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Spatial and Image Learning (SAIL) Group

12 February 2024

UC's Spatial And Image Learning (SAIL) group collaborates with a number of New Zealand organisations. We research image processing and machine learning problems and develop prototypes for deployment. Learn more.


UC Spatial And Image Learning (SAIL) group works on research in collaboration with Waka Kotahi NZ Transport Agency (NZTA)Christchurch City Council (CCC)Christchurch AirportAgResearch and Stats NZ Tatauranga Aotearoa. UC SAIL group is kindly supported by the UC School of Mathematics and StatisticsUC 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.

  • Prof. Mofreh Saleh, and Dr Daniel van der Walt (UC - Civil and Natural Resources)
  • Dr Varvara Vetrova (UC - Mathematics and Statistics)
  • David Humm (UC - Electric Power Engineering Centre)
  • Mark Pinner, Michael Healy, and Grace de Leon (CCC)
  • Richard Evans (Stats NZ)
  • Lee McKenzie, and Amy Strang (NZTA)
  • Dr Federico Tomasetto (AgResearch)
  • Ryan Cooney (Downer)

  • Zachary Todd (PhD Thesis)
  • Nik Bielski (PhD Thesis)
  • Xiandong Cai (Masters Thesis)
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