Add dataset files

README.md

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 ---
 license: mit
+task_categories:
+  - feature-extraction
+  - image-text-matching
+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
+
+Three CLIP-like models are used:
+
+| Model | Description | Training Data |
+| :--- | :--- | :--- |
+| **SigLIP** | General-purpose vision-language model | Web-scale natural image-text pairs |
+| **FarSLIP** | Fine-grained satellite imagery model | Satellite image-text pairs |
+| **SatCLIP** | Location-based satellite model | Satellite image-location pairs |
+
+## Dataset Splits
+
+### 1. `uniform_sample_22k`
+- **22,000** globally distributed satellite images
+- **Files**: `grid_sample_center_22k_{FarSLIP,SatCLIP,SigLIP}_384x384.parquet`
+
+### 2. `uniform_sample_250k` ⚠️ Preview
+- **~250,000** globally distributed satellite images
+- **Current status**: Preview revision with ~243k samples available
+- **Note**: About 7k original image chips were lost due to network error; full version coming soon
+- **Structure**: Organized by model (`farslip/`, `satclip/`, `siglip/`)
+- **Files**: `{FarSLIP,SatCLIP,SigLIP}_grid_sample_center_384x384_243k.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
+
+MIT License

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