EarthEmbeddingExplorer is an interactive web application for cross-modal retrieval of global satellite imagery. It allows you to search the Earth using natural language, images, or geographic coordinates — no need to download terabytes of data or write a single line of code.
Whether you are an AI researcher exploring vision-language models, a remote-sensing scientist looking for specific land-cover patterns, or simply curious about what satellite imagery can reveal, this tool is designed to be accessible and informative for everyone.
- EarthEmbeddingExplorer
Imagine being able to type "a satellite image of a glacier" or "a city with a coastline" and instantly see matching locations on a world map, together with the actual satellite imagery. That is what EarthEmbeddingExplorer does.
Under the hood, the application encodes your query and ~250,000 satellite images into embedding vectors — compact numerical representations that capture semantic meaning. By measuring vector similarity, the system finds the most relevant images across the globe and visualizes them interactively.
Key features:
- 🔍 Text-to-Image Retrieval — Search satellite imagery with free-form natural language.
- 🖼️ Image-to-Image Retrieval — Upload a photo and find visually similar locations on Earth.
- 📍 Location-to-Image Retrieval — Input GPS coordinates or click on a map to discover what a specific place looks like from space.
- ⚡ Near Real-Time Search — Pre-computed embeddings and on-demand HTTP Range requests make retrieval fast without downloading the full dataset.
- 🌍 Global Coverage — Based on the ESA MajorTOM dataset, covering the Earth's land surface with Sentinel-2 imagery.
We use MajorTOM (Major TOM: Expandable Datasets for Earth Observation), a large-scale dataset released by the European Space Agency (ESA). Specifically, we work with the Core-S2L2A subset, which provides global Sentinel-2 Level-2A multispectral imagery at 10 m ground resolution.
| Dataset | Source | Samples | Sensor |
|---|---|---|---|
| MajorTOM-Core-S2L2A | Sentinel-2 L2A | 2,245,886 | Multispectral (10 m) |
Because the original tiles are large (1068 × 1068 pixels) and the full dataset exceeds 23 TB, we create a lightweight, search-friendly version:
- Center Cropping — From each tile we extract the central 384 × 384 patch, which matches the input size expected by modern vision transformers.
- Uniform Sampling — Using MajorTOM's hierarchical grid coding system, we sample roughly 1% of the data (~250,000 images). This preserves global geographic coverage while keeping the embedding index small enough for interactive search.
Geographic distribution of our sampled satellite-image embeddings.
The retrieval engine is powered by four complementary foundation models. If you are coming from an AI background, think of them as different "encoders" that map images, text, or coordinates into a shared latent space. If you are coming from remote sensing, think of them as feature extractors that turn raw pixels (and optional metadata) into comparable signatures.
The core idea (CLIP-like alignment):
Models such as CLIP [2] learn to align images and text by training on massive pairs of (image, caption) data from the web. An image encoder compresses a photo into a vector; a text encoder does the same for a sentence. The key property is that semantically matching pairs end up close together in vector space, while unrelated pairs are far apart.
How CLIP-like models connect images and text in a shared embedding space.
The four models we use:
| Model | Modality | Training Data | Best For |
|---|---|---|---|
| SigLIP [3] | image + text | Natural image–text pairs (web) | General open-vocabulary text queries |
| FarSLIP [4] | image + text | Satellite image–text pairs (RS-specific) | Fine-grained remote-sensing concepts |
| SatCLIP [5] | image + location | Satellite image–GPS coordinate pairs | Location-aware retrieval |
| DINOv2 [7] | image only | Natural images (self-supervised) | Pure visual similarity search |
- SigLIP improves upon CLIP with a sigmoid loss and works well for everyday vocabulary.
- FarSLIP is fine-tuned on remote-sensing captions, making it better at concepts like "deforestation" or "salt evaporation ponds".
- SatCLIP jointly encodes images and their geographic coordinates, enabling queries like "show me places near (lat, lon)".
- DINOv2 learns powerful visual features without any text supervision; it excels at "find me images that look like this one".
Turning satellite images into embedding vectors for fast similarity search.
Search pipeline:
- We pre-compute embeddings for all ~250,000 sampled satellite images using each of the four models.
- When you submit a query (text, image, or coordinates), the app encodes it with the corresponding model's encoder.
- Cosine similarity scores are computed against the entire image index.
- High-scoring locations are plotted on an interactive map, and the top-5 most similar images are fetched on demand.
EarthEmbeddingExplorer system architecture on ModelScope.
The application is designed for cloud-native deployment on ModelScope:
- Models & embeddings are hosted on ModelScope (or Hugging Face) and downloaded on first use.
- Raw imagery stays in remote Parquet shards. We maintain a lightweight metadata index (
product_id → parquet_url, parquet_row) so the app knows exactly which shard and row contain a given image. - On-demand fetching uses HTTP Range requests to download only the necessary byte ranges (target row groups and RGB columns) from a Parquet file. This avoids downloading the full 23 TB dataset and enables near real-time image display.
