis of H-Resolution Forest Imagery. Remote Sensing of Environment, 55: 108-122.
40. Hay, G. J., and K. O. Niemann, 1994. Visualizing 3-D Texture: A Three Dimensional Structural Approach to Model Forest Texture. (Cover Article) Canadian Journal of Remote Sens. Vol. 20, No.2, pp. 90-101.

Refereed Books:

1. T. Blaschke, S. Lang, G.J. Hay. 2008. (Eds). Object-Based Image Analysis. Spatial concepts for knowledge-driven remote sensing applications. Series: XVII Lecture Notes in Geoinformation and Cartography. Springer-Verlag, pp 818, p304 illustrations with CD-ROM, ISBN: 978-3-540-77057-2
   
   

Refereed Book Chapters:

1. Hemachandran^ B., Hay G.J., M.M. Rahman^, I. Couloigner*, Y. Zhang^, B. Karim^, T.S. Fung, C.D. Kyle^. 2018. Developing Multiscale HEAT Scores from H-Res Airborne Thermal Infrared Imagery to Support Urban Energy Efficiency: Challenges Moving Forward. Chapter 11. Urban Remote Sensing 2nd edition. (Editor Q. Weng). Taylor and Francis Catalogue ISBN: 978-1-138-05460-8. pp 235 – 270.
2. Hay, G. J., 2014. Visualizing Scale-Domain Manifolds: A Multiscale Geo-Object-Based Approach, in Scale Issues in Remote Sensing (ed Q. Weng), John Wiley & Sons, Inc., Hoboken, New Jersey. pp. 139-169. doi: 10.1002/9781118801628.ch0, ISBN: 9781118305041
3. G.J. Hay, T. Blaschke, S. Lang, 2008. (Eds). Preface In: Object-Based Image Analysis: Spatial concepts for knowledge-driven remote sensing applications. Eds: T. Blaschke, S. Lang, G. J. Hay. Springer-Verlag. pp. 2-5
4. Hay, G.J., and G. Castilla, 2008. Geographic Object-Based Image Analysis (GEOBIA): A new name for a new discipline?In: Object-Based Image Analysis. Spatial concepts for knowledge-driven remote sensing applications. Eds: T. Blaschke, S. Lang, G. J. Hay. Springer-Verlag. Chapter 1.4, pp. 75 - 89.
5. Castilla, G. and G.J. Hay, 2008. Image-objects and geo-objects. In: Object-Based Image Analysis. Spatial concepts for knowledge-driven remote sensing applications. Eds: T. Blaschke, S. Lang, G. J. Hay. Springer-Verlag., Chapter 1.5. pp. 91-110.
6. Wulder, M.A., White, J.C., Hay, G.J. and G. Castilla, 2008. Pixels to objects to information: Spatial context to aid in forest characterization with remote sensing. In: Object-Based Image Analysis. Spatial concepts for knowledge-driven remote sensing applications. Eds: T. Blaschke, S. Lang, G. J. Hay. Springer-Verlag., Chapter 3.5. pp. 345 - 365.
7. Hay, G. J., and Marceau, D. J., 2004. Multiscale Object-Specific Analysis (MOSA): An integrative approach for multiscale landscape analysis.In: S. M. de Jong & F. D. van der Meer (Eds). Remote Sensing Image Analysis: Including the Spatial Domain. Book series: Remote Sensing and Digital Image Processing. Volume 5. Chapter 3. Kluwer Academic Publishers, Dordrecht. ISBN: 1-4020-2559-9.

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