Datasets:

ML4Sustain
/

EarthEmbeddings

@@ -1,116 +1,116 @@
----
-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

+---
+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