Effectiveness of training sample and features for random forest on road extraction from unmanned aerial vehicle-based point cloud

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dc.authorid0000-0001-8640-1443en_US
dc.contributor.authorBiçici, Serkan
dc.contributor.authorZeybek, Mustafa
dc.date.accessioned2021-09-08T12:17:11Z
dc.date.available2021-09-08T12:17:11Z
dc.date.issued2021
dc.departmentAÇÜ, Mühendislik Fakültesi, Harita Mühendisliği Bölümüen_US
dc.descriptionThe author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was funded by Artvin Coruh University Scientific Research Projects Coordinatorship, grant number 2019.F40.02.02.en_US
dc.description.abstractThe accuracy of random forest (RF) classification depends on several inputs. In this study, two primary inputs—training sample and features—are evaluated for road classification from an unmanned aerial vehicle-based point cloud. Training sample selection is a challenging step since the machine learning stage of the RF classification depends greatly on it. That is, an imbalanced training sample might dramatically decrease classification accuracy. Various criteria are defined to generate different types of training samples to evaluate the effectiveness of the training sample. There are several point features that can be used in RF classification under different circumstances. More features might increase the classification accuracy, however, in that case, the processing time is also increased. Point features such as RGB (red/green/blue), surface normals, curvature, omnivariance, planarity, linearity, surface variance, anisotropy, verticality, and ground/non-ground class are investigated in this study. Different training samples and sets of features are used in the RF to extract the road surface. The experiment is conducted on a local road without a raised curb located on a relatively steep hill. The accuracy assessment is conducted by comparing the model classification results with the manually extracted road surface point cloud. It is found that the accuracy increases up to around 4%–13%, and 95% overall accuracy was obtained when using convenient training samples and features.
dc.identifier.citationBiçici, S., & Zeybek, M. (2021). Effectiveness of Training Sample and Features for Random Forest on Road Extraction from Unmanned Aerial Vehicle-Based Point Cloud. Transportation Research Record, 2675(12), 401-418,03611981211029645.en_US
dc.identifier.doi10.1177/03611981211029645
dc.identifier.endpage418
dc.identifier.issue12
dc.identifier.scopusqualityQ2
dc.identifier.startpage401
dc.identifier.urihttps://hdl.handle.net/11494/3405
dc.identifier.volume2675en_US
dc.identifier.wosqualityQ3
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorZeybek, Mustafa
dc.language.isoenen_US
dc.publisherSage Publicationsen_US
dc.relation.ispartofTransportation Research Record
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.titleEffectiveness of training sample and features for random forest on road extraction from unmanned aerial vehicle-based point clouden_US
dc.typeArticle

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