# EarthEmbeddingExplorer: A Web Application for Cross-Modal Retrieval of Global Satellite Images

arXiv: https://arxiv.org/abs/2603.29441 · HTML: https://mlsanity.com/paper/2603.29441/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 11 references that are in the map.
- First posted on arXiv 31 Mar 2026.

## Builds on (most cited first, 10 of 10)

- [DINOv2: Learning Robust Visual Features without Supervision](https://mlsanity.com/paper/2304.07193/) (2023)
- [Sigmoid Loss for Language Image Pre-Training](https://mlsanity.com/paper/2303.15343/) (2023)
- [Gradio: Hassle-Free Sharing and Testing of ML Models in the Wild](https://mlsanity.com/paper/1906.02569/) (2019)
- [SatCLIP: Global, General-Purpose Location Embeddings with Satellite Imagery](https://mlsanity.com/paper/2311.17179/) (2023)
- [Major TOM: Expandable Datasets for Earth Observation](https://mlsanity.com/paper/2402.12095/) (2024)
- [Global and Dense Embeddings of Earth: Major TOM Floating in the Latent Space](https://mlsanity.com/paper/2412.05600/) (2024)
- [GlacierNet2: A Hybrid Multi-Model Learning Architecture for Alpine Glacier Mapping](https://mlsanity.com/paper/2204.05818/) (2022)
- [Generalizable Slum Detection from Satellite Imagery with Mixture-of-Experts](https://mlsanity.com/paper/2511.10300/) (2025)
- [Earth Embeddings as Products: Taxonomy, Ecosystem, and Standardized Access](https://mlsanity.com/paper/2601.13134/) (2026)
- [FarSLIP: Discovering Effective CLIP Adaptation for Fine-Grained Remote Sensing Understanding](https://mlsanity.com/paper/2511.14901/) (2025)

## Built on by (most cited first, 1 of 1)

- [Earth Embeddings](https://mlsanity.com/paper/2608.03410/) (2026)

## Abstract (arXiv)

While the Earth observation community has witnessed a surge in high-impact foundation models and global Earth embedding datasets, a significant barrier remains in translating these academic assets into freely accessible tools. This tutorial introduces EarthEmbeddingExplorer, an interactive web application designed to bridge this gap, transforming static research artifacts into dynamic, practical workflows for discovery. We will provide a comprehensive hands-on guide to the system, detailing its cloud-native software architecture, demonstrating cross-modal queries (natural language, visual, and geolocation), and showcasing how to derive scientific insights from retrieval results. By democratizing access to precomputed Earth embeddings, this tutorial empowers researchers to seamlessly transition from state-of-the-art models and data archives to real-world application and analysis. The web application is available at https://modelscope.ai/studios/Major-TOM/EarthEmbeddingExplorer.