Core-S1RTC-SSL4EO 📡⚡🛰️

Dataset	Modality	Number of Embeddings	Sensing Type	Total Comments	Source Dataset	Source Model	Size
Core-S1RTC-SSL4EO	Sentinel-1 RTC	36,748,875	SAR	General-Purpose Global	Core-S1RTC	SSL4EO-ResNet50-MOCO	332.5 GB

Content

Field	Type	Description
unique_id	string	hash generated from geometry, time, product_id, and embedding model
embedding	array	raw embedding array
grid_cell	string	Major TOM cell
grid_row_u	int	Major TOM cell row
grid_col_r	int	Major TOM cell col
product_id	string	ID of the original product
timestamp	string	Timestamp of the sample
centre_lat	float	Centre of the fragment latitude
centre_lon	float	Centre of the fragment longitude
geometry	geometry	Polygon footprint (WGS84) of the fragment
utm_footprint	string	Polygon footprint (image UTM) of the fragment
utm_crs	string	CRS of the original product
pixel_bbox	bbox	Boundary box of the fragment (pixels)

Input Data

• Sentinel-1 RTC radar dataset global coverage
• All samples from MajorTOM Core-S1RTC
• Image input size: 224 x 224 pixels, target overlap: 10%, border_shift: True

Model

The image encoder of the SSL4EO-ResNet50-MOCO model was used to extract embeddings.

Example Use

Interface scripts are available at

from datasets import load_dataset
dataset = load_dataset("Major-TOM/Core-S1RTC-SSL4EO")

Generate Your Own Major TOM Embeddings

The embedder subpackage of Major TOM provides tools for generating embeddings like these ones. You can see an example of this in a dedicated notebook at https://github.com/ESA-PhiLab/Major-TOM/blob/main/05-Generate-Major-TOM-Embeddings.ipynb.

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Major TOM Global Embeddings Project 🏭

This dataset is a result of a collaboration between CloudFerro 🔶 and Φ-lab, European Space Agency (ESA) 🛰️ set up in order to provide open and free vectorised expansions of Major TOM datasets and define a standardised manner for releasing Major TOM embedding expansions.

The embeddings extracted from common AI models make it possible to browse and navigate large datasets like Major TOM with reduced storage and computational demand.

The datasets were computed on the GPU-accelerated instances⚡ provided by CloudFerro 🔶 on the CREODIAS cloud service platform 💻☁️. Discover more at CloudFerro AI services.

Authors

Mikolaj Czerkawski (Φ-lab, European Space Agency), Marcin Kluczek (CloudFerro), Jędrzej S. Bojanowski (CloudFerro)

Open Access Manuscript

This dataset is an output from the embedding expansion project outlined in: https://arxiv.org/abs/2412.05600/.

Read Abstract

With the ever-increasing volumes of the Earth observation data present in the archives of large programmes such as Copernicus, there is a growing need for efficient vector representations of the underlying raw data. The approach of extracting feature representations from pretrained deep neural networks is a powerful approach that can provide semantic abstractions of the input data. However, the way this is done for imagery archives containing geospatial data has not yet been defined. 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. Furthermore, four global and dense embedding datasets are released openly and for free along with the publication of this manuscript, resulting in the most comprehensive global open dataset of geospatial visual embeddings in terms of covered Earth's surface.

If this dataset was useful for you work, it can be cited as:

@misc{EmbeddedMajorTOM,
      title={Global and Dense Embeddings of Earth: Major TOM Floating in the Latent Space}, 
      author={Mikolaj Czerkawski and Marcin Kluczek and Jędrzej S. Bojanowski},
      year={2024},
      eprint={2412.05600},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2412.05600}, 
}

Powered by Φ-lab, European Space Agency (ESA) 🛰️ in collaboration with CloudFerro 🔶