Publication List

  • Book Chapters
    • Ko, C. and T.K. Remmel. 2017. Airborne LiDAR applications in forest landscapes. pp. 147-185. In Remmel, T.K. and A.H. Perera (eds.). Mapping forest landscape patterns. New York: Springer. 326 p
    • Remmel, T.K., C. Ko, and R.L. Bello, 2010. Characterizing and comparing GIS polygon shape complexity by iterative shrinking spectra. 41–52 p. In Devillers, R. and H. Goodchild (eds.). Spatial data quality: from processes to decisions. New York: CRC Press. 222 p.
  • Journal Articles (Refereed)
    • Ko, C., Sohn, G., 2019. Machine Learning Methods for Tree Species Classification Using LiDAR Data for Urban Planning Applications. Under Review
    • Ko, C., Sohn, G., Remmel, T.K. and Miller, J., 2016. Maximizing the diversity of ensemble Random Forests for tree genera classification using high density LiDAR data. Remote Sensing 8(8):646.
    • Ko, C., Sohn, G., Remmel, T.K. and Miller, J., 2014. Hybrid ensemble classification of tree genera using airborne LiDAR data. Remote Sensing. 6:11225–11243.
    • Ko, C., Sohn, G. and T.K., Remmel, 2013. A spatial Analysis of Geometric Features Derived from High–Density Airborne LiDAR Data for Tree Species Classification. Canadian Journal of Remote Sensing, 2013, 39(s1):S73–S85, 10.5589/m13–024).
    • Ko, C., Remmel, T.K. and Sohn, G., 2012. Mapping tree genera using discrete LiDAR and geometric tree metrics, BOSQUE, 33(3):313–319.
    • Cheng, Q., C. Ko, Y. Yuan, Ge, Yung, and S. Zhang, 2006. GIS Modeling for Predicting River Runoff Volume in Ungauged Drainages in the Greater Toronto Area, Canada, Computers & Geosciences, 32(8):1108–1119.
    • Ko, C., and Q. Cheng, 2004. GIS spatial modeling of river flow and precipitation in the Oak Ridges Moraine area, Ontario, Computers & Geosciences, 30(4):379–389.
  • Publications: Peer-reviewed Conference Papers
    • Ko, C., Kang, J. and G. Sohn. 2018. Deep Multi-task learning for tree genera classification. ISPRS Technical Commission II Symposium 2018 “Towards Photogrammetry 2020”, June 3-7, Riva del Garda, Italy.
    • Ko, C., G. Sohn, T.K. Remmel, and J.R. Miller. 2014. Tree genera classification with unknown class using high density LiDAR data by ensemble classification models. IGARSS 2014/35th Canadian Symposium on Remote Sensing, July 13–18, Quebec City, Quebec, Canada.
    • Ko, C., G. Sohn, and T.K. Remmel. 2012. The impact of LiDAR point density on classifying tree genus: using geometric features and vertical profile features. SilviLaser 2012, September 16–19, Vancouver, British Columbia, Canada.
    • Ko, C. and T.K. Remmel. 2012. Forest genera mapping using discrete LiDAR and geometric tree metrics. IUFRO Landscape Ecology Conference, November 5–12, Concepción, Chile.
    • Ko, C., G. Sohn, and T.K. Remmel. 2012. A comparative study using geometric and vertical profile features derived from airborne LiDAR for classifying tree genera. The XXII Congress of the International Society of Photogrammetry and Remote Sensing, August 25 – September 1, Melbourne, Australia. ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, 1:129–134.
    • Ko. C, G. Sohn, T. Remmel, 2010. Experimental investigation of geometric features extracted from airborne LiDAR for tree species classification. Silvilaser 2010 – 10th International Conference on LiDAR Applications for Assessing Forest Ecosystems, September 14–17, – Freiburg, Germany. 
    • Ko. C, G. Sohn and T. Remmel, 2009. Classification for Deciduous and Coniferous Trees Using Airborne LiDAR and Internal Structure. Silvilaser 2009, Oct 14–16, Texas A&M, USA.
    • Ko. C, G. Sohn and T.K. Remmel, 2009. A Deciduous–Coniferous Classification and Internal Structure Derivation Using Airborne LiDAR Data. ISPRS Laserscanning 2009, September 1–4, Paris, France.
  • Publications: Conference Presentations
    • Ko, C., G. Sohn. 2019.Tree species classification with deep learning methods in urban environment.  Sustainability: Transdisciplinary Theory, Practice, and Action (STTPA) Conference, Oct 16-18, Toronto, Ontario, Canada.
    • Ko, C., Kang J.and G. Sohn. 2018. Classification of tree genera with deep multi-task learning network. Canadian Symposium on Remote Sensing, June 19–21, Saskatoon, Saskatchewan, Canada.
