Semantic Segmentation of Time Series Imagery Using Deep Convolutional Neural Networks: A Case Study of Sandbars in Grand Canyon

  • Ryan E. Lima (Contributor)
  • Daniel Buscombe (Contributor)
  • Teki Sankey (Contributor)
  • Paul E. Grams (Contributor)
  • Erich R. Mueller (Contributor)



This dataset contains imagery used to train and test Deep Convolutional Neural Networks for the purpose of binary semantic segmentation of a time series of oblique imagery capturing sandbar monitoring sites in The Grand Canyon. In addition the scripts needed for removing image distortion, registering, rectifying, and labeling imagery is present.
Date made availableDec 2 2020

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