CloudFerro and ESA Φ-lab release first global embeddings dataset for Earth observations

CloudFerro and the European Space Agency's (ESA) Φ-lab have presented the first global embeddings dataset for Earth observations, a significant advance in geospatial data analysis. This dataset, part of the Major TOM project, aims to provide standardized, open and accessible ai-ready datasets for Earth observation. This collaboration addresses the challenge of managing and analyzing Copernicus' massive archives of satellite data while promoting scalable ai applications.

The role of incorporating data sets in Earth observation

The increasing volume of Earth observation data presents challenges to efficiently process and analyze large-scale geospatial images. Dataset embedding addresses this problem by transforming high-dimensional image data into compact vector representations. These embeddings encapsulate key semantic features, facilitating faster searches, comparisons, and analysis.

He Great project TOM focuses on the geospatial domain, ensuring that its integrated datasets are compatible and reproducible for various Earth observation tasks. By leveraging advanced deep learning models, these additions streamline satellite image processing and analysis on a global scale.

Features of the global embeddings dataset

The built-in datasets, derived from the core TOM Core datasets, include over 60TB of ai-ready Copernicus data. Key features include:

• Comprehensive Coverage: With more than 169 million data points and more than 3.5 million unique images, the dataset provides a comprehensive representation of the Earth's surface.
• Various models: Generated using four different models (SSL4EO-S2, SSL4EO-S1, SigLIP and DINOv2), the embeddings offer varied feature representations tailored to different use cases.
• Efficient data format: Stored in GeoParquet format, embeddings integrate seamlessly with geospatial data workflows, enabling efficient querying and support for processing pipelines.

Integration methodology

Creating embeds involves several steps:

1. Image Fragmentation: Satellite images are divided into smaller patches suitable for the model input sizes, preserving geospatial details.
2. Preprocessing: Fragments are normalized and scaled according to the requirements of the embedding models.
3. Integrated generation: Pre-processed fragments are processed through pre-trained deep learning models to create embeddings.
4. Data integration: Embeds and metadata are compiled into GeoParquet files, ensuring optimized access and usability.

This structured approach ensures high-quality embeddings while reducing computational demands for downstream tasks.

Applications and use cases

Embedded data sets have various applications, including:

• Land use monitoring: Researchers can efficiently track land use changes by linking integrated spaces with labeled data sets.
• Environmental analysis: The dataset supports the analysis of phenomena such as deforestation and urban expansion with reduced computational costs.
• Search and data recovery: Embeddings enable quick similarity searches, simplifying access to relevant geospatial data.
• Time series analysis: Consistent integration footprints facilitate long-term monitoring of changes in different regions.

Computational efficiency

The built-in data sets are designed for scalability and efficiency. The calculations were performed on CloudFerro's CREODIAS cloud platform, using high-performance hardware such as NVIDIA L40S GPUs. This configuration allowed the processing of trillions of pixels of Copernicus data while maintaining reproducibility.

Standardization and open access

A hallmark of Major TOM's integrated data sets is their standardized format, which ensures compatibility between models and data sets. Open access to these data sets fosters transparency and collaboration, fostering innovation within the global geospatial community.

<h3 class="wp-block-heading" id="h-advancing-ai-in-earth-observation”>ai advances in Earth observation

The global addition dataset represents an important step forward in the integration of ai with Earth observation. Enabling efficient processing and analysis prepares researchers, policymakers, and organizations to better understand and manage Earth's dynamic systems. This initiative lays the foundation for new applications and insights in geospatial analysis.

Conclusion

The partnership between CloudFerro and ESA Φ-lab exemplifies progress in the geospatial data industry. By addressing the challenges of Earth observation and unlocking new possibilities for ai applications, the global integrated data set improves our ability to analyze and manage satellite data. As the Major TOM project evolves, it is poised to drive further advances in science and technology.

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