isp-uv-es/idtrees-taco

IDTReeS 2020 individual tree crowns

This is a repackaging, not a new dataset. It is IDTReeS 2020 individual tree crowns by NEON (National Ecological Observatory Network), IDTReeS 2020 competition (Graves, Marconi et al.), converted to TACO. Pixel values and labels are kept as released except where the description below says otherwise. All credit belongs to the original authors: if you use it, please cite them and follow their licence.

original dataset · paper · licence: CC-BY-4.0

Repackaged into TACO by the Image and Signal Processing Group (ISP), Universitat de València, within the ELLIOT project.

Citation

Please cite the original work:

@article{graves2023idtrees,
  title   = {Data science competition for cross-site individual tree species identification from airborne remote sensing data},
  author  = {Graves, Sarah J. and Marconi, Sergio and Stewart, Dylan and Harmon, Ira and Weinstein, Ben and Kanazawa, Yuzi and Scholl, Victoria M. and Joseph, Maxwell B. and McGlinchy, Joseph and Browne, Luke and Sullivan, Megan K. and Estrada-Villegas, Sergio and Wang, Daisy Zhe and Singh, Aditya and Bohlman, Stephanie and Zare, Alina and White, Ethan P.},
  journal = {PeerJ},
  volume  = {11},
  pages   = {e16578},
  year    = {2023},
  doi     = {10.7717/peerj.16578}
}
@misc{graves2020idtreesdata,
  title     = {{IDTReeS} 2020 Competition Data},
  author    = {Graves, Sarah and Marconi, Sergio},
  publisher = {Zenodo},
  version   = {4},
  year      = {2020},
  doi       = {10.5281/zenodo.3934932}
}

About the data

Individual tree crown delineation and species identification in NEON airborne imagery: 85 plots of 20x20 m at two sites, each with a 0.1 m RGB orthophoto and a 1 m canopy height model.

85 samples · splits: train 68 · validation 17 · tasks: instance-segmentation, object-detection

Packaged as TACO v3.

Full description

Annotations. 1312 hand-delineated crown polygons, of which 1213 carry one of 33 field-identified taxa.

Scope. The release's 369-band hyperspectral cube and LAS point clouds are not included. The competition test set is a separate download.

Splits. The train/val split is not the release's; it is assigned here, blocked by plot.

Getting started

git clone --recursive https://github.com/OscarPellicer/taco
pip install -e "taco/python[ml]"     # builds the reader: C++23, CMake, Ninja, pkg-config, libcurl >= 7.83, OpenSSL >= 3

Read it straight from the Hub:

import os
from huggingface_hub import hf_hub_download, snapshot_download
from taco.ml import Dataset, plot_sample

path = hf_hub_download("isp-uv-es/idtrees-taco", "idtrees.zip", repo_type="dataset")
ds = Dataset(path)
plot_sample(ds[0])

or from a local copy:

ds = Dataset("idtrees.zip")
sample = ds[0]                      # {slot name: SlotValue}, arrays decoded
sample["rgb"].array.shape

Metadata without decoding anything:

import taco
taco.read("idtrees.zip")              # one Arrow table, levels joined

Samples

What a sample contains

role	slot	holds	modality	detail
input	rgb	raster	optical	3 band(s), render
input	chm	raster	elevation	1 band(s), unit m, physical
target	crowns	polygon
target	taxon	class_sequence		34 classes

Licence

CC-BY-4.0

Providers: NEON (National Ecological Observatory Network), IDTReeS 2020 competition (Graves, Marconi et al.)

Acknowledgements

TACO was designed by César Aybar and is specified at https://asterisk.coop/taco/spec/.

Built by Oscar Pellicer within the ELLIOT project at the Image and Signal Processing Group (ISP), Universitat de València.