STORM-AI: AI Challenge for Satellite Tracking and Orbit Resilience Modeling

Precise thermospheric density forecasting is critical for mitigating satellite drag in Low Earth Orbit (LEO), but traditional empirical models such as MSIS and JB2008 can fail during geomagnetic storms. To evaluate whether AI can better capture this transient behavior, the 2025 MIT ARCLab Prize for AI Innovation in Space asked participants to forecast orbit-averaged density three days ahead. The training set included 8,118 samples, each with initial orbital parameters, 60 days of contextual space weather indicators, GOES X-ray flux, OMNI2 solar wind observations, and the corresponding three-day density target, plus 4,589 hidden samples reserved for private testing. Submissions were ranked on Codabench using the Orbital Density Root Mean Square Error (OD-RMSE), a custom metric that emphasizes early-horizon performance and is benchmarked against MSIS. The competition attracted 139 teams and 973 submissions. The development kit with the baseline solutions, evaluation scripts, the challenge documentation, and the description of the dataset is provided in STORM-AI Devkit GitHub. More details on the challenge dataset, design, and results can be found in the related publications.