UC Spatial And Image Learning (SAIL) group works on research in collaboration with Waka Kotahi NZ Transport Agency (NZTA), Christchurch City Council (CCC), Christchurch Airport, AgResearch and Stats NZ Tatauranga Aotearoa. UC SAIL group is kindly supported by the UC School of Mathematics and Statistics, UC Research and Innovation and KiwiNet. The UC SAIL group research in image processing and machine learning problems and develops prototypes for deployment. Machine learning methods that input image data have recently been applied to self-driving cars, automated airport border security gates, facial recognition, and capital asset surveying automation over the past 5-10 years, due to the rise in computational capability in hardware and cost effectiveness of capturing data, deep neural networks have been successfully applied to many image recognition problems, and in some areas surpassed human-level performance.
Currently, UC SAIL has been implementing state of the art machine learning applications for surveying road signage and road surfaces (in collaboration with NZTA), using LiDAR and panoramic cameras. SAIL has developed a unique platform technology for the detection and instance segmentation of thin objects in image data that could be applied to many industrial applications. UC SAIL has also been working with drone and satellite imagery for land cover classification, and expanding into other spatial and image learning research and development. If you have an image learning problem to be solved, or a talented person looking for a project, please contact Dr Thomas Li for more information.
