GSST/CBERS-5 International Symposium on Machine Learning in Heliophysics and Space Weather

CMEAT: A Multimodal Dataset for Predicting CME Earth Impact and Transit Time

Aug 17, 2026, 3:00 PM

20m

Fernando de Mendonça - LIT (National Institute for Space Research, São José dos Campos, SP, Brazil)

Oral Space Weather Forecasting & Operations Oral Contributions

Speaker

Besma Guesmi (Ubotica Technologies)

Description

Geoeffective coronal mass ejections (CMEs) can disrupt satellites, power grids, and navigation systems, making accurate early warning critical for space weather operations. We present CMEAT, a curated multimodal dataset and fusion framework for predicting CME Earth impact and Sun–Earth transit time. CMEAT pairs CDAW LASCO observations (1996-2025) with ICME arrival labels and upstream L1 context, producing several thousand events, including hundreds of verified impacts. We extract a compact physical feature set and texture/geometric descriptors from LASCO C2/C3 running-difference imagery, and evaluate classical ML baselines alongside fusion strategies. On temporally held-out test sets, our fusion approaches achieve the best F1 score of 88.4% for impact classification and a mean absolute error of 19.04 hours for transit-time prediction, outperforming single-modality baselines.