A Deep Learning-Based Method for Quantifying and Mapping the Grain Size on Pebble Beaches

A Deep Learning-Based Method for Quantifying and Mapping the Grain Size on Pebble Beaches

This article proposes a new methodological approach to measure and map the size of coarse clasts on a land surface from photographs. This method is based on the use of the Mask Regional Convolutional Neural Network (R-CNN) deep learning algorithm, which allows the instance segmentation of objects af...

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Bibliographic Details
Main Authors: Antoine Soloy, Imen Turki, Matthieu Fournier, Stéphane Costa, Bastien Peuziat, Nicolas Lecoq
Format: Article
Language:English
Published: MDPI AG 2020-11-01
Series:Remote Sensing
Subjects:
remote sensing
clast size mapping
pebbles
granulometry
image segmentation
mask R-CNN
Online Access:https://www.mdpi.com/2072-4292/12/21/3659

Internet

https://www.mdpi.com/2072-4292/12/21/3659

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