Machine learning for classification of an eroding scarp surface using terrestrial photogrammetry with nir and rgb imagery

H. Bernsteiner, N. Broåová, I. Eischeid, A. Hamer, S. Haselberger, M. Huber, A. Kollert, T. M. Vandyk, F. Pirotti

Research output: Contribution to journalConference articlepeer-review

Original languageEnglish
Pages (from-to)431-437
Number of pages7
JournalISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Volume5
Issue number3
DOIs
Publication statusPublished - 3 Aug 2020
Event2020 24th ISPRS Congress on Technical Commission III - Nice, Virtual, France
Duration: 31 Aug 20202 Sept 2020

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 15 - Life on Land
    SDG 15 Life on Land

Keywords

  • High Mountain Environment
  • Machine Learning
  • Structure from Motion
  • Surface Classification
  • Terrestrial Photogrammetry

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