pKANrtm uses a physics-aware multi-fidelity KAN to emulate high-fidelity radiative transfer coefficients for atmospheric correction with superior accuracy and large speedups over direct libRadtran runs.
The harmonized Landsat and Sentinel-2 surface reflectance data set.Remote Sensing of Environment2018,219, 145–161
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EO-Gym supplies an executable multimodal environment and 9k-trajectory benchmark that turns Earth Observation into a tool-using, multi-step reasoning task, revealing that current VLMs struggle on temporal and cross-sensor workflows while fine-tuning lifts Pass@3 from 0.49 to 0.74.
Prithvi-EO-2.0 shows environment-dependent flood detection limits, with highest accuracy in cropland (IoU 52%) and riverine events (F1 0.69) and near-zero performance in tree cover and built-up areas across 19 global events.
Year-wise cross-validation across ten ML algorithms on Harmonized Landsat-Sentinel imagery shows SVMs achieve mean F1 of 0.74 for almonds in California and 0.59 for corn in Iowa by early June in unseen validation years.
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