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IDTReeS 2020 Competition Data

Graves, Sarah; Marconi, Sergio


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{
  "description": "<p>Data provided by the Integrating Data science with Trees and Remote Sensing (IDTReeS) research group for use in the IDTReeS Competition.</p>\n\n<p>Geospatial and tabular data to be used in two data science tasks focused on using remote sensing data to quantify the locations, sizes and species identities of millions of trees and on determining how these methods generalize to other forests.</p>\n\n<p>Vector data are the geographic extents of Individual Tree Crown boundaries that have been identified by researchers in the IDTReeS group. The data were generated primarily by Sarah Graves, Sergio Marconi, and Benjamin Weinstein, with support from Stephanie Bohlman, Ethan White, and members of the IDTReeS group.</p>\n\n<p>Remote Sensing and Field data were generated by the National Ecological Observatory Network (NEON, Copyright &copy; 2017 Battelle). Data were selected, downloaded, and packaged by Sergio Marconi. The most recent available data of the following products are provided:</p>\n\n<p>National Ecological Observatory Network. 2020. Data Product DP1.30010.001, High-resolution orthorectified camera imagery. Provisional data downloaded from http://data.neonscience.org on March 4, 2020. Battelle, Boulder, CO, USA NEON. 2020.</p>\n\n<p>National Ecological Observatory Network. 2020. Data Product DP1.30003.001, Discrete return LiDAR point cloud. Provisional data downloaded from http://data.neonscience.org on March 4, 2020. Battelle, Boulder, CO, USA NEON. 2020.</p>\n\n<p>National Ecological Observatory Network. 2020. Data Product DP1.10098.001, Woody plant vegetation structure. Provisional data downloaded from http://data.neonscience.org on March 4, 2020. Battelle, Boulder, CO, USA NEON. 2020.</p>\n\n<p>National Ecological Observatory Network. 2020. Data Product DP3.30015.001, Ecosystem structure. Provisional data downloaded from http://data.neonscience.org on March 4, 2020. Battelle, Boulder, CO, USA NEON. 2020.</p>\n\n<p>NEON has the following <a href=\"https://www.neonscience.org/data/about-data/data-policies\">data policy</a>:</p>\n\n<p>&lsquo;The National Ecological Observatory Network is a program sponsored by the National Science Foundation and operated under cooperative agreement by Battelle Memorial Institute. This material is based in part upon work supported by the National Science Foundation through the NEON Program.&rsquo;</p>\n\n<p>THE NEON DATA PRODUCTS ARE PROVIDED &quot;AS IS&quot;, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, TITLE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR ANYONE DISTRIBUTING THE NEON DATA PRODUCTS BE LIABLE FOR ANY DAMAGES OR OTHER LIABILITY, WHETHER IN CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE NEON DATA PRODUCTS.</p>", 
  "license": "https://creativecommons.org/licenses/by/4.0/legalcode", 
  "creator": [
    {
      "affiliation": "University of Wisconsin-Madison", 
      "@id": "https://orcid.org/0000-0003-3805-4242", 
      "@type": "Person", 
      "name": "Graves, Sarah"
    }, 
    {
      "affiliation": "University of Florida", 
      "@id": "https://orcid.org/0000-0002-8096-754X", 
      "@type": "Person", 
      "name": "Marconi, Sergio"
    }
  ], 
  "url": "https://zenodo.org/record/3874909", 
  "datePublished": "2020-06-03", 
  "version": "2", 
  "keywords": [
    "NEON, ecology, remote sensing, data science, image processing, machine learning"
  ], 
  "@context": "https://schema.org/", 
  "distribution": [
    {
      "contentUrl": "https://zenodo.org/api/files/08fe2f02-8fed-408d-b0d2-94dd7e261f97/IDTREES_competition_test.zip", 
      "encodingFormat": "zip", 
      "@type": "DataDownload"
    }, 
    {
      "contentUrl": "https://zenodo.org/api/files/08fe2f02-8fed-408d-b0d2-94dd7e261f97/IDTREES_competition_train_v2.zip", 
      "encodingFormat": "zip", 
      "@type": "DataDownload"
    }, 
    {
      "contentUrl": "https://zenodo.org/api/files/08fe2f02-8fed-408d-b0d2-94dd7e261f97/IDTREES_competition_train.zip", 
      "encodingFormat": "zip", 
      "@type": "DataDownload"
    }
  ], 
  "identifier": "https://doi.org/10.5281/zenodo.3874909", 
  "@id": "https://doi.org/10.5281/zenodo.3874909", 
  "@type": "Dataset", 
  "name": "IDTReeS 2020 Competition Data"
}
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