Class3Dp: A supervised classifier of vegetation species from point clouds

[EN] Recognizing the species composition of an ecosystem is essential for conservation and land management. This study presents the software Class3Dp, a supervised classifier of vegetation species for coloured point clouds. Class3Dp is run through a graphical user interface (GUI) that allows for the...

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Bibliographic Details
Authors: Carbonell-Rivera, Juan Pedro|||0000-0002-6724-6780; Estornell Cremades, Javier|||0000-0003-0854-5358; Ruiz Fernández, Luis Ángel|||0000-0003-0073-7259; Crespo-Peremarch, Pablo|||0000-0003-2241-4493; Torralba, Jesús|||0000-0001-8644-8604; Almonacid-Caballer, Jaime
Format: Article
Publication date:2024
Country:España
Institution:Universitat Politècnica de València (UPV)
Repository:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
Language:English
OAI Identifier:oai:riunet.upv.es:10251/205205
Online Access:https://riunet.upv.es/handle/10251/205205
Access Level:Open access
Keyword:Bare-earth extraction
Machine learning
Coloured point cloud
Unmanned Aerial Vehicles (UAVs)
Digital aerial Photogrammetry (DAP)
INGENIERIA CARTOGRAFICA, GEODESIA Y FOTOGRAMETRIA
13.- Tomar medidas urgentes para combatir el cambio climático y sus efectos
15.- Proteger, restaurar y promover la utilización sostenible de los ecosistemas terrestres, gestionar de manera sostenible los bosques, combatir la desertificación y detener y revertir la degradación de la tierra, y frenar la pérdida de diversidad biológica
Description
Summary:[EN] Recognizing the species composition of an ecosystem is essential for conservation and land management. This study presents the software Class3Dp, a supervised classifier of vegetation species for coloured point clouds. Class3Dp is run through a graphical user interface (GUI) that allows for the selection of training samples from RGB or MS (multispectral) clouds and their classification based on geometric, spectral and neighbourhood features, along with different machine learning methods, obtaining the point cloud classified according to the classes (species) introduced. A case study is shown where a classification of ground and vegetation is carried out, obtaining an overall accuracy (OA) of 0.94 in the RGB classification and 0.95 in the MS. Points classified as vegetation were re-classified in the species Anthyllis cytisoides L., Chamaerops humilis L., Cistus monspeliensis L., Pistacia lentiscus L. and Quercus coccifera L., obtaining an OA of 0.86 in the RGB classification and 0.87 in the MS.