metadata

license: cc-by-sa-4.0
tags:
  - embeddings
  - earth-observation
  - remote-sensing
  - Sentinel-2
  - geospatial
  - satellite
  - satellite-imagery

Core-S2RGB-DINOv2 🔴🟢🔵

Dataset	Modality	Number of Embeddings	Sensing Type	Total Comments	Source Dataset	Source Model	Size
Core-S2RGB-SigLIP	Sentinel-2 Level 2A (RGB)	56,147,150	True Colour (RGB)	General-Purpose Global	Core-S2L2A	DINOv2	223.1 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-2 (Level 2A) RGB reflectance multiplied by 2.5 and clipped between 0 and 1 to resemble images in the training data
All samples from MajorTOM Core-S2LA
Image input size: 224 x 224 pixels, target overlap: 10%, border_shift: True

Model

The image encoder of the DINOv2 model was used to extract embeddings.

Example Use

Interface scripts are available at

from datasets import load_dataset
dataset = load_dataset("Major-TOM/Core-S2RGB-DINOv2")

Generate Your Own Embeddings

The embedder subpackage of Major TOM provides tools for generating embeddings like this ones. You can see an example of this in a dedicated notebook at (link).

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

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

Cite

  title={Global and Dense Embeddings of Earth: Major TOM Floating in the Latent Space}, 
  author={Mikolaj Czerkawski, Marcin Kluczek, Jędrzej S. Bojanowski},
  year={2024},
  archivePrefix={arXiv},
  primaryClass={cs.CV}
}

Powered by [Φ-lab, European Space Agency (ESA) 🛰️](https://philab.esa.int/) in collaboration with [CloudFerro 🔶](https://cloudferro.com/)