[ICCV 25] Deep Space Weather Model: Long-Range Solar Flare Prediction from Multi-Wavelength Images 🛰️

Deep Space Weather Model (Deep SWM) - accurately and reliably predicts solar flares by capturing long-range spatio-temporal dependencies and fine-grained features in multi-channel solar image series.

📖 Abstract

<details> <summary>Click to expand</summary> Accurate, reliable solar flare prediction is crucial for mitigating potential disruptions to critical infrastructure, while predicting solar flares remains a significant challenge. Existing methods based on heuristic physical features often lack representation learning from solar images. On the other hand, end-to-end learning approaches struggle to model long-range temporal dependencies in solar images.

In this study, we propose Deep Space Weather Model (Deep SWM), which is based on multiple deep state space models for handling both ten-channel solar images and long-range spatio-temporal dependencies. Deep SWM also features a sparse masked autoencoder, a novel pretraining strategy that employs a two-phase masking approach to preserve crucial regions such as sunspots while compressing spatial information. Furthermore, we built FlareBench, a new public benchmark for solar flare prediction covering a full 11-year solar activity cycle, to validate our method. Our method outperformed baseline methods and even human expert performance on standard metrics in terms of performance and reliability.

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✨ Key Features

Solar Spatial Encoder (SSE): Captures spatio-temporal features by selectively weighting image channels and modeling dependencies across space and time.
Long-range Temporal SSM (LT-SSM): Extends deep state-space models to capture temporal patterns exceeding the solar rotation period.
Sparse MAE: A tailored pretraining strategy for solar images that preserves critical but sparse regions (like sunspots) using a novel two-phase masking approach.
FlareBench Dataset: A new public benchmark covering a complete 11-year solar cycle for robust and unbiased evaluation.

<img src="web/images/model_white.png" width="800px" alt="Deep SWM Architecture"> Deep SWM Architecture: Solar Spatial Encoder (SSE) and Long-range Temporal SSM (LT-SSM) <img src="web/images/pretraining_white.png" width="600px" alt="Sparse MAE Pretraining"> Our Sparse MAE pre-training preserves critical sunspot regions better than standard methods.

🏆 Results

Our model demonstrates state-of-the-art performance, outperforming existing methods and even human experts on the comprehensive FlareBench dataset.

<details> <summary>Quantitative Comparison</summary> Our method achieves the highest scores across all standard evaluation metrics.

| Method | Test Period | GMGS↑ | BSS≥M↑ | TSS≥M↑ | | --------------------------------- | ------------------------------------ | ----------------- | -------------------- | ----------------- | | Flare Transformer (w/o PF) [1] | 2014-2017 (4 years) | 0.220±0.116 | -1.770±0.225 | 0.198±0.371 | | DeFN-R [2] | 2014-2015 (2 years) | 0.302±0.055 | 0.036±0.982 | 0.279±0.162 | | CNN-LSTM | 2019-12-01 - 2022-11-30 (3 years) | 0.315±0.166 | 0.272±0.259 | 0.330±0.306 | | DeFN [3] | 2014-2015 (2 years) | 0.375±0.141 | 0.022±0.782 | 0.413±0.150 | | Flare Transformer (full) [1] | 2014-2017 (4 years) | 0.503±0.059 | 0.082±0.974 | 0.530±0.112 | | Ours | 2019-12-01 - 2022-11-30 (3 years)| 0.582±0.032 | 0.334±0.299 | 0.543±0.074 | | Experts | 2000-2015 (16 years) | 0.48 | 0.16 | 0.50 |

</details> <details> <summary>Qualitative Examples</summary> Deep SWM successfully predicts high-impact X-class flares where baseline models fail. <img src="web/images/qualitative_results_white.png" alt="Qualitative results for flare predictions"> Qualitative results showing successful predictions of X-class and M-class solar flares. </details>

🛠️ Getting Started

Installation

Clone the repository and set up the environment:

# Clone the repository
git clone https://github.com/username/DeepSWM.git
cd DeepSWM

# Install dependencies
pip install -r requirements.txt

Usage

📊 Data Preparation

The FlareBench dataset will be made available on Zenodo upon publication. Please check back soon for download instructions.

🚀 Pre-training

Run the pre-training process to learn representations from the magnetogram images:

cd ml
python pretrain.py --input_dir ../flarebench_dataset/all_data_hours \
                  --output_dir ../flarebench_dataset/all_features \
                  --mode train \
                  --data_root ../flarebench_dataset

🔍 Feature Extraction

Extract intermediate features from the pre-trained model:

cd ml
python pretrain.py --input_dir ../flarebench_dataset/all_data_hours \
                  --output_dir ../flarebench_dataset/all_features \
                  --mode inference_all \
                  --data_root ../flarebench_dataset

Organize the extracted features by sample:

cd ml
python src/features/create_history_features.py

🎯 Training

Train the model using the extracted features:

cd ml
python main.py --params params/main/params.yaml \
              --imbalance \
              --fold 3 \
              --data_root ../flarebench_dataset \
              --cuda_device 0

📚 References

[1] Kanta Kaneda et al. Flare transformer: Solar flare prediction using magnetograms and sunspot physical features. In ACCV, pages 1488–1503, 2022.

[2] Naoto Nishizuka, Yuki Kubo, et al. Reliable probability forecast of solar flares: Deep flare net-reliable (defn-r). The Astrophysical Journal, 899(2):150, 2020.

[3] N. Nishizuka, K. Sugiura, Y. Kubo, M. Den, and M. Ishii. Deep flare net (defn) model for solar flare prediction. The Astrophysical Journal, 858(2):113, 2018.

📝 Citation

If you find our work helpful, please consider citing the following paper and/or ⭐ the repo:

@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}
}

📄 License

This work is licensed under the BSD-3-Clause-Clear License. To view a copy of this license, see LICENSE.

DeepSWM

Install / Use

README