Learning general-purpose representations that capture spatial, temporal, and contextual signals across Earth.

Embeddings, explained simply

An embedding is a short list of numbers that acts like a fingerprint for something complex (an image, text, or a place). Similar fingerprints mean similar content-enabling fast search, grouping, and prediction.

Key papers and main takeaways

Earth Embeddings: Toward AI-centric Representations of our Planet (IEEE Geoscience and Remote Sensing Magazine — Perspective, 2025)

Earth Embeddings illustration

We argue that Earth Embeddings represent the next abstraction layer for geospatial AI: shared representations of our planet learned from satellite imagery, maps, elevation, climate, and other geospatial data — with the potential to become a fundamental building block of next-generation Earth observation models.

Main contributions:

  • Categorizes Earth embeddings as datasets and models, spanning implicit and explicit representations.
  • Identifies four core functions of Earth embeddings across retrieval, generalization, fusion, and diagnostics.
  • Draws parallels between geospatial and language embeddings, motivating Earth embeddings as a first-class abstraction.
  • Outlines a roadmap and calls to action for the geospatial AI community.

Also available as preprint: EarthArXiv

SatCLIP: Global, General-Purpose Location Embeddings with Satellite Imagery

  • Proposes contrastive pretraining that matches satellite image features with their geographic coordinates to learn a location encoder.
  • Produces general-purpose location embeddings that transfer across many downstream tasks and improve geographic generalization.
  • Shows that geolocalized EO imagery can act as scalable supervision for learning “place representations” without dense labels.

Geographic Location Encoding with Spherical Harmonics and Sinusoidal Representation Networks

  • Highlights why global location encoding is tricky: naïve coordinate embeddings can create spherical artifacts (notably near the poles).
  • Introduces a principled global encoder combining spherical harmonics (sphere-native basis) with sinusoidal representation networks (SIREN).
  • Demonstrates strong performance across benchmarks, motivating INRs/location encoders as a foundation for global Earth representations.

Measuring the Intrinsic Dimension of Earth Representations

  • Studies intrinsic dimension as a label-free lens on “how much information” Earth representations actually use (vs. their ambient vector size).
  • Finds intrinsic dimension is often much smaller than the embedding size and varies with resolution and training modality.
  • Shows intrinsic dimension can correlate with downstream performance and reveal spatial artifacts, supporting diagnostics and model selection.

Where we are heading

We build Earth embeddings to enable:

  • global retrieval (“find places like this”),
  • robust transfer across regions and sensors,
  • multimodal fusion (EO, climate, maps, text),
  • and interpretable representations with diagnostics that help scientific trust and use.

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