    • Ko, C. and G. Sohn. 2016. Ensemble classification approach for recognizing tree species from airborne multi-spectral LiDAR. Canadian Symposium on Remote Sensing, June 7–9, Winnipeg, Manitoba, Canada.
    • Ko, C., T.K. Remmel, and G. Sohn. 2014. Tree genera classification using LiDAR data: quality of training. CAGOnt, October 24–25, Toronto, Ontario, Canada.
    • Ko, C., T.K. Remmel, and G. Sohn. 2014. Tree genera classification using airborne LiDAR data by ensemble methods. Spatial Knowledge and Information Canada Conference, February 7–9, Banff, Alberta, Canada.
    • Ko, C., T.K. Remmel, G. Sohn and J.R. Miller. 2013. Ensemble classification of tree genera from airborne LiDAR point clouds. Silvilaser 2013, Oct 9 –  11, Beijing, China.
    • Ko, C., T.K. Remmel, and G. Sohn. 2012. Algorithm for processing a LiDAR point cloud to retrieve internal geometric tree crown structures. Canadian Association of Geographers Annual General Meeting, May 28 – June 2, Waterloo, Ontario, Canada.
    • Ko, C. and T.K. Remmel. 2012. Forest genera mapping using discrete LiDAR and geometric tree metrics. IUFRO Landscape Ecology Conference, November 5–12, Concepción, Chile.
    • Ko, C., G. Sohn, and T.K. Remmel. 2012. The impact of LiDAR point density on classifying tree genus: using geometric features and vertical profile features. SilviLaser 2012, September 16–19, Vancouver, British Columbia, Canada.
    • Ko, C., G. Sohn, and T.K. Remmel. 2012. A comparative study using geometric and vertical profile features derived from airborne LiDAR for classifying tree genera. The XXII Congress of the International Society of Photogrammetry and Remote Sensing, August 25 – September 1, Melbourne, Australia.
    • Ko, C., T.K. Remmel, and G. Sohn. 2012. Algorithm for processing a LiDAR point cloud to retrieve internal geometric tree crown structures. Canadian Association of Geographers Annual General Meeting, May 28 – June 2, Waterloo, Ontario, Canada.
    • Ko, C., T.K. Remmel, and G. Sohn. 2011. Extracting geometric features from airborne LiDAR for tree species classification. Canadian Association of Geographers Annual General Meeting, May 31 – June 4, Calgary, Alberta, Canada.
    • Ko. C, T.K. Remmel and G. Sohn, 2010. A statistical partitioning of vegetated airborne laser scanning data towards understory and canopy separation. Canadian Association of Geographers Annual General Meeting, June 1–5, Regina, Saskatchewan, Canada.
    • Ko. C, G. Sohn and T.K. Remmel, 2010. Derivation of 3D geometric models of single tree using high density airborne LiDAR data. AOLS (Association of Ontario Land Surveyors) 118th Annual General Meeting, Feb 17–19, Huntsville, Ontario.
    • Remmel, T.K., C. Ko, and R. Bello. 2009. Characterizing and comparing GIS polygon shape complexity by iterative shrinking spectra. 6th International Symposium on Spatial Data Quality, July 6–8, St. John’s, Newfoundland, Canada.
    • Ko, C., Remmel, T.K., and G. Sohn. 2009. A precise reconstruction of three–dimensional crown shape from airborne LiDAR point clouds. Canadian Association of Geographers Annual General Meeting, May 26–30, Ottawa, Ontario, Canada.
    • Remmel, T.K. and C. Ko. 2008. Characterizing and comparing GIS polygon shapes by iterative shrinking: a case study of forest fire residuals and arctic ponds. Canadian Association of Geographers Annual General Meeting, May 20–24, Quebec City, Quebec, Canada.
    • Ko, C. 2004. Spatial modeling of the storm runoff volume in the Oak Ridges Moraine area, Ontario using GIS and remote sensing techniques. Poster Presentation, Joint Assembly, American Geophysical Union/Canadian Geophysical Union, Montreal.
  • Works in Progress
    • Ko, C. and Sohn, G. Using deep learning techniques for tree species classification by augmenting training samples (paper in preparation) – supported by ORF-RE Intelligent System for Sustainable Urban Mobility (ISSUM) project.
    • Ko, C. and Sohn, G. Using multispectral LiDAR for tree species classification in southern Ontario, focus on trees along power distribution line- NSERC CRD (Thales).
    • Basu, R. and C. Ko., Subalterity, public education, and welfare cities: Comparing the experience of displaced migrants in three cities (Toronto, Kolkata, Havana).