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.
Multisource and Multitemporal Data Fusion in Remote Sensing: A Comprehensive Review of the State of the Art,
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
Affinity-propagation clustering of Arctic VHSR imagery enables MAE pretraining of a ViT-Large encoder that outperforms ImageNet and Prithvi-EO-2.0 baselines by 5-15 percentage points in mean F1 on four downstream Arctic detection and segmentation tasks.
citing papers explorer
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Land cover and flood type govern the detection limits of satellite-based flood mapping across diverse global flood events
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.