Pine Plantation Identification using NAIP Imagery and a Convolutional Neural Network

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dc.contributor.authorMiller, Benjaminen
dc.contributor.authorThomas, Valerie A.en
dc.contributor.authorWynne, Randolph H.en
dc.coverage.countryUnited Statesen
dc.date.accessioned2021-05-06T13:00:39Zen
dc.date.available2021-05-06T13:00:39Zen
dc.date.issued2021-04-30en
dc.description.abstractPine plantations in the Southeastern United states are presently under-quantified using disturbance based metrics of forest change. Methods such as the Global Forest Change data-set have limited accuracy in identifying pine plantations. Direct estimation of pine plantations poses its’ own challenges but the structure of plantations creates an interesting opportunity. The uniform structure and pattern of pine plantations permits the implementation of object identifying neural network techniques using high spatial resolution imagery such as the National Agriculture Imagery Program. This presentation will explore the preliminary results of such a process.en
dc.description.sponsorshipVirginia Tech. Office of Geographical Information Systems and Remote Sensingen
dc.format.mimetypeapplication/pdfen
dc.identifier20210426_OGIS_NAIP_CNN.pdfen
dc.identifier.urihttp://hdl.handle.net/10919/103216en
dc.language.isoenen
dc.publisherVirginia Techen
dc.relation.ispartof2021 GIS and Remote Sensing Research Symposiumen
dc.rightsCreative Commons Attribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.titlePine Plantation Identification using NAIP Imagery and a Convolutional Neural Networken
dc.typePosteren
dc.typeConference proceedingen

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