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
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
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
(18.7 GB)
| Name | Size | |
|---|---|---|
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CNN_LABELS_2019.zip
md5:cd47bc6b75f674a9fde56536cf4f3996 |
302.3 kB | Download |
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D17_CHM_all.tfw
md5:1c5c4143ea190be96ccd66b370081ae1 |
90 Bytes | Download |
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D17_CHM_all.tif
md5:7cd563036f50261f4933fddb1397ce65 |
959.0 MB | Download |
|
D17_CHM_all.tif.aux.xml
md5:f6f84f1a7b1142cc8608a5b8ba1efad6 |
2.5 kB | Download |
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D17_CHM_all.tif.ovr
md5:7093ed985e579b6c1c488ed4d4355462 |
325.4 MB | Download |
|
D17_CHM_all.zip
md5:d83553422da132670e91d94633ffed3a |
299.4 MB | Download |
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Labels_Trimmed_Selective.CPG
md5:ae3b3df9970b49b6523e608759bc957d |
5 Bytes | Download |
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Labels_Trimmed_Selective.dbf
md5:154608e331495a1d4b78dbbf6178a336 |
11.5 kB | Download |
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Labels_Trimmed_Selective.prj
md5:91faad560efe79cfac796ce48b289b45 |
403 Bytes | Download |
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Labels_Trimmed_Selective.sbn
md5:7396a055c52e0498819501a4eac3ee15 |
7.2 kB | Download |
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Labels_Trimmed_Selective.sbx
md5:0a30d4dff47b380912b05adb618af470 |
532 Bytes | Download |
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Labels_Trimmed_Selective.shp
md5:956e78cc3e8e46715cd8b6813627c1ca |
462.1 kB | Download |
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Labels_Trimmed_Selective.shp.xml
md5:fe8a57215bd3669ae46b2947eb424253 |
17.8 kB | Download |
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Labels_Trimmed_Selective.shx
md5:a9fb286779acee2b88816e29fccefd1d |
5.8 kB | Download |
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NEON_D17_TEAK_DP1_20170627_181333_reflectance.tif
md5:63ee0d65be0e9acaf7a9d3d4b077231e |
9.6 GB | Download |
|
NEON_D17_TEAK_DP1_20170627_181333_reflectance.tif.aux.xml
md5:406e22158f789ae3670f004d71a83fe1 |
7.4 kB | Download |
|
NEON_D17_TEAK_DP1_20170627_181333_reflectance.tif.enp
md5:dc0d07b092c3caedb9251657499d1227 |
639 Bytes | Download |
|
NEON_D17_TEAK_DP1_20170627_181333_reflectance.tif.ovr
md5:1a198f7cbc697d13f1e2e8fe320a436e |
1.6 GB | Download |
|
NEON_D17_TEAK_DP1QA_20170627_181333_RGB_Reflectance.zip
md5:3a189d128d457e7ddd8bb28b0d131140 |
5.9 GB | Download |
973
9,209
views
downloads
| All versions | This version | |
|---|---|---|
| Views | 973 | 127 |
| Downloads | 9,209 | 6,672 |
| Data volume | 47.1 TB | 41.1 TB |
| Unique views | 842 | 120 |
| Unique downloads | 1,094 | 671 |
Indexed in
- Publication date:
- September 27, 2019
- DOI:
-
- Keyword(s):
- shapefile tree species convolutional neural network hyperspectral imagery NEON
- Published in:
- Remote Sensing:.
- License (for files):
- Creative Commons Attribution 4.0 International