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M3LEO: A Multi-Modal Multi-Label Earth Observation Dataset

This repository contains information about the multi-modal multi-label, wide area Earth Observation (EO) datasets collated during the 2023 Frontier Development Lab. It contains around 40 TB of co-aligned machine learning ready data tiles, spanning 9 EO datasets and 6 geographic regions. For ease of access, the dataset has been compressed as parquet files. For a smaller (uncompressed) version of our dataset, check out the M3LEO miniset.

PAPER @ NeurIPS 2024 D&B track

Decompression

If you need to decompress the files, please see the main README at the github repo.
If you want to use them directly from the parquet files, the original .tif/.nc files were read into the rows as binary file data sources

Tile Definitions

Each data tile covers an area of 4480m x 4480m (448x448 pixels at 10m/pixel) and is labelled with a unique identifier based on location.

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Areas of Interest

Our areas of interest (AOIs) span China, Conus, Europe, the Middle East, Pakin, and South America. Each AOI has a '.geojson' file associated with the geometries and identifiers of each data tile.

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Train-Test-Validation Splits

For each geopgraphic area, we provide '.csv' files with predefined train, test and validation splits that can be used for repeatability and comparability of experiments. 60% of tiles are allocated for training, 20% for validation, and 20% for testing.

Temporal Coverage

As of now, M3LEO contains data from 2018 - 2020 for SAR amplitude and multi-spectral Sentinel-2 imagery. Other datasets are provided for 2020 only. Future iterations might extend the dataset to other years.

Datasets

The M3LEO dataset spans 9 diverse EO data types, covering input EO imagery and associated labels.

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Synthetic Aperture Radar Datasets

Optical Imagery

Labeled Datasets

Digital Elevation Model

Acknowledgements

This work has been enabled by Frontier Development Lab Europe a public / private partnership between the European Space Agency (ESA), Trillium Technologies, the University of Oxford and leaders in commercial AI supported by Google Cloud and NVIDIA, developing open science for all Humankind.