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Automatic Rural Road Centerline Detection and Extraction from Aerial Images for a Forest Fire Decision Support System

2023-01-02Artigo científico Acesso aberto
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dc.contributor.authorLourenço, Miguel
dc.contributor.authorEstima, Diogo
dc.contributor.authorOliveira, Henrique
dc.contributor.authorOliveira, Luís
dc.contributor.authorMora, André
dc.contributor.institutionDEE - Departamento de Engenharia Electrotécnica e de Computadores
dc.contributor.institutionCTS - Centro de Tecnologia e Sistemas
dc.contributor.institutionUNINOVA-Instituto de Desenvolvimento de Novas Tecnologias
dc.contributor.pblMolecular Diversity Preservation International (MDPI)
dc.date.accessioned2023-06-06T22:22:02Z
dc.date.available2023-06-06T22:22:02Z
dc.date.issued2023-01-02
dc.descriptionPublisher Copyright: © 2023 by the authors.
dc.description.abstractTo effectively manage the terrestrial firefighting fleet in a forest fire scenario, namely, to optimize its displacement in the field, it is crucial to have a well-structured and accurate mapping of rural roads. The landscape’s complexity, mainly due to severe shadows cast by the wild vegetation and trees, makes it challenging to extract rural roads based on processing aerial or satellite images, leading to heterogeneous results. This article proposes a method to improve the automatic detection of rural roads and the extraction of their centerlines from aerial images. This method has two main stages: (i) the use of a deep learning model (DeepLabV3+) for predicting rural road segments; (ii) an optimization strategy to improve the connections between predicted rural road segments, followed by a morphological approach to extract the rural road centerlines using thinning algorithms, such as those proposed by Zhang–Suen and Guo–Hall. After completing these two stages, the proposed method automatically detected and extracted rural road centerlines from complex rural environments. This is useful for developing real-time mapping applications.en
dc.description.versionpublishersversion
dc.description.versionpublished
dc.format.extent20
dc.format.extent6682414
dc.identifier.doi10.3390/rs15010271
dc.identifier.issn2072-4292
dc.identifier.otherPURE: 62892607
dc.identifier.otherPURE UUID: 8de57425-3144-4321-9410-b5faa50f3092
dc.identifier.otherScopus: 85145876230
dc.identifier.otherWOS: 000909124400001
dc.identifier.urihttp://hdl.handle.net/10362/153669
dc.identifier.urlhttps://www.scopus.com/pages/publications/85145876230
dc.language.isoeng
dc.peerreviewedyes
dc.relationinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F00066%2F2020/PT
dc.relationCentre of Technology and Systems
dc.relationinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F00066%2F2020/PT
dc.subjectcenterline extraction
dc.subjectconvolutional neural network (CNN)
dc.subjectdecision support system (DSS)
dc.subjectdeep learning
dc.subjectforest fires
dc.subjectgeographic information system (GIS)
dc.subjectrural roads
dc.subjectspatial pyramid pooling (SPP)
dc.subjectwireless sensor networks (WSN)
dc.subjectGeneral Earth and Planetary Sciences
dc.subjectSDG 15 - Life on Land
dc.titleAutomatic Rural Road Centerline Detection and Extraction from Aerial Images for a Forest Fire Decision Support Systemen
dc.typejournal article
degois.publication.issue1
degois.publication.titleRemote Sensing
degois.publication.volume15
dspace.entity.typePublication
oaire.awardNumberUIDB/00066/2020
oaire.awardNumberPCIF/SSI/0102/2017
oaire.awardTitleCentre of Technology and Systems
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F00066%2F2020/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/3599-PPCDT/PCIF%2FSSI%2F0102%2F2017/PT
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStream3599-PPCDT
project.funder.identifierhttp://doi.org/10.13039/501100001871
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
rcaap.rightsopenAccess
relation.isProjectOfPublication779ae807-14a4-4e58-87e2-6dd81eb7a030
relation.isProjectOfPublication7f731199-68cf-46d5-b72b-1ae7819b7e3a
relation.isProjectOfPublication.latestForDiscovery779ae807-14a4-4e58-87e2-6dd81eb7a030

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