Determination of forest road cut slope surface material types using machine learning methods in UAV data

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dc.contributor.authorCinar, Tunahan
dc.contributor.authorTurk, Yilmaz
dc.contributor.authorAydin, Abdurrahim
dc.contributor.authorEker, Remzi
dc.date.accessioned2026-07-01T11:39:32Z
dc.date.available2026-07-01T11:39:32Z
dc.date.issued2025
dc.departmentDüzce Üniversitesi
dc.description.abstractBackground: The cost of forest roads is practically estimated by determining the Surface Material Types (SMT). Experts determine SMT by classifying soil, loose soil, and rocky surface material classes (%) through in-situ measurements, which are both costly and time-intensive. This study aims to reduce cost and time loss by evaluating the effectiveness of high-resolution remote sensing (RS) data in determining SMT. Conducted on a forest road in Konuralp region of D & uuml;zce district in T & uuml;rkiye, the study involved experts classifying the road's Soil, Loose Soil and Rocky surface material classes (%) and collecting high-resolution RS data using UAV. The RS data was processed through Random Forest (RF) and Support Vector Machine (SVM) algorithms to classify the surface material types, and their accuracy was assessed using the Kappa Coefficient, Overall Accuracy (%) and Conditional Kappa. The images were clipped at 20-meter intervals for detailed analysis. The RS data classifications were then compared with in-situ measurements using statistical analyses Index-of-Agreement (IA). Results: The RF algorithm made the best identification, although the classification of the Loose Soil class was more difficult for both algorithms compared to the other classes. Both algorithms highest accuracy in identifying the Rocky class. Conclusions: This study proposes methods to reduce time loss in cost calculations and enhance the use of RS images for estimating forest road costs.
dc.identifier.doi10.1590/01047760202531013563
dc.identifier.issn0104-7760
dc.identifier.scopus2-s2.0-105040152904
dc.identifier.scopusqualityQ3
dc.identifier.urihttps://doi.org/10.1590/01047760202531013563
dc.identifier.urihttps://hdl.handle.net/20.500.12684/23351
dc.identifier.volume31
dc.identifier.wosWOS:001665668200001
dc.identifier.wosqualityQ3
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherUniv Federal Lavras-Ufla
dc.relation.ispartofCerne
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WOS_20260623
dc.subjectRandom Forest
dc.subjectSupport Vector Machine
dc.subjectRemote Sensing
dc.subjectClassification Algorithms
dc.titleDetermination of forest road cut slope surface material types using machine learning methods in UAV data
dc.typeArticle

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