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

2026-03-31

AI Summary

This work addresses the challenge of transforming Earth observation foundation models and their associated embeddings into open, accessible scientific tools that support cross-modal retrieval and discovery. The authors present a cloud-native, interactive web platform that, for the first time, publicly provides precomputed embeddings for global satellite imagery. The system integrates three complementary query modalities—natural language, image similarity, and geographic coordinates—enabling efficient, low-barrier, multimodal search over remote sensing data. By bridging the gap between advanced academic models and real-world applications, the platform significantly enhances the accessibility and scientific utility of Earth observation data at a global scale.

Technology Category

• Computer Vision: Remote Sensing / Geospatial AI
• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Language Grounding & Multi-modal NLP

Application Category

• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

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.

Problem

• Earth observation
• foundation models
• cross-modal retrieval
• satellite images
• web application

Innovation

• cross-modal retrieval
• Earth embedding
• web application
• cloud-native architecture
• satellite imagery