Since it’s infancy, nearly 20 years ago, eCognition has included functionality to work with image data that represent a set of time frames. The t- dimension sits alongside the x-, y- and z- dimensions, representing the full 4D eCognition image data model. The t- dimension can be a powerful tool for classic change detection/analysis work, such as with multi-temporal satellite and aerial image data, but also for use with image data from time-lapse sensor and video systems that are increasingly used in field studies and experimental setups.

The aim of this webinar is to explore how to work in eCognition with the t- dimension, in terms of both entering image data as a frame series and using frame related features in rule sets, including the powerful Link Class (algorithm Create Links) functionality.

The use case presented in the webinar by Dr. Geoff Groom, a senior researcher at the Institute of Bioscience of Aarhus University in Denmark, looks at a set of multi-temporal tripod-mounted camera photos of a vegetated (Dryas octopetala) field plot in Greenland, collected as part of climate change ecology effect research.

Topics covered include:

  • Setting up t- dimension projects
  • Working with the t- dimension in rule sets
  • t- dimension-based change detection strategies


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