Datasets:

heesup
/

VisTA-SR

annotations_creators:
  - expert-generated
language_creators:
  - found
language:
  - en
license: apache-2.0
multimonolingual: false
size_categories:
  - 1K<n<10K
source_datasets:
  - original
task_categories:
  - image-to-image
task_ids:
  - super-resolution
pretty_name: VisTA-SR Paired Thermal-RGB Agricultural Dataset
tags:
  - agriculture
  - thermal-imaging
  - super-resolution
  - cvpr-2024

VisTA-SR: Paired Low/High-Resolution Thermal & RGB Agricultural Dataset (`Training_T4_1_2_3`)

Official dataset repository for the CVPR 2024 Workshop paper: "VisTA-SR: Improving the Accuracy and Resolution of Low-Cost Thermal Imaging Cameras for Agriculture"

📄 Paper HTML: CVPR 2024 OpenAccess
📄 Paper PDF: Download PDF
💻 Official Codebase: https://github.com/heesup/VisTA-SR

Dataset Description

This dataset consists of aligned multi-modal image triplets captured in field conditions (University of California, Davis) during the 2022 growing season across warm-season grain legume fields (Cowpea Vigna unguiculata and Common Bean Phaseolus vulgaris).

Image Modalities & Camera Hardware

Low-Resolution Thermal (IR_LOW): FLIR One Pro (160x120 radiometric thermal sensor, 8-14 µm spectral range).
High-Resolution Ground Truth Thermal (IR_HIGH): FLIR Boson / VarioCAM HD (640x512 / 1024x768 industrial radiometric thermal sensor).
Visible RGB (RGB): Integrated FLIR One Pro visible camera (1440x1080 resolution).

Dataset Structure (`Training_T4_1_2_3`)

Training_T4_1_2_3/
├── train/
│   ├── IR_LOW/      # Low-resolution 160x120 thermal images
│   ├── IR_HIGH/     # High-resolution ground truth thermal images
│   └── RGB/         # Paired visible RGB images
└── val/
    ├── IR_LOW/
    ├── IR_HIGH/
    └── RGB/

Quickstart & Usage

Downloading via Hugging Face `datasets`

from datasets import load_dataset

dataset = load_dataset("heesup/VisTA-SR")
print(dataset)

Citation

@inproceedings{yun2024vista,
  title={VisTA-SR: Improving the Accuracy and Resolution of Low-Cost Thermal Imaging Cameras for Agriculture},
  author={Yun, Heesup and Lo, Sassoum and Diepenbrock, Christine H and Bailey, Brian N and Earles, J Mason},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
  pages={5470--5479},
  year={2024}
}

VisTA-SR: Paired Low/High-Resolution Thermal & RGB Agricultural Dataset (Training_T4_1_2_3)