Using data-driven algorithms for semi-automated geomorphological mapping.

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State: Public
Version: Final published version
License: CC BY 4.0
Serval ID
serval:BIB_94468C4E4AE8
Type
Article: article from journal or magazin.
Collection
Publications
Institution
Title
Using data-driven algorithms for semi-automated geomorphological mapping.
Journal
Stochastic environmental research and risk assessment
Author(s)
Giaccone E., Oriani F., Tonini M., Lambiel C., Mariéthoz G.
ISSN
1436-3240 (Print)
ISSN-L
1436-3240
Publication state
Published
Issued date
2022
Peer-reviewed
Oui
Volume
36
Number
8
Pages
2115-2131
Language
english
Notes
Publication types: Journal Article
Publication Status: ppublish
Abstract
In this paper, we compare the performance of two data-driven algorithms to deal with an automatic classification problem in geomorphology: Direct Sampling (DS) and Random Forest (RF). The main goal is to provide a semi-automated procedure for the geomorphological mapping of alpine environments, using a manually mapped zone as training dataset and predictor variables to infer the classification of a target zone. The applicability of DS to geomorphological classification was never investigated before. Instead, RF based classification has already been applied in few studies, but only with a limited number of geomorphological classes. The outcomes of both approaches are validated by comparing the eight detected classes with a geomorphological map elaborated on the field and considered as ground truth. Both DS and RF give satisfactory results and provide similar performances in term of accuracy and Cohen's Kappa values. The map obtained with RF presents a noisier spatial distribution of classes than when using DS, because DS takes into account the spatial dependence of the different classes. Results suggest that DS and RF are both suitable techniques for the semi-automated geomorphological mapping in alpine environments at regional scale, opening the way for further improvements.
Keywords
Supervised classification, Direct sampling, Random forest, Geomorphology, Alpine environment
Pubmed
Web of science
Open Access
Yes
Funding(s)
Swiss National Science Foundation / CR23I2_162754
University of Lausanne
Create date
09/08/2021 13:37
Last modification date
14/02/2023 7:55
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