Supported Models (Advanced Reference)

This page preserves the detailed model comparison matrices and preprocessing notes.

If you are choosing a model for the first time, start with:

• Supported Models (Overview)

If you are authoring a new per-model doc page, use:

• Model Detail Template

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This page is best used after you already narrowed down candidate models and want to compare:

1. preprocessing assumptions
2. temporal packaging
3. side-input requirements
4. environment-variable tuning knobs

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How To Use This Page

Quick chooser by goal

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

Readability tips

• Start with Quick Comparison if you are deciding between models
• Read Temporal Handling and Multi-frame Semantics before comparing temporal models
• Read Modality and Extra Inputs Matrix if you need fair cross-model benchmarking
• Read Environment Variables... only when tuning preprocessing or reproducing training pipelines

Precomputed Embeddings

Model	ID	Output	Resolution	Dim	Time Coverage	Notes
Tessera	tessera	pooled / grid	10m	128	2017–2025	GeoTessera global tile embeddings
Google Satellite Embedding (Alpha Earth)	gse	pooled / grid	10 m	64	2017–2024	Annual embeddings via GEE
Copernicus Embed	copernicus	pooled / grid	0.25°	768	2021	Official Copernicus embeddings

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On-the-fly Foundation Models

Source of truth:

• src/rs_embed/embedders/catalog.py
• src/rs_embed/embedders/onthefly_*.py
• src/rs_embed/embedders/_vit_mae_utils.py
• src/rs_embed/embedders/runtime_utils.py

Registered on-the-fly IDs:

• remoteclip
• satmae
• scalemae
• anysat
• dynamicvis
• galileo
• wildsat
• prithvi
• terrafm
• terramind
• dofa
• fomo
• thor
• agrifm
• satvision

Quick Comparison

Model ID	Architecture / Backbone	Input	Default Preprocessing	Resize / Crop / Pad	Output Structure	Training Alignment
remoteclip	rshf.remoteclip.RemoteCLIP (open_clip style CLIP ViT)	S2 RGB (B4,B3,B2)	raw SR 0..10000 -> /10000 -> RGB uint8; then model transform if available, else CLIP norm	image size 224; fallback path uses Resize + CenterCrop; no pad	pooled vector or ViT token grid	Medium (high if wrapper transform matches training; fallback is generic CLIP pipeline)
satmae	rshf.satmae.SatMAE	S2 RGB (B4,B3,B2)	raw SR -> /10000 -> RGB uint8; prefer model transform, else CLIP norm	default 224; CLIP fallback has Resize + CenterCrop; no pad	token sequence -> pooled or patch-token grid	Medium
scalemae	rshf.scalemae.ScaleMAE (ViT style)	S2 RGB (B4,B3,B2) + input_res_m	raw SR -> /10000 -> RGB uint8; CLIP norm tensor; pass input_res_m	default 224; CLIP path has Resize + CenterCrop; no pad	token sequence or pooled vector depending on wrapper output	Medium
anysat	AnySat from upstream hubconf.py (AnySat)	S2 10-band TCHW (or CHW auto-expanded)	clip to 0..10000; normalize mode default per_tile_zscore; builds per-frame s2_dates	resize TCHW to default 24; no crop, no pad	patch output [D,H,W], pooled by spatial mean/max	Medium
dynamicvis	DynamicVisBackbone (official repo)	S2 RGB (B4,B3,B2)	raw SR -> /10000 -> RGB uint8 -> ImageNet mean/std	default 512; resize to square; no pad	last feature map [D,H,W], pooled by spatial mean/max	Medium
galileo	Encoder from official single_file_galileo.py	S2 10-band TCHW (or CHW auto-expanded)	clip to 0..10000; normalize mode default unit_scale; constructs Galileo tensors with configurable T + per-frame months, optional NDVI channel	default 64 with patch 8; bilinear resize; no pad	pooled token vector and S2-group token grid	Medium
wildsat	WildSAT backbone + optional image head from checkpoint	S2 RGB CHW	clip to 0..10000 then /10000; default normalization minmax; convert to uint8 then unit tensor	default 224; resize RGB; no pad	pooled branch output and optional grid (token or feature path)	Medium-Low
prithvi	TerraTorch BACKBONE_REGISTRY Prithvi backbone	S2 6-band (BLUE,GREEN,RED,NIR_NARROW,SWIR_1,SWIR_2)	raw SR -> /10000 -> clamp [0,1]; prep mode from env	default mode resize to 224; optional pad to patch multiple (legacy)	token sequence -> pooled or patch-token grid	Medium
terrafm	TerraFM-B from HF code/weights	S2 12-band or S1 VV/VH	S2: /10000 to [0,1]; S1: log1p + p99 scaling to [0,1]	resize to 224; no pad	pooled embedding, optional feature-map grid	Medium
terramind	TerraTorch BACKBONE_REGISTRY TerraMind backbone	S2 SR 12-band	raw 0..10000; resize 224; z-score with TerraMind v1/v01 pretrained mean/std	fixed 224; no pad	token sequence -> pooled or patch-token grid	High
dofa	TorchGeo DOFA (dofa_base_patch16_224 / dofa_large_patch16_224)	multi-band SR CHW + wavelengths	raw SR -> /10000 to [0,1]; provide/infer wavelengths	bilinear resize to 224; explicitly no crop/pad	pooled vector or token grid (usually 14x14)	Medium-High
fomo	FoMo MultiSpectralViT (FoMo-Bench)	S2 SR 12-band	clip 0..10000; default unit_scale (optional minmax/none)	default 64; bilinear resize; no pad	token sequence pooled; grid as spectral-mean patch-token map	Medium
thor	THOR via TerraTorch + thor_terratorch_ext	S2 SR 10-band	clip 0..10000; default thor_stats z-score after reflectance scaling	default 288; bilinear resize; no pad	pooled tokens and grouped token grid	Medium-High
agrifm	AgriFM PretrainingSwinTransformer3DEncoder	S2 10-band time series [T,C,H,W]	clip 0..10000; default agrifm_stats z-score using official config stats	default 224; TCHW resize; no pad	feature map grid [D,H,W], pooled by spatial mean/max	High
satvision	timm SwinTransformerV2 (SatVision-TOA checkpoints)	TOA 14 channels in strict order	channel-aware normalization to [0,1] (auto/raw/unit, reflectance + emissive calibration)	default 128; bilinear resize; no pad	model output as pooled or grid depending on tensor shape	High (if band order and calibration match checkpoint)

