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isp-uv-es/ai4smallfarms-taco

AI4SmallFarms This is a repackaging, not a new dataset. It is AI4SmallFarms by University of Twente ITC (Persello et al.), 4TU.ResearchData / DANS, converted to TACO with its data unchanged. 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.… See the full description on the dataset page: https://huggingface.co/datasets/isp-uv-es/ai4smallfarms-taco.

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<p align="center"><img src="assets/logos.png" width="546" alt="Image and Signal Processing Group, Universitat de València · Elliot"></p>

AI4SmallFarms

This is a repackaging, not a new dataset. It is AI4SmallFarms by University of Twente ITC (Persello et al.), 4TU.ResearchData / DANS, converted to TACO with its data unchanged. 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:

bibtex
@article{persello2023ai4smallfarms,
  title   = {AI4SmallFarms: A Dataset for Crop Field Delineation in Southeast Asian Smallholder Farms},
  author  = {Persello, Claudio and Grift, Jeroen and Fan, Xinyan and Paris, Claudia and Hansch, Ronny and Koeva, Mila and Nelson, Andy},
  journal = {IEEE Geoscience and Remote Sensing Letters},
  volume  = {20}, year = {2023},
  doi     = {10.1109/LGRS.2023.3323095}
}

About the data

Crop-field boundary delineation in smallholder landscapes: 149 Sentinel-2 tiles of about 5x5 km at 10 m (B2/B3/B4/B8, reflectance x 1e4) with a binary field mask.

149 samples · splits: test 24 · train 103 · validation 22 · tasks: semantic-segmentation

Packaged as TACO v3.

<details> <summary>Full description</summary>

  • —62 tiles over Vietnam and Cambodia carrying 439,001 field polygons: the benchmark.
  • —87 tiles over the Netherlands, the authors' pre-training set.

Splits. The release's own, per region.

Tiles. Stored at their native size, not cropped.

</details>

Getting started

bash
git clone https://github.com/OscarPellicer/taco
pip install -e "taco[ml]"

Read it straight from the Hub:

python
from huggingface_hub import hf_hub_download
from taco.ml import Dataset, plot_sample

path = hf_hub_download("isp-uv-es/ai4smallfarms-taco", "ai4smallfarms.tacozip", repo_type="dataset")
ds = Dataset(path)
plot_sample(ds[0])

or from a local copy:

python
ds = Dataset("ai4smallfarms.tacozip")
sample = ds[0]                      # {slot name: SlotValue}, arrays decoded
sample["image"].array.shape

Metadata without decoding anything:

python
import taco
taco.read("ai4smallfarms.tacozip")              # one Arrow table, levels joined

Samples

[image] [image] [image] [image] [image]

What a sample contains

roleslotholdsmodalitydetail
inputimagerasteroptical4 band(s), unit 1, scaled
targetfieldmasklabel_raster2 classes

Licence

CC-BY-4.0

Providers: University of Twente ITC (Persello et al.), 4TU.ResearchData / DANS

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.