EarthEmbeddings

Satellite imagery embeddings dataset for the EarthEmbeddingExplorer, enabling natural language and location-based search of Earth observation data.

Overview

This repository contains pre-computed embeddings of satellite imagery using state-of-the-art vision-language models. These embeddings power the EarthEmbeddingExplorer application, which allows users to search for satellite images using text queries, image uploads, or geographic locations.

Key features:

• Global satellite imagery from Sentinel-2 (MajorTOM Core-S2L2A)
• Multiple embedding models optimized for Earth observation
• Fast similarity search without raw image preprocessing
• Ready-to-use Parquet format for efficient data access

Dataset Description

Data Source

• Base dataset: MajorTOM Core-S2L2A (Sentinel-2 Level 2A, 2.2M+ samples)
• Processing: Center crop (384×384 pixels) + uniform global sampling

Embedding Models

Embedding Models

Four state-of-the-art vision models are used:

Model	Description	Training Data
SigLIP	General-purpose vision-language model	Web-scale natural image-text pairs
DINOv2	Self-supervised vision transformer	Web-scale natural images (self-supervised)
FarSLIP	Fine-grained satellite imagery model	Satellite image-text pairs
SatCLIP	Location-based satellite model	Satellite image-location pairs

Dataset Splits

1. uniform_sample_250k ⚠️ Preview

├── uniform_sample_250k
│   ├── dinov2
│   │   ├── DINOv2_grid_sample_center_224x224_249k_MajorTOM.parquet
│   │   └── DINOv2_grid_sample_center_384x384_244k.parquet
│   ├── farslip
│   │   └── FarSLIP_grid_sample_center_384x384_244k.parquet
│   ├── satclip
│   │   └── SatCLIP_grid_sample_center_384x384_244k.parquet
│   └── siglip
│       └── SigLIP_grid_sample_center_384x384_244k.parquet

• ~250,000 globally distributed satellite images
• Current status: Preview revision with ~244k pre-computed embeddings and ~249k embeddings sampled from Major-TOM/Core-S2RGB-DINOv2 available
• Note: About 4-6k original image chips were lost due to network error; full version coming soon
• Crop size: For the 1/9 sampled grids, we crop the central bbox in each grid. To ensure the image patches are the same for each model, we chose crop size of 384x384, for pre-computed embeddings, we chose the crop size at 384x384. So these embeddings could represent the same regions on Earth surface.

Filename	Embedding Model	Crop Size	Model Input Size	Embedding Dim	Source
DINOv2_grid_sample_center_224x224_249k_MajorTOM.parquet	DINOv2-large	224×224	224×224	1024	Major-TOM/Core-S2RGB-DINOv2
DINOv2_grid_sample_center_384x384_244k.parquet	DINOv2-large	384×384	224×224	1024	Pre-computed
FarSLIP_grid_sample_center_384x384_244k.parquet	FarSLIP-ViT-B-16	384×384	224×224	512	Pre-computed
SatCLIP_grid_sample_center_384x384_244k.parquet	SatCLIP-ViT16-L40	384×384	224×224	256	Pre-computed
SigLIP_grid_sample_center_384x384_244k.parquet	SigLIP-SO400M-14	384×384	384×384	1152	Pre-computed

2. uniform_sample_22k

• 22,000 globally distributed satellite images
• Files: grid_sample_center_22k_{FarSLIP,SatCLIP,SigLIP}_384x384.parquet

3. Zhejiang_samples

• 2,000 samples from Zhejiang region, China
• Files: zhejiang_sample_center_2k_{FarSLIP,SatCLIP,SigLIP}_384x384.parquet
• Regional case study dataset

Data Format

All embeddings are stored in Parquet format:

• Efficient columnar storage for fast download
• 384×384 pixel satellite image crops

Related Work

• Tutorial: EarthEmbeddingExplorer Tutorial
• Application: EarthEmbeddingExplorer Space
• Base Dataset: MajorTOM by ESA

License

CC-BY-SA-4.0