Version 1
: Received: 21 April 2024 / Approved: 22 April 2024 / Online: 22 April 2024 (10:28:57 CEST)
How to cite:
Wernette, P. Machine Learning Vegetation Filtering of Coastal Cliff and Bluff Point Clouds. Preprints2024, 2024041387. https://doi.org/10.20944/preprints202404.1387.v1
Wernette, P. Machine Learning Vegetation Filtering of Coastal Cliff and Bluff Point Clouds. Preprints 2024, 2024041387. https://doi.org/10.20944/preprints202404.1387.v1
Wernette, P. Machine Learning Vegetation Filtering of Coastal Cliff and Bluff Point Clouds. Preprints2024, 2024041387. https://doi.org/10.20944/preprints202404.1387.v1
APA Style
Wernette, P. (2024). Machine Learning Vegetation Filtering of Coastal Cliff and Bluff Point Clouds. Preprints. https://doi.org/10.20944/preprints202404.1387.v1
Chicago/Turabian Style
Wernette, P. 2024 "Machine Learning Vegetation Filtering of Coastal Cliff and Bluff Point Clouds" Preprints. https://doi.org/10.20944/preprints202404.1387.v1
Abstract
Coastal cliffs erode in response to short- and long-term environmental changes but predicting these changes continues to be a challenge. In addition to a chronic lack of data on the cliff face, vegetation presence and growth can bias our erosion measurements and limit our ability to detect geomorphic erosion by obscuring the cliff face. This paper builds on past research segmenting vegetation in 3-band red, green, blue (RGB) imagery and presents two approaches to segmenting and filtering vegetation from the bare cliff face in dense point clouds constructed from RGB images and structure-from-motion (SfM) software. Vegetation indices were computed from previously published research and their utility in segmenting vegetation from bare cliff face were compared against machine learning (ML) models for point cloud segmentation. Results demonstrate that, while existing vegetation indices and ML models are both capable of segmenting vegetation and bare cliff face sediments, ML models can be more efficient and robust across different growing seasons. ML model accuracy quickly reached an asymptote with only two layers and RGB images only (i.e., no vegetation indices), suggesting that these more parsimonious models may be more robust to a range of environmental conditions than existing vegetation indices which vary substantially from one growing season to another with changes in vegetation phenology.
Keywords
coastal geomorphology; vegetation; SfM; structure from motion; machine learning; MLP
Subject
Environmental and Earth Sciences, Remote Sensing
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
