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.), 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{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 H{\"a}nsch, Ronny and Koeva, Mila and Nelson, Andy},
  journal = {IEEE Geoscience and Remote Sensing Letters},
  volume  = {20}, pages = {1--5}, 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.

Full description

• 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.

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/ai4smallfarms-taco", "ai4smallfarms.zip", repo_type="dataset")
ds = Dataset(path)
plot_sample(ds[0])

or from a local copy:

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

Metadata without decoding anything:

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

Samples

What a sample contains

role	slot	holds	modality	detail
input	image	raster	optical	4 band(s), unit 1, scaled
target	field	mask	label_raster	2 classes

Licence

CC-BY-4.0

Providers: University of Twente ITC (Persello 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.