CropNet, a lightweight CNN jointly convolving spectral and temporal dimensions, learns invariant crop signatures from multispectral time series and outperforms larger models under geographic domain shifts on the new CropGlobe benchmark spanning eight countries.
Esa worldcover 10 m 2021 v200
3 Pith papers cite this work. Polarity classification is still indexing.
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FireDataForge automates retrieval and harmonization of 11 multi-source wildfire geospatial datasets into common-grid NumPy arrays for a given MTBS Event ID.
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.
citing papers explorer
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FireDataForge: A Unified Framework for Multi-Source Wildfire Data Retrieval and Integration
FireDataForge automates retrieval and harmonization of 11 multi-source wildfire geospatial datasets into common-grid NumPy arrays for a given MTBS Event ID.
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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.