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Global AI embeddings in Earth Observation
First global embeddings dataset for Earth observations
Innovative dataset that integrates cutting-edge AI technologies to advance Earth Observation capabilities, enabling more precise and scalable analysis of satellite data.
- Over 170 million embeddings
- More than 62 TB of raw data
- 9.368 trillion pixels processed
Solution Overview
What are embeddings?
Embeddings are high-dimensional vectors that transform complex data into numerical representations, capturing relationships and meanings.
By mapping words, images, or entire documents into this space, embeddings maintain the semantic properties of the original data, allowing AI models to understand and process context with accuracy.
This enables machines to identify patterns, similarities, and connections that might otherwise be challenging to detect.
Embeddings insights
Raw data AI transformation
Embeddings transform raw data into a structured format that can be meaningfully interpreted, allowing AI models to extract deeper insights and relationships.
More accurate analysis
By capturing the underlying patterns and connections within the data, embeddings enable more accurate and context-aware analysis, needed in machine learning, natural language understanding, and computer vision.
The foundation for scalability
Embeddings unlock new possibilities across a wide range of applications, from predictive modeling to advanced decision-making systems.
First global embeddings dataset for EO
CloudFerro and European Space Agency (ESA) Φ-lab has introduced the first global embeddings dataset for Earth observations. This innovative dataset integrates cutting-edge AI technologies to advance Earth Observation capabilities, enabling more precise and scalable analysis of satellite data. By harnessing the power of GPU-accelerated instances provided by CloudFerro, that not only shares the infrastructure but also plays a key role in preparing the embeddings, alongside the expertise of ESA Φ-lab.
As a CloudFerro team, we have created a high-performance computational environment capable of processing vast amounts of Earth Observation data at an unprecedented scale. The global embeddings are computed using the CREODIAS cloud service platform, powered by GPU-accelerated instances provided by CloudFerro.
Learn more in the paper: Global and Dense Embeddings of Earth: Major TOM Floating in the Latent Space.
Value for Earth Observation data
Embeddings are increasingly valuable in the field of Earth Observation (EO), offering a range of applications for professionals across this sector.
Empowering EO professionals
Embeddings can be leveraged by a wide range of professionals across the Earth Observation sector: remote sensing scientists, geospatial analysts, and environmental researchers.
Wide range of applications
Data scientists and machine learning engineers in EO can apply embeddings to process and analyze large volumes of geospatial data for tasks such as pattern recognition, anomaly detection, and predictive modeling.
Efficient data representation
Users can use embeddings to extract meaningful features from satellite imagery, sensor data, and geographic information systems (GIS), enabling more efficient analysis of complex spatial relationships, saving time and resources.
Simplifying satellite data processing
With CloudFerro handling embedding calculations users can offload the heavy computational tasks to the cloud, which allows them to work with lightweight, pre-processed embeddings rather than managing large, complex satellite datasets themselves.
Global embeddings for satellite images
CloudFerro, in collaboration with ESA Phi Lab, has successfully calculated global embeddings based on advanced AI models for Sentinel-2 and Sentinel-1 imagery at a 10-meter resolution. Utilizing general-purpose vision models like SigLIP and DINOv2, along with SSL4EO for Earth Observation models. This global run marks a major advancement in our efforts, representing a significant leap forward in the scale and scope of Earth Observation data processing.
Over 170 Million Embeddings from Trillions of Pixels
Over 170 million embeddings were generated from more than 62 TB of raw data, distilling insights from 9.368 trillion pixels of source data. This comprehensive analysis involved processing more than 8 million Sentinel-1 and Sentinel-2 images from the Major TOM dataset.
CloudFerro and Phi Lab have created an efficient data representation that captures key relationships and insights, enabling faster processing, easier analysis, and more actionable data for smarter decision-making. This work is part of an expanded standard for releasing Major TOM. Embeddings expansion through open datasets on HuggingFace, including:
– similar colors lead to similar interpretations.
– similar colors lead to similar interpretations.
Enhancing trend discovery, and applications e.g. in agriculture, land management, image restoration, through AI-driven embeddings.
AI embeddings enable a broad range of advanced capabilities for more intelligent and efficient analyses of satellite imagery.
Embeddings can enhance satellite image browsers, allowing users to quickly and accurately explore vast datasets with improved navigation and context. With similarity search, patterns and trends from satellite images can be identified at a global scale, unlocking insights into climate patterns, urbanization, and natural events. Additionally, AI-driven embeddings help model crop yields, improving agricultural predictions, while aiding in image restoration for clearer, more precise visuals. For land cover classification, embeddings enable more accurate mapping of various terrains and crop types, supporting better land management and resource allocation.
Next steps
Evaluating embeddings across EO tasks, integrating foundation models, and expanding resource availability through the CREODIAS repository.
The next steps involve testing and evaluating the computed embeddings across a range of Earth Observation (EO) tasks to assess their performance and applicability in various real-world scenarios. Additionally, further testing will be conducted with other EO foundation models, including MMEarth and DeCUR, to examine their integration with the existing embeddings and optimize their capabilities. Furthermore, the MajorTOM dataset with embeddings will be integrated into the CREODIAS repository, providing access to these resources for the broader EO research community.
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