Earth Embeddings as Products: Taxonomy, Ecosystem, and Standardized Access

2026-01-19

AI Summary

This work addresses the fragmentation and lack of standardized interfaces in the ecosystem of geospatial foundation model embeddings, which severely hinder model comparison and reproducibility. We formalize, for the first time, the Earth embedding product ecosystem and propose a three-tier taxonomy grounded in data, tools, and value dimensions. Through a systematic analysis of interoperability barriers, we extend TorchGeo to develop a unified API that treats Earth embeddings as standardized geospatial datasets, enabling plug-and-play integration of heterogeneous, multi-source embedding products. This framework effectively decouples downstream analytical tasks from embedding engineering, substantially lowering the barrier to entry and promoting reproducibility, transparency, and fair benchmarking in remote sensing workflows.

Technology Category

• Computer Vision: Remote Sensing / Geospatial AI
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
• Natural Language Processing: Language Grounding & Multi-modal NLP

Application Category

• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Federated Web and WoT systems, including distributed, federated and edge-based data processing
• Security and Privacy: Data transparency and provenance

Abstract

Geospatial Foundation Models (GFMs) provide powerful representations, but high compute costs hinder their widespread use. Pre-computed embedding data products offer a practical"frozen"alternative, yet they currently exist in a fragmented ecosystem of incompatible formats and resolutions. This lack of standardization creates an engineering bottleneck that prevents meaningful model comparison and reproducibility. We formalize this landscape through a three-layer taxonomy: Data, Tools, and Value. We survey existing products to identify interoperability barriers. To bridge this gap, we extend TorchGeo with a unified API that standardizes the loading and querying of diverse embedding products. By treating embeddings as first-class geospatial datasets, we decouple downstream analysis from model-specific engineering, providing a roadmap for more transparent and accessible Earth observation workflows.

Problem

• Geospatial Foundation Models
• embedding products
• standardization
• interoperability
• reproducibility

Innovation

• Geospatial Foundation Models
• Earth Embeddings
• Standardized API
• Interoperability
• TorchGeo