isp-uv-es/reforestree-taco

ReforesTree

This is a repackaging, not a new dataset. It is ReforesTree by ETH Zurich / WWF Ecuador (Reiersen 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:

@inproceedings{reiersen2022reforestree,
  title     = {{ReforesTree}: A Dataset for Estimating Tropical Forest Carbon Stock with Deep Learning and Aerial Imagery},
  author    = {Reiersen, Gyri and Dao, David and L{\"u}tjens, Bj{\"o}rn and Klemmer, Konstantin and Amara, Kenza and Steinegger, Attila and Zhang, Ce and Zhu, Xiaoxiang},
  booktitle = {Proceedings of the AAAI Conference on Artificial Intelligence},
  volume    = {36},
  number    = {11},
  pages     = {12119--12125},
  year      = {2022},
  doi       = {10.1609/aaai.v36i11.21471}
}

About the data

105 drone orthomosaic tiles of 4000x4000 RGB over 6 Ecuadorian agroforestry sites, annotated with 4663 tree crowns as bounding boxes, each labelled with one of six species groups and a field-measured above-ground biomass. Tiles are re-encoded from lossless PNG to COG, and per-crown biomass and carbon are carried as metadata.

105 samples · splits: test 36 · train 57 · validation 12 · tasks: object-detection

Packaged as TACO v3.

Full description

Splits. The release ships none, and neighbouring tiles of one orthomosaic would leak across a random split, so the assignment here is site-disjoint and is marked as such.

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/reforestree-taco", "reforestree.zip", repo_type="dataset")
ds = Dataset(path)
plot_sample(ds[0])

or from a local copy:

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

Metadata without decoding anything:

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

Samples

What a sample contains

role	slot	holds	modality	detail
input	image	raster	optical	3 band(s), render
target	crowns	bbox_2d
target	species	class_sequence		6 classes

Licence

CC-BY-4.0

Providers: ETH Zurich / WWF Ecuador (Reiersen 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.