datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
crop-burn-detection-raw
Crop Burn Detection — Raw Sentinel-2 (India, 2025)
Paired RGB + SWIR Sentinel-2 satellite image tiles across agricultural districts of northern India, capturing the paddy (Oct–Nov 2025) and wheat (Mar–May 2025) burning seasons. Built to train and benchmark vision models for real-time crop residue burn detection — including models designed to run directly on satellites.
Why We Built This
Every October and November, farmers across Punjab, Haryana, Uttar Pradesh, Rajasthan… See the full description on the dataset page: https://huggingface.co/datasets/munish0838/crop-burn-detection-raw.vegetable_crop_early_detection
Vegetable Crop Early Detection
A dataset for early stage object detection of vegetable crops. The dataset contains 2,801 images with 17,387 bounding box annotations across 6 categories.
This dataset is indexed on https://project-agml.github.io/ as part of the AgML python library.
Citation
@article{lac2022annotated,
title={An annotated image dataset of vegetable crops at an early stage of growth for proximal sensing applications},
author={Lac, Louis and… See the full description on the dataset page: https://huggingface.co/datasets/Project-AgML/vegetable_crop_early_detection.crop-burn-detection-labeled
Crop Burn Detection — Labeled Sentinel-2 (India, 2025)
Paired RGB + SWIR Sentinel-2 satellite image tiles across agricultural districts of northern India, with per-tile burn annotations. Covers the paddy (Oct–Nov 2025) and wheat (Mar–May 2025) stubble burning seasons across 68 districts. Built to train and benchmark vision models for real-time crop residue burn detection.
The raw (unlabeled) version is available as munish0838/crop-burn-detection-raw.
Why We Built This… See the full description on the dataset page: https://huggingface.co/datasets/munish0838/crop-burn-detection-labeled.