Rate My Professor Kourosh Khoshelham

Professor Khoshelham's main research areas are 3D modelling and computer vision for civil infrastructure and the built environment, with primary interests in photogrammetry and computer vision. His specializations include mapping and 3D modelling of natural and built environments using photogrammetry and LiDAR technologies, positioning and navigation in indoor environments, augmented reality, and deep learning methods for automated interpretation of imagery and point clouds. Expertise covers spatial analysis, digital mapping, remote sensing, geospatial science, geoinformation, satellite image analysis, and feature extraction. He has co-authored over 140 refereed publications, with 303 listed on ResearchGate and more than 9,700 citations on Google Scholar. Key publications include "Accuracy and resolution of Kinect depth data for indoor mapping applications" (2012, 2,398 citations), "Accuracy analysis of Kinect depth data" (2011, 754 citations), "Real-time monitoring of construction sites: Sensors, methods, and applications" (2022, 473 citations), "Indoor Localization Improved by Spatial Context-A Survey" (2019, 200 citations), "Effect of slope on treetop detection using a LiDAR Canopy Height Model" (2015, 161 citations), "Performance evaluation of automated approaches to building detection in multi-source aerial data" (2010, 160 citations), and "Locomotion activity recognition using stacked denoising autoencoders" (2018, 147 citations). He contributes to the International Society for Photogrammetry and Remote Sensing (ISPRS) as Co-chair of Working Group IV/5 and leader of the ISPRS Benchmark on Indoor Modelling. His research impacts areas such as LiDAR applications for vehicle localization and deformation monitoring, mixed reality visualization, BIM alignment, and environmental monitoring.