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Prithvi-EO-2.0-tiny-TL

Hugging Face model ibm-nasa-geospatial/Prithvi-EO-2.0-tiny-TL

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What's in it

Data offered docs-verified
multi-temporal HLS image reconstruction (inference.py) · pretrained 5M-parameter ViT backbone with temporal and location embeddings, for finetuning via TerraTorch
Coverage llm-inferred
Global
Resolution docs-verified
30 m (NASA HLS V2 pretraining imagery)
Formats docs-verified
GeoTIFF
Access docs-verified
Free and open; Apache-2.0 licence; no API key stated in DOCS (Hugging Face download)

Useful for

Use cases llm-inferred
Landslide segmentation screening from multi-temporal HLS imagery, following the docs example notebook · Habitat or land-cover change baseline by finetuning a segmentation head with TerraTorch · Reconstructing cloud-gapped HLS time series as clean input for ecological or hydrological baseline mapping
Audience llm-inferred
GIS analysts · remote sensing specialists · geotechnical engineers · ecologists · hydrologists

Limitations

Constraints llm-inferred
Tiny variant performs lower than the larger Prithvi-EO-2.0 models on benchmarks · Inputs must be six reflectance bands in the stated order (Blue, Green, Red, Narrow NIR, SWIR, SWIR 2) · DOCS list band names inconsistently between pretraining (SWIR) and inference (SWIR 1) · Inference expects co-located images at different timestamps, supplied in chronological order · Finetuning requires terratorch>=1.1 · DOCS do not state the geographic coverage of the pretraining samples · Not deployed by any Hugging Face Inference Provider; no hosted API
Licence
apache-2.0
Authentication
No API key required (repository README, auto-extracted; check upstream requirements)
GitHub stars
Unknown
Last commit
Unknown
Last successful page check
14/10/2026
Page health
healthy
Package
Not recorded
Verification
Not independently verified

Provenance & evidence

Verified 2026-10-15 via HF API: exists; 1366 downloads/30d, 11 likes.

Metadata observed 14/10/2026 · research sweep: manual-2026-10-15 (Hugging Face models)