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Supported Models (Overview)

This page is the model selection entry point. Use it to answer one question quickly:

  • Which model IDs should I shortlist for this task?

After you have a shortlist:

  • use Advanced Model Reference for side-by-side preprocessing and temporal details
  • open the linked detail page for the exact contract, caveats, and examples

How To Read This Page

  1. Pick a shortlist from the quick chooser
  2. Scan the catalog table for input and temporal fit
  3. Open the detail page before benchmarking or production use

Canonical model IDs use the short public names shown on this page, such as remoteclip, prithvi, terrafm, and thor. Some detail-page filenames still use older names for compatibility, but the canonical IDs above are the names users should copy into code.

Quick Chooser by Goal

Goal Good starting models Why
Fast baseline / simple pipeline tessera, gse, copernicus Precomputed embeddings, fewer runtime dependencies
Simple S2 RGB on-the-fly experiments remoteclip, satmae, satmaepp, scalemae Straightforward RGB input paths
Time-series temporal modeling agrifm, anysat, galileo Native multi-frame temporal packaging
Multispectral / strict spectral semantics satmaepp_s2_10b, dofa, terramind, thor, satvision Strong channel/schema assumptions
Mixed-modality experiments (S1/S2) terrafm Supports S2 or S1 path (per call)

Model Catalog Snapshot

Precomputed Embeddings

Model ID Type Primary Input / Source Default Resolution Outputs Temporal mode Notes Detail
tessera Precomputed GeoTessera embedding tiles 10m pooled, grid yearly coverage product Fast baseline, source-fixed precomputed workflow detail
gse Precomputed Google Satellite Embedding (annual) 10m pooled, grid TemporalSpec.year(...) Annual product via provider path detail
copernicus Precomputed Copernicus embeddings 0.25° pooled, grid limited (2021) Coarse resolution product detail

On-the-fly Foundation Models

Model ID Primary Input Default Resolution Temporal style Outputs Notable requirements Detail
remoteclip S2 RGB (B4,B3,B2) 10m single composite window pooled, grid provider backend; RGB preprocessing detail
satmae S2 RGB (B4,B3,B2) 10m single composite window pooled, grid RGB path; ViT token/grid behavior detail
satmaepp S2 RGB (B4,B3,B2) 10m single composite window pooled, grid SatMAE++ fMoW-style eval preprocessing; channel order control detail
satmaepp_s2_10b S2 SR 10-band (B2,B3,B4,B5,B6,B7,B8,B8A,B11,B12) 10m single composite window pooled, grid strict 10-band order; grouped-channel token handling detail
scalemae S2 RGB + scale 10m single composite window pooled, grid requires sensor.scale_m / input_res_m detail
wildsat S2 RGB 10m single composite window pooled, grid normalization options detail
prithvi S2 6-band 30m single composite window pooled, grid required temporal + location side inputs detail
terrafm S2 12-band or S1 VV/VH 10m single composite window pooled, grid choose modality per call detail
terramind S2 SR 12-band 10m single composite window pooled, grid strict normalization/channel semantics detail
dofa Multispectral + wavelengths 10m single composite window pooled, grid wavelength vector required/inferred detail
fomo S2 12-band 10m single composite window pooled, grid normalization mode choices detail
thor S2 SR 10-band 10m single composite window pooled, grid strict stats-based normalization detail
satvision TOA 14-channel 1000m single composite window pooled, grid strict channel order + calibration detail
anysat S2 10-band time series 10m multi-frame pooled, grid frame dates (s2_dates) side input detail
galileo S2 10-band time series 10m multi-frame pooled, grid month tokens + grouped tensors detail
agrifm S2 10-band time series 10m multi-frame pooled, grid fixed T frame stack behavior detail

Temporal and Comparison Notes (What People Usually Miss)

  • TemporalSpec.range(start, end) is usually a window for compositing, not a single-scene acquisition selector.
  • OutputSpec.grid() may be a token/patch grid, not a georeferenced raster grid (especially for ViT-like backbones).
  • Cross-model comparisons are easiest with OutputSpec.pooled() and fixed ROI/temporal/compositing settings.
  • "Default Resolution" on this page means the default source/provider fetch resolution, not the final resized tensor shape fed into the backbone.
  • Multi-frame models (agrifm, anysat, galileo) need extra attention to frame count and temporal side inputs.

Read the details in Supported Models (Advanced Reference).

More Detail