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

• URL: http://arxiv.org/abs/2603.29441v2
• Date: Wed, 08 Apr 2026 03:10:22 GMT
• Title: EarthEmbeddingExplorer: A Web Application for Cross-Modal Retrieval of Global Satellite Images
• Abstract summary: Earth observation community has witnessed a surge in high-impact foundation models and global Earth embedding datasets.<n>This tutorial introduces Earth-EmbeddingExplorer, an interactive web application designed to bridge this gap.<n>We will provide a comprehensive hands-on guide to the system, detailing its cloud-EmbeddingExplorer software architecture.
• Score: 10.020566784262819
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Toward a Scientific Discovery Engine for Weather and Climate Data: A Visual Analytics Workbench for Embedding-Based Exploration [0.0]
  Earth system science is producing increasingly large, high-dimensional datasets from physics based Earth system models to AI-based weather and climate models.<n> Embedding-based representations can make these data searchable through similarity search and analog retrieval, but nearest neighbors in latent space are not automatically scientifically meaningful.<n>We present an open-source visual analytics workbench for each of these steps.
  arXiv  Detail & Related papers  (2026-05-01T17:03:33Z)
• Web World Models [60.208836336654315]
  We introduce the Web World Model (WWM), a middle ground where world state and physics'' are implemented in ordinary web code.<n>We build a suite of WWMs on a realistic web stack, including an infinite travel atlas grounded in real geography, fictional galaxy explorers, web-scale encyclopedic and narrative worlds, and simulation- and game-like environments.<n>Our results suggest that web stacks themselves can serve as a scalable substrate for world models, enabling controllable yet open-ended environments.
  arXiv  Detail & Related papers  (2025-12-29T18:31:45Z)
• MESA: Text-Driven Terrain Generation Using Latent Diffusion and Global Copernicus Data [0.0]
  We present MESA - a novel data-centric alternative to procedural terrain modeling.<n>MESA generates high-quality terrain samples from text descriptions using global remote sensing data.<n>The model's capabilities are demonstrated through extensive experiments, highlighting its ability to generate realistic and diverse terrain landscapes.
  arXiv  Detail & Related papers  (2025-04-09T18:37:24Z)
• EarthView: A Large Scale Remote Sensing Dataset for Self-Supervision [72.84868704100595]
  This paper presents a dataset specifically designed for self-supervision on remote sensing data, intended to enhance deep learning applications on Earth monitoring tasks.<n>The dataset spans 15 tera pixels of global remote-sensing data, combining imagery from a diverse range of sources, including NEON, Sentinel, and a novel release of 1m spatial resolution data from Satellogic.<n>Accompanying the dataset is EarthMAE, a tailored Masked Autoencoder developed to tackle the distinct challenges of remote sensing data.
  arXiv  Detail & Related papers  (2025-01-14T13:42:22Z)
• Global and Dense Embeddings of Earth: Major TOM Floating in the Latent Space [0.0]
  There is a growing need for efficient vector representations of the underlying raw data.<n>The approach of extracting feature representations from pretrained deep neural networks is a powerful approach that can provide semantic abstractions of the input data.<n>In this work, an extension is proposed to an existing community project, Major TOM, focused on the provision and standardization of open and free AI-ready datasets for Earth observation.
  arXiv  Detail & Related papers  (2024-12-07T09:49:47Z)
• Swarm Intelligence in Geo-Localization: A Multi-Agent Large Vision-Language Model Collaborative Framework [51.26566634946208]
  We introduce smileGeo, a novel visual geo-localization framework. By inter-agent communication, smileGeo integrates the inherent knowledge of these agents with additional retrieved information. Results show that our approach significantly outperforms current state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-08-21T03:31:30Z)
• A General Purpose Neural Architecture for Geospatial Systems [142.43454584836812]
  We present a roadmap towards the construction of a general-purpose neural architecture (GPNA) with a geospatial inductive bias. We envision how such a model may facilitate cooperation between members of the community.
  arXiv  Detail & Related papers  (2022-11-04T09:58:57Z)
• EarthNets: Empowering AI in Earth Observation [24.160463837610074]
  Earth observation (EO) aims at monitoring the state of planet Earth using remote sensing data. This paper presents a comprehensive review of more than 500 publicly published datasets. We propose to measure, rank, and select datasets to build a new benchmark for model evaluation.
  arXiv  Detail & Related papers  (2022-10-10T18:09:35Z)
• Rapid Exploration for Open-World Navigation with Latent Goal Models [78.45339342966196]
  We describe a robotic learning system for autonomous exploration and navigation in diverse, open-world environments. At the core of our method is a learned latent variable model of distances and actions, along with a non-parametric topological memory of images. We use an information bottleneck to regularize the learned policy, giving us (i) a compact visual representation of goals, (ii) improved generalization capabilities, and (iii) a mechanism for sampling feasible goals for exploration.
  arXiv  Detail & Related papers  (2021-04-12T23:14:41Z)
• Object Goal Navigation using Goal-Oriented Semantic Exploration [98.14078233526476]
  This work studies the problem of object goal navigation which involves navigating to an instance of the given object category in unseen environments. We propose a modular system called, Goal-Oriented Semantic Exploration' which builds an episodic semantic map and uses it to explore the environment efficiently.
  arXiv  Detail & Related papers  (2020-07-01T17:52:32Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.