isp-uv-es/agrifieldnet-india-taco

AgriFieldNet India Competition

This is a repackaging, not a new dataset. It is AgriFieldNet India Competition by Radiant Earth Foundation, IDinsight, 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:

@misc{radiantearth2022agrifieldnet,
  author = {Radiant Earth Foundation and IDinsight},
  title  = {AgriFieldNet Competition Dataset},
  year   = {2022}, version = {1.0}, publisher = {Radiant MLHub},
  doi    = {10.34911/rdnt.wu92p1}
}

About the data

Crop-type segmentation over four states of northern India: 1165 Sentinel-2 chips of 256x256 at 10 m, each with a dense crop mask over 13 crop classes (codes up to 36; unused codes are listed as unassigned) and a field-id raster.

1,165 samples · splits: train 929 · validation 236 · tasks: semantic-segmentation

Packaged as TACO v3.

Full description

Radiometry. The imagery is 8-bit with a per-scene linear stretch: matched to their source L2A scenes, two chips fit L2A = g x DN + o per band with g and o differing by band and by chip. No dataset-wide scale exists, so the slot is requantised with none, and its level is unknown.

Splits. The competition's field-level test labels are withheld, so only the train-labelled chips are included, and the train/val cut is a spatial blocking defined here.

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

or from a local copy:

ds = Dataset("agrifieldnet-india.zip")
sample = ds[0]                      # {slot name: SlotValue}, arrays decoded
sample["s2"].array.shape

Metadata without decoding anything:

import taco
taco.read("agrifieldnet-india.zip")              # one Arrow table, levels joined

Samples

What a sample contains

role	slot	holds	modality	detail
input	s2	raster	multispectral	12 band(s), requantised
target	crop_type	mask	label_raster	37 classes
target	field_id	instance_id	label_raster

Licence

CC-BY-4.0

Providers: Radiant Earth Foundation, IDinsight

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.