- GPU acceleration is provided by xGPU, allowing flexible allocation of GPU resources for encoding queries.
| Dataset | Link | Description |
|---|---|---|
| Core-S2L2A-249k | ModelScope | Sampled subset of MajorTOM Core-S2L2A used in this project |
Each embedding dataset contains the vector representation of every sampled image, together with metadata (grid_cell, parquet_url, parquet_row) needed to retrieve the original pixels on demand.
| Model | Embedding Dataset | Link |
|---|---|---|
| SigLIP | Core-S2RGB-249k-SigLIP | ModelScope |
| FarSLIP | Core-S2RGB-249k-FarSLIP | ModelScope |
| DINOv2 | Core-S2RGB-249k-DINOv2 | ModelScope |
| SatCLIP | Core-S2RGB-249k-SatCLIP | ModelScope |
Note for developers: The
parquet_urlfield stores a direct HuggingFace URL (e.g.,https://huggingface.co/datasets/Major-TOM/Core-S2L2A/resolve/main/images/part_00001.parquet) andparquet_rowstores the global row index, enabling online image download when the app is deployed on ModelScope or Hugging Face Spaces.
# 1. Clone the repository
git clone https://github.com/VoyagerX/EarthEmbeddingExplorer.git
cd EarthEmbeddingExplorer
# 2. Install dependencies
pip install -r requirements.txt
# 3. Launch the app
python app.pyBy default the app will attempt to download models and embeddings from ModelScope (modelscope.cn). You can change the download endpoint via the environment variable:
export DOWNLOAD_ENDPOINT="modelscope.cn" # options: modelscope.cn, modelscope.ai, huggingface (to be supported).
python app.pyYou can host your own instance of EarthEmbeddingExplorer on ModelScope Studio with free GPU access:
-
(Optional) Apply to join the xGPU-Explorers organization on ModelScope.cn (or modelscope.ai) to unlock GPU backend resources.
-
Fork the studio by clicking Duplicate on the project page.
-
Configure resources & environment variables. Select an appropriate GPU instance and set
DOWNLOAD_ENDPOINTto your preferred source (modelscope.cn,modelscope.ai, orhuggingface).
-
Publish your studio.
For detailed instructions, please refer to the official ModelScope Studio documentation.
Type a natural-language description and see matching locations worldwide.
Query: "a satellite image of a glacier"
Upload an image and retrieve geographically diverse locations that share visual similarity.
Query image → similar satellite locations in the Amazon region.
Click on the map or enter GPS coordinates to discover what that place looks like from space.
Location query near the Amazon basin.
- Domain gap in SigLIP: SigLIP is trained primarily on natural images from the web (people, pets, everyday objects). Scientific or geospatial terms that rarely appear in web captions may be poorly understood.
- FarSLIP specificity: Because FarSLIP is specialized for remote-sensing concepts, it can underperform on generic non-RS queries (e.g., "an image of a face").
- Spatial resolution: We use 384 × 384 center crops from 1068 × 1068 Sentinel-2 tiles. Very small objects or fine textures may be lost during cropping and resizing.
- Search latency: Exact brute-force similarity search over 250k vectors is fast on GPU, but may become a bottleneck if the index grows significantly. We plan to add FAISS-based approximate search in the future.
- Support DINOv2 embedding model and embedding datasets.
- Increase geographic coverage to ~1.2% of Earth's land surface (~250k samples).
- Integrate FAISS for faster approximate nearest-neighbor search.
- Support additional embedding models .
- What features do you want? Leave an issue or start a discussion!
We warmly welcome new contributors. See CONTRIBUTING.md for guidelines.
We thank the following open-source projects and datasets that made EarthEmbeddingExplorer possible:
Models:
- SigLIP — Vision Transformer for image-text alignment
- FarSLIP — Fine-grained remote-sensing language-image pretraining
- SatCLIP — Satellite location-image pretraining
- DINOv2 — Self-supervised vision transformer
Datasets:
- MajorTOM — Expandable datasets for Earth observation by ESA
We are grateful to the research communities and organizations that developed and shared these resources.
If you use EarthEmbeddingExplorer in your research, please cite:
@inproceedings{
zheng2026earthembeddingexplorer,
title={EarthEmbeddingExplorer: A Web Application for Cross-Modal Retrieval of Global Satellite Images},
author={Yijie Zheng and Weijie Wu and Bingyue Wu and Long Zhao and Guoqing Li and Mikolaj Czerkawski and Konstantin Klemmer},
booktitle={4th ICLR Workshop on Machine Learning for Remote Sensing (Tutorial Track)},
year={2026},
url={https://openreview.net/forum?id=LSsEenJVqD}
}[1] Francis, A., & Czerkawski, M. (2024). Major TOM: Expandable Datasets for Earth Observation. IGARSS 2024.
[2] Radford, A., et al. (2021). Learning Transferable Visual Models From Natural Language Supervision. ICML 2021.
[3] Zhai, X., et al. (2023). Sigmoid Loss for Language-Image Pre-Training. ICCV 2023.
[4] Li, Z., et al. (2025). FarSLIP: Discovering Effective CLIP Adaptation for Fine-Grained Remote Sensing Understanding. arXiv 2025.
[5] Klemmer, K., et al. (2025). SatCLIP: Global, General-Purpose Location Embeddings with Satellite Imagery. AAAI 2025.
[6] Czerkawski, M., Kluczek, M., & Bojanowski, J. S. (2024). Global and Dense Embeddings of Earth: Major TOM Floating in the Latent Space. arXiv preprint arXiv:2412.05600.
[7] Oquab, M., et al. (2023). DINOv2: Learning Robust Visual Features without Supervision. arXiv preprint arXiv:2304.07193.
[8] Zheng, Y., et al. (2026). EarthEmbeddingExplorer: A Web Application for Cross-Modal Retrieval of Global Satellite Images. 4th ICLR Workshop on ML4RS (Tutorial Track).