---
license: cc-by-4.0
dataset_info:
  features:
  - name: solar_images
    dtype: image
  - name: xrs
    dtype: float32
  configs:
  - config_name: default
    data_files:
    - split: train
      path: "**/*"
language:
- en
tags:
- solar-physics
- space-weather
- flare-prediction
- astronomy
- time-series
- computer-vision
- deep-learning
- iccv2025
size_categories:
- 10K<n<100K
task_categories:
- time-series-forecasting
- image-classification
pretty_name: FlareBench
---

# FlareBench: A Comprehensive Benchmark for Solar Flare Prediction 🌞

[![ICCV 2025](https://img.shields.io/badge/ICCV-2025-b31b1b.svg)](https://iccv.thecvf.com/)
[![arXiv](https://img.shields.io/badge/arXiv-2508.07847-b31b1b.svg)](https://arxiv.org/abs/2508.07847)
[![Project Page](https://img.shields.io/badge/Project-Page-brightgreen.svg)](https://keio-smilab25.github.io/DeepSWM)

**FlareBench** is a novel benchmark dataset for solar flare prediction that covers the entire 11-year solar activity cycle. This dataset was introduced in our ICCV 2025 paper "Deep Space Weather Model: Long-Range Solar Flare Prediction from Multi-Wavelength Images".

## 🚀 What's New

- **ICCV 2025 Accepted!** Our paper has been accepted to ICCV 2025
- **Complete Solar Cycle Coverage**: First benchmark covering a full 11-year solar activity cycle
- **Multi-Modal Data**: Combines solar images (AIA/HMI) and X-ray sensor data (XRS)
- **Time-Series Cross-Validation**: Designed for robust evaluation across diverse solar activity states

## 📊 Dataset Overview

FlareBench addresses the limitations of conventional solar flare prediction datasets that often:
- Cover only limited periods of solar activity
- Contain small, low-resolution sunspot patches  
- Exhibit biases towards specific solar activity periods

### Why FlareBench?

Most conventional datasets for solar flare prediction do not cover diverse solar activity states. Consequently, models trained on such datasets can exhibit biases towards specific periods of solar activity. Furthermore, many datasets contain only small, low-resolution sunspot patches.

**FlareBench presents unique challenges:**
- Requires modeling long-term, diverse solar states spanning the entire 11-year solar cycle
- Demands computationally efficient architectures for multi-wavelength images capturing multi-layered physical phenomena
- Handles highly imbalanced class distributions that vary significantly across different years

## 🗂️ Dataset Structure

```
FlareBench/
├── solar_images/
│   ├── aia/          # Atmospheric Imaging Assembly (9 wavelengths)
│   │   ├── 2011/     # 📊 Data available (Apr-Dec 2011)
│   │   ├── 2012/     # 📁 Directory structure only
│   │   ├── ...
│   │   └── 2024/
│   └── hmi/          # Helioseismic and Magnetic Imager
│       ├── 2011/     # 📊 Data available (Jan-Dec 2011)  
│       ├── 2012/     # 📁 Directory structure only
│       └── ...
└── xrs/              # X-Ray Sensor data from GOES satellites
    ├── 2011/         # 📊 Data available (full year)
    ├── 2012/         # 📁 Directory structure only
    ├── ...
    ├── g15/          # GOES-15 specific data structure
    └── g16/          # GOES-16 specific data structure
```

## 📈 Dataset Statistics

- **Total Samples**: 95,837 (after quality filtering)
- **Time Period**: June 2011 - November 2022 (full solar cycle)
- **Temporal Resolution**: 1-hour cadence
- **Spatial Resolution**: Full-disk solar observations
- **Class Distribution**:
  - X-class: 1,750 samples (1.8%)
  - M-class: 13,263 samples (13.8%) 
  - C-class: 34,978 samples (36.5%)
  - No-flare: 47,775 samples (49.9%)

### Data Composition

**Solar Images:**
- **AIA (Atmospheric Imaging Assembly)**: Multi-wavelength extreme ultraviolet observations capturing the multi-layered coronal atmosphere
- **HMI (Helioseismic and Magnetic Imager)**: Line-of-sight and vector magnetic field observations from the photosphere

**X-Ray Sensor Data:**
- **XRS**: Solar X-ray flux measurements from GOES satellites
- **Formats**: Both CSV and NetCDF files
- **Content**: Raw and processed solar X-ray flux data

## 🎯 Prediction Task

FlareBench focuses on predicting the **maximum class of solar flare within the next 24 hours**, following standard approaches in solar flare prediction research. The prediction classes are:

- **X-class**: Major flares (≥10⁻⁴ W/m²)
- **M-class**: Moderate flares (10⁻⁵ to 10⁻⁴ W/m²)  
- **C-class**: Minor flares (10⁻⁶ to 10⁻⁵ W/m²)
- **No-flare**: Below C-class threshold

## 📋 Current Release (2011 Data)

This initial release contains **2011 data only**. The complete dataset spanning 2011-2022 will be released upon paper publication.

**Available in this release:**
- Solar images: April-December 2011 (AIA), January-December 2011 (HMI)
- XRS data: Full year 2011
- Directory structure for all years (2010-2024) for future releases

## 📧 Request Full Dataset

**Need data from other years (2012-2022)?** Please contact us at: [contact email]

We will provide access to the complete dataset for research purposes. Please include:
- Your research affiliation
- Brief description of your research project
- Intended use of the dataset

## 🔬 Evaluation Protocol

FlareBench uses **time-series cross-validation** to ensure unbiased evaluation:

- **3-fold cross-validation** covering different phases of the solar cycle
- **Chronological splits** maintaining temporal order
- **Diverse solar conditions** in each fold's test set
- **Standard metrics**: TSS, BSS, GMGS for comprehensive evaluation

## 📚 Citation

If you use FlareBench in your research, please cite our ICCV 2025 paper:

```bibtex
@inproceedings{nagashima2025deepswm,
  title={Deep Space Weather Model: Long-Range Solar Flare Prediction from Multi-Wavelength Images},
  author={Shunya Nagashima and Komei Sugiura},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
  year={2025}
}
```

## 🔗 Related Resources

- **Paper**: [arXiv:2508.07847](https://arxiv.org/abs/2508.07847)
- **Project Page**: [https://keio-smilab25.github.io/DeepSWM](https://keio-smilab25.github.io/DeepSWM)
- **Code Repository**: [GitHub](https://github.com/keio-smilab25/DeepSWM)

## 📄 License

This dataset is released under the **Creative Commons Attribution 4.0 International License (CC BY 4.0)**.

You are free to:
- **Share** — copy and redistribute the material in any medium or format
- **Adapt** — remix, transform, and build upon the material for any purpose, even commercially

Under the following terms:
- **Attribution** — You must give appropriate credit and indicate if changes were made

## 🤝 Acknowledgments

This dataset is built using observations from:
- **Solar Dynamics Observatory (SDO)**: NASA's flagship solar observation mission
- **GOES Satellites**: NOAA's Geostationary Operational Environmental Satellites

We thank the SDO and GOES teams for making these invaluable observations publicly available.

## 📞 Contact

For questions, issues, or collaboration opportunities:

- **Primary Contact**: [Your Name] - [email]
- **Institution**: [Your Institution]
- **Project Page**: [https://keio-smilab25.github.io/DeepSWM](https://keio-smilab25.github.io/DeepSWM)

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