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nasa-ibm-ai4science/ar_segmentation_surya

sourceHugging Faceapache-2.0updated 1y agoView on Hugging Face

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🌌 Surya – Active Region Segmentation

📖 Model Overview

This repository hosts fine-tuned weights of Surya – a heliophysics foundation model – for the task of solar Active Region (AR) segmentation. Solar Active Regions are magnetically complex structures associated with flares and coronal mass ejections (CMEs). Within ARs, the Polarity Inversion Line (PIL) serves as a critical precursor of eruptions. Accurate segmentation of ARs containing PILs is essential for space weather forecasting and understanding solar magnetic complexity.

📊 Results

We benchmarked Surya against a standard UNet baseline on the ARPIL dataset.

Model	Params	IoU	Dice Coeff
UNet	9.2 M	0.688	0.801
Surya (LoRA)	4.1 M	0.768	0.853

Surya achieves higher segmentation quality with fewer parameters, highlighting the benefits of foundation model pretraining and parameter-efficient adaptation.

🖼 Example

—Top Row: Input SDO/HMI data (Date: 2014-02-01, Time: 08:12)
—Middle Row: Surya segmentation output
—Bottom Row: Ground Truth

⚡ Usage

Follow the instructions at Surya/downstream_examples/ar_segmentation

🤝 Acknowledgements

—NASA IMPACT and IBM for developing the Surya foundation model