Datasets:
Tasks:
Image-to-Image
Modalities:
Image
Formats:
imagefolder
Sub-tasks:
super-resolution
Languages:
English
Size:
10K - 100K
License:
File size: 2,835 Bytes
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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](https://openaccess.thecvf.com/content/CVPR2024W/Vision4Ag/html/Yun_VisTA-SR_Improving_the_Accuracy_and_Resolution_of_Low-Cost_Thermal_Imaging_CVPRW_2024_paper.html)
- 📄 **Paper PDF**: [Download PDF](https://openaccess.thecvf.com/content/CVPR2024W/Vision4Ag/papers/Yun_VisTA-SR_Improving_the_Accuracy_and_Resolution_of_Low-Cost_Thermal_Imaging_CVPRW_2024_paper.pdf)
- 💻 **Official Codebase**: [https://github.com/heesup/VisTA-SR](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`
```python
from datasets import load_dataset
dataset = load_dataset("heesup/VisTA-SR")
print(dataset)
```
---
## Citation
```bibtex
@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}
}
```
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