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Multi-Layer Modeling of Dense Vegetation from Aerial LiDAR Scans

hal.structure.identifierLaboratoire sciences et technologies de l'information géographique [LaSTIG]
hal.structure.identifierBiodiversité, Gènes & Communautés [BioGeCo]
dc.contributor.authorKALINICHEVA, Ekaterina
hal.structure.identifierLaboratoire sciences et technologies de l'information géographique [LaSTIG]
dc.contributor.authorLANDRIEU, Loic
hal.structure.identifierLaboratoire sciences et technologies de l'information géographique [LaSTIG]
dc.contributor.authorMALLET, Clément
hal.structure.identifierInstitut Polytechnique de Bordeaux [Bordeaux INP]
dc.contributor.authorCHEHATA, Nesrine
dc.date.conference2022-06-19
dc.description.abstractEnThe analysis of the multi-layer structure of wild forests is an important challenge of automated large-scale forestry. While modern aerial LiDARs offer geometric information across all vegetation layers, most datasets and methods focus only on the segmentation and reconstruction of the top of canopy. We release WildForest3D, which consists of 29 study plots and over 2000 individual trees across 47 000m 2 with dense 3D annotation, along with occupancy and height maps for 3 vegetation layers: ground vegetation, understory, and overstory. We propose a 3D deep network architecture predicting for the first time both 3D pointwise labels and high-resolution layer occupancy rasters simultaneously. This allows us to produce a precise estimation of the thickness of each vegetation layer as well as the corresponding watertight meshes, therefore meeting most forestry purposes. Both the dataset and the model are released in open access: https://github.com/ ekalinicheva/multi_layer_vegetation.
dc.language.isoen
dc.source.title2022 IEEE/CVF Conference on Computer VIsion and Pattern Recognition Workshops
dc.title.enMulti-Layer Modeling of Dense Vegetation from Aerial LiDAR Scans
dc.typeCommunication dans un congrès
dc.subject.halSciences de l'environnement
dc.subject.halInformatique [cs]/Vision par ordinateur et reconnaissance de formes [cs.CV]
bordeaux.countryUS
bordeaux.title.proceeding2022 IEEE/CVF Conference on Computer VIsion and Pattern Recognition Workshops
bordeaux.conference.cityNew Orleans
bordeaux.peerReviewedoui
hal.identifierhal-03718729
hal.version1
hal.invitednon
hal.proceedingsnon
hal.popularnon
hal.audienceInternationale
hal.origin.linkhttps://hal.archives-ouvertes.fr//hal-03718729v1
bordeaux.COinSctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.btitle=2022%20IEEE/CVF%20Conference%20on%20Computer%20VIsion%20and%20Pattern%20Recognition%20Workshops&rft.au=KALINICHEVA,%20Ekaterina&LANDRIEU,%20Loic&MALLET,%20Cl%C3%A9ment&CHEHATA,%20Nesrine&rft.genre=unknown


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