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arXiv · 2506.06176

SatelliteFormula: Multi-Modal Symbolic Regression from Remote Sensing Imagery for Physics Discovery

Zhenyu Yu·Mohd. Yamani Idna Idris·Pei Wang·Yuelong Xia·Fei Ma·Rizwan Qureshi
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Abstract

We propose SatelliteFormula, a novel symbolic regression framework that derives physically interpretable expressions directly from multi-spectral remote sensing imagery. Unlike traditional empirical indices or black-box learning models, SatelliteFormula combines a Vision Transformer-based encoder for spatial-spectral feature extraction with physics-guided constraints to ensure consistency and interpretability. Existing symbolic regression methods struggle with the high-dimensional complexity of multi-spectral data; our method addresses this by integrating transformer representations into a symbolic optimizer that balances accuracy and physical plausibility. Extensive experiments on benchmark datasets and remote sensing tasks demonstrate superior performance, stability, and generalization compared to state-of-the-art baselines. SatelliteFormula enables interpretable modeling of complex environmental variables, bridging the gap between data-driven learning and physical understanding.

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Zhenyu Yu, Mohd. Yamani Idna Idris, Pei Wang, Yuelong Xia, Fei Ma, Rizwan Qureshi. 2025-06-06. SatelliteFormula: Multi-Modal Symbolic Regression from Remote Sensing Imagery for Physics Discovery. https://arxiv.org/abs/2506.06176

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