Geoffrey Hay

Dr Geoffrey J. Hay is a Full Professor in GIScience at the University of Calgary where he directs the Geovation Group. He is also a Co-Founder and Chief Science Officer at MyHEAT.Inc. 

Dr Hay is actively engaged in developing and applying Geospatial Technologies to 'Make Energy Visible' and reduce urban GHGs, through his award-winning university HEAT research program (Heat Energy Assessment Technologies) and university Startup - MyHEAT Inc. During 2020, MyHEAT acquired and processed proprietary high-resolution (sub-meter) thermal images for 50+ cities in Canada and the USA, covering 11,000+ sq.km and a population of about 9 million people - with more projects underway.

Dr Hay has 25+ years’ experience in GIScience. He is the author of more than 250 scholarly works, including 6 co/edited international remote sensing special issues on related topics. Geoff has given keynotes at numerous international venues including NASA (GSFC), TEDx and a Special Session of the United Nations Science-Policy-Business Forum on the Environment. His research program 'The Remote Sensing of Urban Energy Efficiency' is funded by NSERC (2019-2023.)

The scientific goal of Dr Hay's research program is to develop and operationally apply innovative theroetical and methodological approaches to better understand, map, monitor, model and communicate the multiscale dynamics of urban patterns and processes, with an emphasis on the use of high-resolution (25cm-1m) thermal infrared (TIR) imagery, GEOBIA (Geographic Object-Based Image Analysis), Machine learning, GIS, and geospatial analysis to accurately define urban energy efficiency and urban features.
 

• See our Research Lab for project details:

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

1. Kucharczyk, M.; Hay, G.J.; Ghaffarian, S.; Hugenholtz, C.H. (2020). Geographic Object-Based Image Analysis: A Primer and Future Directions. Remote Sens. 12, 2012.
2. Lang, S., G. J Hay, A Baraldi, D Tiede, T Blaschke.(2019) GEOBIA achievements and spatial opportunities in the era of big Earth observation data. ISPRS International Journal of Geo-Information 8 (11), 474
3. Griffith^, D.C.; Hay, G.J. (2018). Integrating GEOBIA, Machine Learning, and Volunteered Geographic Information to Map Vegetation over Rooftops. ISPRS Int. J. Geo-Inf. 2018, 7, 462
4. 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
5. 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.
6. 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).
7. 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.
8. 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)
9. 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)
10. 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
11. 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
12. 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.
13. 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. 
14. 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.
15. 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.
16. 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. 
17. 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.
18. 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.
19. 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.
20. 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.
21. 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.
22. 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.
23. 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.
24. 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.
25. 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.
26. 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
27. 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.
28. 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.
29. Castilla, G* and G.J. Hay, 2006. Uncertainties in land use data. Hydrology and Earth System Sciences Discussions. Vol 3. pp 3439-3472.
30. Hay, G. J., 2005. Bridging Scales and Epistemologies: An Introduction. International Journal of Applied Earth Observation and Geoinformation. Vol 7. pp.249-252.
31. 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.
32. 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.
33. 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.
34. 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.
35. 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.
36. 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.
37. 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.
38. 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.
39. 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.
40. 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.
41. 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.
42. 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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