TorchGeo embeddings - Revision history

Darkblueb at 21:13, 15 June 2026

2026-06-15T21:13:06Z

2026-06-15T21:12:07Z

Self-Supervised Vision Transformers

2026-06-15T21:10:03Z

Vision-Language Models

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Vision-Language Models

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MMEarth site

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Projects

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Projects

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1. Foundation Models for Earth Observation (EO)

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2. Datasets

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2. Datasets

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 }}
-arXiv:2601.13134v1 [cs.SE] 19 Jan 2026
+[[https://arxiv.org/html/2601.13134v1 arXiv:2601.13134v1]] [cs.SE] 19 Jan 2026
 '''Earth Embeddings as Products: Taxonomy, Ecosystem, and Standardized Access''' is a comprehensive survey that organizes existing geospatial embedding products into a structured taxonomy through a three-layer taxonomy:

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 Transformers pre-trained via self-supervised objectives (e.g., DINO, MAE) are currently the state-of-the-art for extracting rich, transferable embeddings from remote sensing imagery.
-* '''DINO / DINOv2 / DINOv3''' – [[https://github.com/facebookresearch/dino [LINK]]] (Meta/INRIA, 2021–2023)
+* '''DINO / DINOv2 / DINOv3''' – [[https://ai.meta.com/research/dinov3/ [LINK]]] (Meta/INRIA, 2021–2023)
 Vision transformers trained with self-distillation without labels. DINOv2, in particular, produces highly discriminative embeddings that capture fine-grained geospatial features without downstream fine-tuning.
 * '''MAE (Masked Autoencoders)''' – [[https://arxiv.org/abs/2111.06377 [LINK]]] (CVPR 2021)

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 A vision-language model designed to generate global location representations by aligning satellite imagery with geographic text embeddings, originally published in 2023 and widely studied.
-* '''MMEarth''' – [[https://vishalned.github.io/mmearth/ SITE]] [[https://arxiv.org/abs/2405.02771v2 [arXiv]]] (ECCV 2024)
+* '''MMEarth''' – [[https://vishalned.github.io/mmearth/ SITE]] [[https://arxiv.org/abs/2405.02771v2 [arXiv]]] [[https://github.com/vishalned/MMEarth-data Datasets]] (ECCV 2024)
 A global dataset for multi-modal and geospatial representation learning. It consists of 12 modalities including pixel-level and image-level modalities from 1.2 million locations.

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 A vision-language model designed to generate global location representations by aligning satellite imagery with geographic text embeddings, originally published in 2023 and widely studied.
-* '''MMEarth''' – [[https://vishalned.github.io/mmearth/ SITE]] [[https://arxiv.org/abs/2405.02771v2 [ArXiV]]] (ECCV 2024)
+* '''MMEarth''' – [[https://vishalned.github.io/mmearth/ SITE]] [[https://arxiv.org/abs/2405.02771v2 [arXiv]]] (ECCV 2024)
 A global dataset for multi-modal and geospatial representation learning. It consists of 12 modalities including pixel-level and image-level modalities from 1.2 million locations.

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 A vision-language model designed to generate global location representations by aligning satellite imagery with geographic text embeddings, originally published in 2023 and widely studied.
-* '''MMEarth''' – [[https://arxiv.org/abs/2405.02771v2 [LINK]]] (ECCV 2024)
+* '''MMEarth''' – [[https://vishalned.github.io/mmearth/ SITE]] [[https://arxiv.org/abs/2405.02771v2 [ArXiV]]] (ECCV 2024)
 A global dataset for multi-modal and geospatial representation learning. It consists of 12 modalities including pixel-level and image-level modalities from 1.2 million locations.

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 === Projects ===
 * '''OlmoEarth''' – [[https://allenai.org/olmoearth AllenAI]] (2025)
 *: Latent image modeling approach for multimodal Earth observation.
 * '''Clay Foundation Model''' – [[https://huggingface.co/made-with-clay/Clay HuggingFace]] (2024)
 *: A multimodal foundation model for Earth using diverse data sources.
 * '''Earth Index Embeddings''' – [[https://www.earthgenome.org/earth-index EarthGenome]] (2025)
 *: A large-scale embedding system built from Earth observation data.

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 === Projects ===
 * '''Clay Foundation Model''' – [[https://huggingface.co/made-with-clay/Clay HuggingFace]] (2024)
 *: A multimodal foundation model for Earth using diverse data sources.
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 * '''Google Satellite Embedding (AlphaEarth)''' – [[https://developers.google.com/earth-engine/datasets/catalog/GOOGLE_SATELLITE_EMBEDDING_V1_ANNUAL LINK]] Google Earth Engine
 *: An early-stage embedding model using Google's global satellite data.
 === Key Papers ===

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 *: A multimodal foundation model for Earth using diverse data sources.
 * '''Major TOM''' – [[https://huggingface.co/Major-TOM MajorTOM]] AFrancis IGARSS 2024
-*: Expandable datasets and models for global EO coverage.
+*:  (Terrestrial Observation Metaset) is an open-source framework and massive satellite dataset created by the European Space Agency's Φ-lab.
 * '''Earth Index Embeddings''' – [[https://www.earthgenome.org/earth-index EarthGenome]] (2025)
 *: A large-scale embedding system built from Earth observation data.

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 == 2. Datasets ==
-Large-scale, open-access datasets play a central role in training and evaluating Earth foundation models.  See the list below for a representative sample.
+Large-scale, open-access datasets play a central role in training and evaluating Earth foundation models.  See the [[TorchGeo_embeddings#Deatasets_Auto-Edit|list]] below for a representative sample.
 == 3. Model Architectures for Embeddings ==