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One line of code to get embeddings from any Remote Sensing Foundation Model (RSFM) for any location and any time

Motivation

rs-embed background

The remote sensing community has seen an explosion of foundation models in recent years. Yet, using them in practice remains surprisingly painful:

  • Inconsistent model interfaces: imagery models vs. precomputed embedding products
  • Ambiguous input semantics: patch vs. tile vs. grid vs. pooled output
  • Large differences in temporal, spectral, and spatial assumptions
  • No clean way to benchmark multiple models under one API

RS-Embed aims to fix this.

Goal

Provide a minimal, unified, and stable API that turns diverse RS foundation models into a simple ROI → embedding service — so researchers can focus on downstream tasks, benchmarking, and analysis, not glue code.

Start Here

Get something running

  • Quickstart: install the package, run a first example, and learn the three core APIs

Choose a model

  • Models: shortlist model IDs by task, input type, and temporal behavior

Exact signatures

  • API: exact signatures for specs, embedding, export, and inspection

Support for a new model

  • Extending: add a new model adapter or integrate with the registry/export flow

Why rs-embed

  • Support embedding acquisition for any place and time, flexibly assist your downstream tasks

  • Supports simple usage, as well as highly customizable features

  • Scale from single regions to massive datasets built around three functions:

    • get_embedding(...)
    • get_embeddings_batch(...)
    • export_batch(...)

  • Detailed documentation support

Common Tasks

Goal Page Main API
Get one embedding for one ROI Quickstart get_embedding(...)
Compute embeddings for many ROIs Quickstart get_embeddings_batch(...)
Build an export dataset Quickstart export_batch(...)
Compare model assumptions Models model tables + detail pages
Inspect a raw provider patch Inspect API inspect_provider_patch(...)

Advanced Reading

  • Concepts: deeper semantics for TemporalSpec, OutputSpec, and backends
  • Workflows: extra task-oriented recipes beyond the quickstart path
  • Advanced Model Reference: detailed preprocessing and temporal comparison tables
  • Limitations: current constraints and edge cases