AgricultureVision

aerial imagery agriculture computer vision deep learning machine learning

Description

Agriculture-Vision aims to be a publicly available large-scale aerial agricultural image dataset that is high-resolution, multi-band, and with multiple types of patterns annotated by agronomy experts. The original dataset affiliated with the 2020 CVPR paper includes 94,986 512x512images sampled from 3,432 farmlands with nine types of annotations: double plant, drydown, endrow, nutrient deficiency, planter skip, storm damage, water, waterway and weed cluster. All of these patterns have substantial impacts on field conditions and the final yield. These farmland images were captured between 2017 and 2019 across multiple growing seasons in numerous farming locations in the US. Each field image contains four color channels: Near-infrared (NIR), Red, Green and Blue. We first randomly split the 3,432 farmland images with a 6/2/2 train/val/test ratio. We then assign each sampled image to the split of the farmland image they are cropped from. This guarantees that no cropped images from the same farmland will appear in multiple splits in the final dataset. The generated (supervised) Agriculture-Vision dataset thus contains 56,944/18,334/19,708 train/val/test images. Additionally, we continue to grow this datset. In 2021 as a part of the Prize Challenge at CVPR, we have added sequences of full-field imagery across 52 fields to promote the use of weakly supervised methods.

Update Frequency

Periodically

License

Provided in the bucket.

Documentation

https://arxiv.org/abs/2001.01306

Managed By

Intelinair, Inc.

See all datasets managed by Intelinair, Inc..

Contact

support@intelinair.com

Usage Examples

Publications

Resources on AWS

  • Description
    Original dataset affiliated with the 2020 CVPR paper. Dataset provided as a series of tar.gz files with data for each year and an associated json file dscribing the train/validation/test split.
    Resource type
    S3 Bucket
    Amazon Resource Name (ARN)
    arn:aws:s3:::intelinair-data-releases/agriculture-vision/cvpr_paper_2020
    AWS Region
    us-east-1
    AWS CLI Access (No AWS account required)
    aws s3 ls s3://intelinair-data-releases/agriculture-vision/cvpr_paper_2020/ --no-sign-request
  • Description
    Dataset affiliated with the 2021 CVPR Agricutlure Vision Workshop. This includes both the supervised and additional raw imagery. The supervised portion is split train-val-test. The full-field imagery is given as a series of folders where each folder corresponds to a field, and the images contained are named according to the date of collection. This is the high-resolution-only subset of cvpr_challenge_2021_full.
    Resource type
    S3 Bucket
    Amazon Resource Name (ARN)
    arn:aws:s3:::intelinair-data-releases/agriculture-vision/cvpr_challenge_2021
    AWS Region
    us-east-1
    AWS CLI Access (No AWS account required)
    aws s3 ls s3://intelinair-data-releases/agriculture-vision/cvpr_challenge_2021/ --no-sign-request
  • Description
    Dataset affiliated with the 2021 CVPR Agricutlure Vision Workshop. This includes both the supervised and additional raw imagery, both high-resolution (10cm/pixel) and low-resolution (sentinel-1 10m/pixel) imagery. The supervised portion is split train-val-test. The full-field imagery is given as a series of folders where each folder corresponds to a field, and the images contained are named according to the date of collection. High-resolution images are named _.tif Low-resolution images are named gamma0_low.tif.
    Resource type
    S3 Bucket
    Amazon Resource Name (ARN)
    arn:aws:s3:::intelinair-data-releases/agriculture-vision/cvpr_challenge_2021_full
    AWS Region
    us-east-1
    AWS CLI Access (No AWS account required)
    aws s3 ls s3://intelinair-data-releases/agriculture-vision/cvpr_challenge_2021_full/ --no-sign-request

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