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.
Title resolution pending
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2representative citing papers
citing papers explorer
-
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.
- Urban Flood Observations: A hand-labeled training and validation dataset of post-flood inundation