Temporal Handling

• For most on-the-fly adapters, TemporalSpec.range(start, end) means: filter imagery in [start, end), then build one composite patch for model input (median by default, or mosaic if configured via SensorSpec.composite).
• In these adapters, meta.input_time is typically the midpoint of the temporal window and is mainly metadata (or an auxiliary time signal for models that require it), not a guaranteed single-scene acquisition date.
• Multi-frame adapters: agrifm, anysat, and galileo fetch TCHW sequences by splitting the requested range into sub-windows and compositing each sub-window into one frame.
• Current single-composite adapters include: remoteclip, satmae, scalemae, dynamicvis, wildsat, prithvi, terrafm, terramind, dofa, fomo, thor, and satvision.

Multi-frame Semantics

Shared behavior for current multi-frame adapters (agrifm, anysat, galileo):

• Frame construction: split TemporalSpec.range(start, end) into T equal sub-windows (end-exclusive), then composite each sub-window into one frame.
• Missing-observation fallback: if a sub-window has no valid image, provider path reuses a fallback composite so frame count remains stable.
• Fixed frame count: runtime always ensures exact T frames for model input. For user-provided input_chw, CHW is repeated to T, and TCHW is padded/truncated to T.
• Sensor compositing policy: frame composite mode follows SensorSpec.composite (median default, mosaic optional).

Per-model temporal packaging:

Model ID	Frame count env (default)	Temporal side input	Notes
agrifm	RS_EMBED_AGRIFM_FRAMES (8)	none (uses TCHW directly)	Temporal information is encoded only in the frame stack.
anysat	RS_EMBED_ANYSAT_FRAMES (8)	s2_dates (per-frame DOY, 0..364)	DOY values are derived from each frame bin midpoint date.
galileo	RS_EMBED_GALILEO_FRAMES (8)	months (per-frame month, 1..12)	By default from frame bin midpoints; RS_EMBED_GALILEO_MONTH can force a constant month for all frames.

Modality and Extra Inputs Matrix

Interpretation:

• "Backbone multimodal" means the upstream foundation model family supports multiple modalities.
• "Current rs-embed path" means what this implementation currently feeds in practice.
• "Requires extra metadata" means additional non-image inputs required by the forward path (hard requirement).

