Published September 27, 2019
| Version v2
Dataset
Open
A Convolutional Neural Network classifier identifies tree species in mixed-conifer forest from hyperspectral imagery
Creators
- 1. Social Sciences Department, California Polytechnic State University, San Luis Obispo
- 2. Department of Computer Sciences and Software Engineering, California Polytechnic State University, San Luis Obispo
- 3. Amazon Corporation
- 4. US Forest Service, PSW Research Station
- 5. Bren School of Environmental Science and Management, University of California, Santa Barbara
- 6. Department of Botany and Plant Sciences, University of California, Riverside
Description
Digital Publication of the training data polygons and hyperspectral imagery used in the manuscript "A Convolutional Neural Network classifier identifies tree species in mixed-conifer forest from hyperspectral imagery".
Code is available in a Jupyter Notebook and can be found here: https://github.com/jonathanventura/canopy
National Ecological Observatory Network. 2018. Provisional data downloaded from http://data.neonscience.org on 22 June 2018. Battelle, Boulder, CO, USA
Notes
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.
Files
CNN_LABELS_2019.zip
Files
(18.7 GB)
| Name | Size | Download all |
|---|---|---|
|
md5:cd47bc6b75f674a9fde56536cf4f3996
|
302.3 kB | Preview Download |
|
md5:1c5c4143ea190be96ccd66b370081ae1
|
90 Bytes | Download |
|
md5:7cd563036f50261f4933fddb1397ce65
|
959.0 MB | Preview Download |
|
md5:f6f84f1a7b1142cc8608a5b8ba1efad6
|
2.5 kB | Preview Download |
|
md5:7093ed985e579b6c1c488ed4d4355462
|
325.4 MB | Download |
|
md5:d83553422da132670e91d94633ffed3a
|
299.4 MB | Preview Download |
|
md5:ae3b3df9970b49b6523e608759bc957d
|
5 Bytes | Download |
|
md5:154608e331495a1d4b78dbbf6178a336
|
11.5 kB | Download |
|
md5:91faad560efe79cfac796ce48b289b45
|
403 Bytes | Download |
|
md5:7396a055c52e0498819501a4eac3ee15
|
7.2 kB | Download |
|
md5:0a30d4dff47b380912b05adb618af470
|
532 Bytes | Download |
|
md5:956e78cc3e8e46715cd8b6813627c1ca
|
462.1 kB | Download |
|
md5:fe8a57215bd3669ae46b2947eb424253
|
17.8 kB | Preview Download |
|
md5:a9fb286779acee2b88816e29fccefd1d
|
5.8 kB | Download |
|
md5:63ee0d65be0e9acaf7a9d3d4b077231e
|
9.6 GB | Preview Download |
|
md5:406e22158f789ae3670f004d71a83fe1
|
7.4 kB | Preview Download |
|
md5:dc0d07b092c3caedb9251657499d1227
|
639 Bytes | Download |
|
md5:1a198f7cbc697d13f1e2e8fe320a436e
|
1.6 GB | Download |
|
md5:3a189d128d457e7ddd8bb28b0d131140
|
5.9 GB | Preview Download |