SSL4EO-S12 v1.1: A Multimodal, Multiseasonal Dataset for Pretraining, Updated

• URL: http://arxiv.org/abs/2503.00168v2
• Date: Thu, 06 Mar 2025 09:23:35 GMT
• Title: SSL4EO-S12 v1.1: A Multimodal, Multiseasonal Dataset for Pretraining, Updated
• Authors: Benedikt Blumenstiel, Nassim Ait Ali Braham, Conrad M Albrecht, Stefano Maurogiovanni, Paolo Fraccaro,
• Abstract summary: SSL4EO-S12 v1.1 is a dataset designed for pretraining large-scale foundation models.<n>This dataset covers the world's 10,000 largest cities and its surroundings within a 50 km radius across four seasons.
• Score: 1.627786257389396
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: This technical report presents SSL4EO-S12 v1.1, a multimodal, multitemporal Earth Observation dataset designed for pretraining large-scale foundation models. Building on the success of SSL4EO-S12 v1.0, the new version addresses the previous challenges of data misalignment and a limited data structure for low-barrier, analysis-ready EO processing. SSL4EO-S12 v1.1 covers the world's 10,000 largest cities and its surroundings within a 50 km radius across four seasons, resulting in a diverse collection of nearly one million patches. SSL4EO-S12 v1.1 packages the data in Zarr file format for cloud-efficient loading and representation of meta-information such as including cloud masks and geolocation. Released under the CC-BY-4.0 license, SSL4EO-S12 v1.1 facilitates open research and provides a robust foundation for future advancements in self-supervised learning and geospatial analysis. The dataset is available online through https://datapub.fz-juelich.de/ssl4eo-s12, and we provided additional resources at https://github.com/DLR-MF-DAS/SSL4EO-S12-v1.1.

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