Model ID	Backbone multimodal?	Current rs-embed path uses multiple modalities?	Multi-input forward (beyond image tensor)?	Requires extra metadata?
remoteclip	No	No	No	No
satmae	No	No	No	No
scalemae	No	No	Yes (input_res_m)	Yes: scale/resolution (sensor.scale_m)
anysat	Yes	Partially (S2-only imagery, plus temporal date tokens)	Yes (s2, s2_dates)	Yes: day-of-year/date signal (derived from temporal range)
dynamicvis	No	No	No	No
galileo	Yes	Mostly S2 path in current adapter + temporal month tokens	Yes (multiple tensors + masks + months)	Yes: month/time signal (derived from temporal range)
wildsat	No	No	No	No
prithvi	No (this adapter path)	No	Yes (x, temporal_coords, location_coords)	Yes: location + time are required
terrafm	Yes (S1/S2)	Yes (select one modality per call: s1 or s2)	No	No hard extra metadata (optional S1 options: orbit, linear/DB path)
terramind	Yes	Usually single selected modality (S2L2A default)	No (single selected modality tensor in this adapter)	No hard extra metadata
dofa	Yes (spectral generalization)	Yes (multi-band spectral input)	Yes (image + wavelength list)	Yes: per-band wavelengths (explicit or inferable from bands)
fomo	No	No	No	No
thor	No (this adapter path)	No	No	No
agrifm	No (this adapter path)	No	No extra side tensor, but temporal stack [T,C,H,W] required	Temporal coverage is important (no separate metadata tensor)
satvision	No (this adapter path)	No	No separate side tensor	Yes: strict 14-channel order/calibration schema (band semantics)

Practically multi-input models:

• prithvi: image + temporal coords + location coords
• anysat: image/time-series + date tokens (s2_dates)
• galileo: image-derived tensors + masks + per-frame month tokens (months)
• dofa: image + wavelength vector
• scalemae: image + input_res_m

Environment Variables That Directly Change Preprocessing/Temporal Packaging

Model ID	Main preprocessing env keys
remoteclip	fixed image_size=224 in code path; no per-model preprocess env switch
satmae	RS_EMBED_SATMAE_IMG
scalemae	RS_EMBED_SCALEMAE_IMG
anysat	RS_EMBED_ANYSAT_IMG, RS_EMBED_ANYSAT_NORM, RS_EMBED_ANYSAT_FRAMES
dynamicvis	RS_EMBED_DYNAMICVIS_IMG
galileo	RS_EMBED_GALILEO_IMG, RS_EMBED_GALILEO_PATCH, RS_EMBED_GALILEO_NORM, RS_EMBED_GALILEO_INCLUDE_NDVI, RS_EMBED_GALILEO_FRAMES, RS_EMBED_GALILEO_MONTH
wildsat	RS_EMBED_WILDSAT_IMG, RS_EMBED_WILDSAT_NORM
prithvi	RS_EMBED_PRITHVI_PREP, RS_EMBED_PRITHVI_IMG, RS_EMBED_PRITHVI_PATCH_MULT
terrafm	modality and sensor-side options (s2/s1); image size fixed to 224 in implementation
terramind	RS_EMBED_TERRAMIND_NORMALIZE (default z-score stats), image size fixed 224
dofa	image size fixed 224; provider/tensor channels and wavelengths drive preprocessing
fomo	RS_EMBED_FOMO_IMG, RS_EMBED_FOMO_NORM
thor	RS_EMBED_THOR_IMG, RS_EMBED_THOR_NORMALIZE
agrifm	RS_EMBED_AGRIFM_IMG, RS_EMBED_AGRIFM_NORM, RS_EMBED_AGRIFM_FRAMES
satvision	RS_EMBED_SATVISION_TOA_IMG, RS_EMBED_SATVISION_TOA_NORM, channel-index and calibration env keys

Practical Guidance

• For highest reproducibility, keep each model's default normalization mode unless you can match the original training pipeline exactly.
• For strict-schema models (satvision, terramind, thor, agrifm), do not change channel order unless checkpoint metadata explicitly allows it.
• If comparing embeddings across models, standardize ROI and temporal compositing first; model preprocessing differences are substantial.