Supported Models (Overview)¶

This page is the model selection entry point. The library has 22 canonical adapters: 16 image encoders, 3 location encoders, and 3 precomputed products. The manuscript's frozen benchmark has 20 systems; GeoCLIP and Esri GLE are software extensions, not new benchmark results. See validation for actual test status and environment requirements. Use it to answer one question quickly: which model IDs should I shortlist for this task?

Once you have a shortlist, use Advanced Model Reference for side-by-side preprocessing and temporal details, then open the linked detail page for the exact contract, caveats, and examples.

How To Read This Page¶

Start with the quick chooser, then scan the catalog table for input and temporal fit, and 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)
Location prior without imagery	`satclip`	Coordinates only; class B location encoder

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; product-native fixed CRS	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 on fixed EPSG:4326 grid	detail

Location Encoders¶

Model ID	Type	Primary Input	Default Resolution	Outputs	Temporal mode	Notes	Detail
`satclip`	Location	Longitude/latitude only (no imagery)	set by harmonic degree (L=40)	`pooled`, `grid`	ignored	Class B; coordinate prior, constant over time	detail
`geoclip`	Location	Longitude/latitude only	learned location prior	`pooled`, `grid`	ignored	512 dimensions; optional GeoCLIP runtime	detail
`esri_gle`	Location	Longitude/latitude only	learned location prior	`pooled`, `grid`	ignored	256 dimensions; direct PyTorch; ~3.7 MB official download	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 compositing window rather than a single-scene selector, and OutputSpec.grid() may be a token or patch grid rather than a georeferenced raster, especially for ViT-like backbones. Cross-model comparisons are usually easiest with OutputSpec.pooled() plus fixed ROI, temporal, and compositing settings.

Precomputed products can also keep their own product-native projection instead of the common provider-backed EPSG:3857 sampling grid. Today that matters especially for tessera and copernicus, so check each detail page before comparing grid outputs directly against on-the-fly models.

On this page, "Default Resolution" means the default source-side fetch resolution, not the final resized tensor shape sent into the backbone. Multi-frame models such as agrifm, anysat, and galileo also need extra attention to frame count and temporal side inputs.

Read the details in Supported Models (Advanced Reference).

More Detail¶

For cross-model preprocessing, temporal packaging, and environment knobs, continue to Advanced Model Reference. If you are adding a new adapter, use Extending to keep the implementation and documentation consistent.