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A deep learning approach for average height estimation in oak colony using rgb images

2026Conference paper Open access
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datacite.subject.fosCiências Agrárias::Biotecnologia Agrária e Alimentar
datacite.subject.fosEngenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática
datacite.subject.sdg04:Educação de Qualidade
dc.contributor.authorBritto, Raphael Duarte
dc.contributor.authorMendes, João
dc.contributor.authorGrilo, Vinicius
dc.contributor.authorCastro, João Paulo
dc.contributor.authorSantos, Murillo Ferreira dos
dc.contributor.authorCastro, Marina
dc.contributor.authorPereira, Ana I.
dc.contributor.authorLima, José
dc.date.accessioned2025-12-04T15:31:13Z
dc.date.available2025-12-04T15:31:13Z
dc.date.issued2026
dc.description.abstractMany strategies have been developed to monitor the volume of volume of Above Ground Biomass (AGB) in forest areas as a fundamental step for managing carbon concentration. This study explores the use of use of Light Detection and Ranging (LiDAR) data obtained through Unmanned Aerial Vehicles (UAVs) to estimate height values in a vegetation colony composed of oaks (Quercus pyrenaica Willd.) in northern Portugal. The extraction of pertinent information from LiDAR data was facilitated by using the LAStools extension within the Quantum Geographic Information System (QGIS) software framework. The generated raster and image information were used to calculate the height values of the vegetation. Following this extraction, the information was meticulously organized into datasets, which were then employed in Deep Learning (DL) algorithms. The VGG16 model was selected as the underlying framework for the present study. Height predictions were made using dimensions of 16× 16, 32× 32, and 64 × 64 pixels for the Red, Green and Blue (RGB) images. The data was estimated and compared using both the standard format of the VGG16 model and a superficially adapted version of its convolution layers. The algorithm’s efficacy was validated by comparing the forecast results with the data obtained from QGIS, which revealed minimal discrepancies. It was observed that using 64× 64 pixel scale images yielded enhanced accuracy, resulting in reduced values for the Mean Absolute Error (MAE). The study demonstrates the viability of applying DL techniques to accurately capture information about a forest area using RGB images.eng
dc.description.sponsorshipThis study was funded by iCarbono project Fundação La Caixa (PL23-00038) and LIFE SILFORE project (LIFE21-CCA-ES-LIFE). The authors are also grateful to CeDRI (UID/05757), SusTEC (LA/P/0007/2021), CIMO (UIDP/00690/2020), CEFET-MG and the National Council for Scientific and Technological Development – CNPq, related to project 442696/2023-0.
dc.identifier.citationBritto, Raphael; Duarte Mendes, João; Grilo, Vinicius; Castro, João P.; Santos, Murillo; Ferreira dos Castro, Marina; Pereira, Ana I.; Lima, José (2026). A deep learning approach for average height estimation in oak colony using rgb images. In 5th International Conference OL2A. Cham: Springer Nature. p. 272-285. ISBN 9783032001368
dc.identifier.doi10.1007/978-3-032-00137-5_19
dc.identifier.isbn9783032001368
dc.identifier.isbn9783032001375
dc.identifier.issn1865-0929
dc.identifier.issn1865-0937
dc.identifier.urihttp://hdl.handle.net/10198/35169
dc.language.isoeng
dc.peerreviewedyes
dc.publisherSpringer Nature
dc.relationResearch Centre in Digitalization and Intelligent Robotics
dc.relationAssociate Laboratory for Sustainability and Tecnology in Mountain Regions
dc.relationMountain Research Center
dc.relation.ispartofCommunications in Computer and Information Science
dc.relation.ispartofOptimization, Learning Algorithms and Applications
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectDeep Learning
dc.subjectLiDAR
dc.subjectQGIS
dc.subjectRGB Images
dc.subjectVGG16
dc.titleA deep learning approach for average height estimation in oak colony using rgb imageseng
dc.typeconference paper
dspace.entity.typePublication
oaire.awardTitleResearch Centre in Digitalization and Intelligent Robotics
oaire.awardTitleAssociate Laboratory for Sustainability and Tecnology in Mountain Regions
oaire.awardTitleMountain Research Center
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F05757%2F2020/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/LA%2FP%2F0007%2F2020/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDP%2F00690%2F2020/PT
oaire.citation.endPage285
oaire.citation.startPage272
oaire.citation.title5th International Conference OL2A 2025
oaire.fundingStream6817 - DCRRNI ID
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oaire.fundingStream6817 - DCRRNI ID
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85
person.affiliation.nameCEDRI, Instituto Politécnico de Bragança
person.familyNameMendes
person.familyNameGrilo
person.familyNameCastro
person.familyNameCastro
person.familyNamePereira
person.familyNameLima
person.givenNameJoão
person.givenNameVinicius
person.givenNameJoão Paulo
person.givenNameMarina
person.givenNameAna I.
person.givenNameJosé
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person.identifier.ridA-8581-2014
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project.funder.identifierhttp://doi.org/10.13039/501100001871
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project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
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