Research Vision

My research focuses mainly on augmented urban space that integrates urban ICT (Information and Communication Technologies) with virtual 3D city models. This research goal motivates me to study on three-dimenional modeling, sensor networking, HCI (Human Computer Interaction), redenring and exploring of large-scale cityscape, landscape and seabedscape using active and passive remote sensing data for developing new engineering, and scientific applications.

Opportunities

I am currently looking for self-motivated under-graduate (through RAY program, ENG4000 and ESS4000) and/or graduate students (master level) who will participate in research projects in: 1) power-line anomaly detection using thermal imagery or 2) radiometric and geometric calibration of LiDAR FWF (full-waveform data) or 3) real-time spatialization of spatio-temporal objects using uncalibrated video sequence. Please send an email to gsohn@yorku.ca with your CV if you are intereted in the suggested research fields.

Latest Activities

Public Talk@ Center of City Ecology

Power Line Project Technology Demonstration

*you will be asked to install Google Earth plug-ins.

Recently Published Journal Articles

M.N.K. Boulos, B. Resch, D.N. Crowley, J.G. Breslin, G. Sohn, R. Burtner, W.A. Pike, E. Jezierski and K.S. Chuang, 2011. Crowdsourcing, citizen sensing and sensor web technologies for public and environmental health surveillance and crisis managment: trends, OGC standards and application examples. International Journal of Health Geographics, 10:67; doi:10.1186/1476-072X-10-67.

Recently Submitted Journal Articles

Y. Jwa and G. Sohn, 2012. A Piece-wise Catenary Curve Model Growing for 3D Power Line Reconstruction. Photogrammetric Engineering & Remote Sensing. In print.

Armenakis, C., Y. Gao and G. Sohn, 2011. Co-registration of aerial photogrammetric and lidar point clouds in urban environments using semi-automatic plane correspondence. Applied Geomatics. (Submitted).

Ko, C., G. Sohn and T.K., Remmel, 2011. A Spatial Analysis of Geometric Features Derived from High-Density Airborne LiDAR Data for Tree Species Classification. Photogrammetric Engineering & Remote Sensing (Submitted).

Kim, H.B. and G. Sohn, 2011. Power-line Scene Classification from LiDAR Data Using Ramdom Forests.Journal of International Society of Photogrammetry and Remote Sensing.(Submitted).

Preparing Journal Articles

Sohn, G., Y. Jwa and J. Jung, 2011. An Implicit Generalization of Building Rooftop Models Using Minimum Description Length. (Preparing).

Zhang, J. and G. Sohn, 2011. A Markov Random Field Model for Single Tree Detection from Airborne Laserscanning Data. (Preparing).