Dataset Open Access

A Convolutional Neural Network classifier identifies tree species in mixed-conifer forest from hyperspectral imagery

Geoffrey A Fricker; Jonathan Daniel Ventura; Jeffrey Wolf; Malcolm P. North; Frank W. Davis; Janet Franklin


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@dataset{geoffrey_a_fricker_2019_3470250,
  author       = {Geoffrey A Fricker and
                  Jonathan Daniel Ventura and
                  Jeffrey Wolf and
                  Malcolm P. North and
                  Frank W. Davis and
                  Janet Franklin},
  title        = {{A Convolutional Neural Network classifier 
                   identifies tree species in mixed-conifer forest
                   from hyperspectral imagery}},
  month        = sep,
  year         = 2019,
  note         = {{Data to replicate the experiment is available for 
                   download in two zipped files: "NEON\_D17\_TEAK\_DP1QA
                   \_20170627\_181333\_RGB\_Reflectance.zip" (Imagery)
                   "CNN\_LABELS\_2019.zip" (Training Label Shapefiles)
                   * Note: The imagery is 5.5 gb (zipped).    All
                   code used to run the analysis is located in a
                   repository here:
                   https://github.com/jonathanventura/canopy  The
                   only flightline you will need to repeat our
                   results is called
                   "NEON\_D17\_TEAK\_DP1\_20170627\_181333".   If you
                   download your own NEON data, the raw HDF 5 files
                   can be converted to a geotiff using R code found
                   here:  http://neonscience.github.io/neon-data-
                   institute-2016//R/open-NEON-hdf5-functions/
                   Contact the National Ecological Observatory
                   Network (NEON) to download the comparable imagery
                   data files for all sites and collections:
                   https://data.neonscience.org/home.}},
  publisher    = {Zenodo},
  doi          = {10.5281/zenodo.3470250},
  url          = {https://doi.org/10.5281/zenodo.3470250}
}
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