Dr. Connie Ko is an Adjunct Faculty Member, GIS / Remote Sensing Technician at the Department of Geography and Research Associate at the Department of Earth and Space and Engineering at York University. Her research focuses on deep learning with augmented LiDAR dataset for tree species classification with multi-modal LiDAR.
Previously, Dr. Ko has been part of various research projects. Currently, She is part of Intelligent Systems for Sustainable Urban Mobility (ISSUM) funded by the Ontario Research Fund, York University, University of Waterloo, and private sector partners (Esri Canada, Fugro Roadware, Miovision, Mircom, Teledyne Optech, Exascale Solutions, Trans-Plan) and 3D Mobile Mapping Artificial Intelligence (3DMMAI) project, which is supported by the NSERC (Natural Sciences and Engineering Research Council of Canada) Collaborative Research Development (CRD) program and Teledyne Optech in Toronto.
Experience
- Jan, 2015–2014: Research Associate, GeoICT/ AUSMLab, York University Canada
- July, 2019–2014: Adjunct Faculty Member at Department of Geography, York University, Canada
- Sep, 2016–Dec, 2016: Course Director(GEOG3330: Geoinformatics, GIS 1) at Department of Geography, York University, Canada
- Sep, 2015–Dec, 2015: Course Director (GEOG3440: Geoinformatics, Remote Sensing 1) at Department of Geography, York University, Canada
- Sep, 2014–Dec, 2014: Course Director (MTA001: Mathematics 1) at Faculty of Applied Science and Engineering, Seneca College, Canada
- Jan, 2003–2014: GIS & Remote Sensing Technician at Department of Geography, York University, Canada
Publications (12)
2023
- Yoo, S., Ko, C., Sohn, G., and Lee, H. (2023). YUTO Semantic: A Large Scale Aerial LiDAR Dataset for Semantic Segmentation. Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.
- Ko, C., Jeong, Y., Lee, H., and Sohn, G. (2023). YUTO Tree5000: A Large-Scale Airborne LiDAR Dataset for Single Tree Detection. Pattern Recognition, Computer Vision, and Image Processing (ICPR 2022 Workshops), 371-385.
2018
- Ko, C., Kang, J., and Sohn, G. (2018). Deep Multi-task Learning for Tree Genera Classification. ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.
2016
- Ko, C., Sohn, G., Remmel, T. K., and Miller, J. R. (2016). Maximizing the Diversity of Ensemble Random Forests for Tree Genera Classification Using High Density LiDAR Data. Remote Sensing, 8(8), 646.
2014
- Ko, C., Sohn, G., Remmel, T. K., and Miller, J. (2014). Hybrid Ensemble Classification of Tree Genera Using Airborne LiDAR Data. Remote Sensing, 6(11), 11225-11243.
2013
- Ko, C., Sohn, G., and Remmel, T. K. (2013). Tree Genera Classification with Geometric Features from High-density Airborne LiDAR. Canadian Journal of Remote Sensing, 39(sup1), S73-S85.
2012
- Ko, C., Sohn, G., and Remmel, T. K. (2012). A Comparative Study Using Geometric and Vertical Profile Features Derived from Airborne LiDAR for Classifying Tree Genera. ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.
- Ko, C., Sohn, G., and Remmel, T. K. (2012). The Impact of LiDAR Point Density on Classifying Tree Genus: Using Geometric Features and Vertical Profile Features. Proceedings of SilviLaser.
2010
- Ko, C., Sohn, G., and Remmel, T. K. (2010). Experimental Investigation of Geometric Features Extracted from Airborne LiDAR for Tree Species Classification. Proceedings of SilviLaser 2010.
- Ko, C., Remmel, T. K., and Sohn, G. (2010). A Statistical Partitioning of Vegetated Airborne Laser Scanning Data Towards Understory and Canopy Separation. Proceedings of The Prairie Summit.
2009
- Ko, C., Sohn, G., and Remmel, T. K. (2009). Classification for Deciduous and Coniferous Trees Using Airborne LiDAR and Internal Structure Reconstructions. Proceedings of SilviLaser 2009, 36-45.
- Ko, C., Sohn, G., and Remmel, T. K. (2009). A Deciduous-Coniferous Single Tree Classification and Internal Structure Derivation Using Airborne LiDAR Data. Laser Scanning 2009, IAPRS.