Pine Plantation Identification using NAIP Imagery and a Convolutional Neural Network
| dc.contributor.author | Miller, Benjamin | en |
| dc.contributor.author | Thomas, Valerie A. | en |
| dc.contributor.author | Wynne, Randolph H. | en |
| dc.coverage.country | United States | en |
| dc.date.accessioned | 2021-05-06T13:00:39Z | en |
| dc.date.available | 2021-05-06T13:00:39Z | en |
| dc.date.issued | 2021-04-30 | en |
| dc.description.abstract | Pine 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.sponsorship | Virginia Tech. Office of Geographical Information Systems and Remote Sensing | en |
| dc.format.mimetype | application/pdf | en |
| dc.identifier | 20210426_OGIS_NAIP_CNN.pdf | en |
| dc.identifier.uri | http://hdl.handle.net/10919/103216 | en |
| dc.language.iso | en | en |
| dc.publisher | Virginia Tech | en |
| dc.relation.ispartof | 2021 GIS and Remote Sensing Research Symposium | en |
| dc.rights | Creative Commons Attribution 4.0 International | en |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | en |
| dc.title | Pine Plantation Identification using NAIP Imagery and a Convolutional Neural Network | en |
| dc.type | Poster | en |
| dc.type | Conference proceeding | en |
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