README.md

---
license: cc-by-sa-4.0
task_categories:
  - text-to-image
  - image-to-image
  - other
language:
  - en
tags:
  - satellite-imagery
  - earth-observation
  - embeddings
  - geospatial
  - clip
  - majortom
size_categories:
  - 10K<n<100K
  - 100K<n<1M
---

<div style="display: flex; gap: 0.2em; align-items: center; justify-content: center;">
    <a href="https://www.modelscope.cn/studios/VoyagerX/EarthExplorer"><img src="https://img.shields.io/badge/Open in ModelScope.cn-xGPU-624aff"></a>
    <a href="https://www.modelscope.ai/studios/VoyagerX/EarthExplorer"><img src="https://img.shields.io/badge/Open in ModelScope.ai-CPU-624aff"></a>
    <a href="https://huggingface.co/spaces/ML4Sustain/EarthExplorer"><img src="https://img.shields.io/badge/Open in HF Space-CPU-FFD21E"></a>
    <a href="https://modelscope.cn/studios/VoyagerX/EarthExplorer/file/view/master/Tutorial.md?status=1"> <img src="https://img.shields.io/badge/Tutorial-📖-007bff"> </a>
    <a href="https://www.modelscope.cn/learn/3958"> <img src="https://img.shields.io/badge/中文教程-📖-007bff"> </a>
</div>

# 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](https://huggingface.co/spaces/ML4Sustain/EarthExplorer) 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](https://huggingface.co/datasets/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](https://huggingface.co/facebook/dinov2-large) | 224×224 | 224×224 | 1024 | [Major-TOM/Core-S2RGB-DINOv2](https://huggingface.co/datasets/Major-TOM/Core-S2RGB-DINOv2) |
| `DINOv2_grid_sample_center_384x384_244k.parquet` | [DINOv2-large](https://huggingface.co/facebook/dinov2-large) | 384×384 | 224×224 | 1024 | Pre-computed |
| `FarSLIP_grid_sample_center_384x384_244k.parquet` | [FarSLIP-ViT-B-16](https://huggingface.co/ZhenShiL/FarSLIP) | 384×384 | 224×224 | 512 | Pre-computed |
| `SatCLIP_grid_sample_center_384x384_244k.parquet` | [SatCLIP-ViT16-L40](https://github.com/microsoft/satclip) | 384×384 | 224×224 | 256 | Pre-computed |
| `SigLIP_grid_sample_center_384x384_244k.parquet` | [SigLIP-SO400M-14](https://huggingface.co/timm/ViT-SO400M-14-SigLIP-384) | 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](https://huggingface.co/spaces/ML4Sustain/EarthExplorer/blob/main/Tutorial.md)
- **Application**: [EarthEmbeddingExplorer Space](https://huggingface.co/spaces/ML4Sustain/EarthExplorer)
- **Base Dataset**: [MajorTOM by ESA](https://github.com/ESA-PhiLab/MajorTOM)

## License

CC-BY-SA-4.0