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

arXiv: https://arxiv.org/abs/2503.00168 · HTML: https://mlsanity.com/paper/2503.00168/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 18 references that are in the map.
- First posted on arXiv 28 Feb 2025.

## Builds on (most cited first, 7 of 7)

- [CROMA: Remote Sensing Representations with Contrastive Radar-Optical Masked Autoencoders](https://mlsanity.com/paper/2311.00566/) (2023)
- [Prithvi-EO-2.0: A Versatile Multi-Temporal Foundation Model for Earth Observation Applications](https://mlsanity.com/paper/2412.02732/) (2024)
- [SEN12MS -- A Curated Dataset of Georeferenced Multi-Spectral Sentinel-1/2 Imagery for Deep Learning and Data Fusion](https://mlsanity.com/paper/1906.07789/) (2019)
- [BigEarthNet-MM: A Large Scale Multi-Modal Multi-Label Benchmark Archive for Remote Sensing Image Classification and Retrieval](https://mlsanity.com/paper/2105.07921/) (2021)
- [MMEarth: Exploring Multi-Modal Pretext Tasks For Geospatial Representation Learning](https://mlsanity.com/paper/2405.02771/) (2024)
- [PANGAEA: A Global and Inclusive Benchmark for Geospatial Foundation Models](https://mlsanity.com/paper/2412.04204/) (2024)
- [Major TOM: Expandable Datasets for Earth Observation](https://mlsanity.com/paper/2402.12095/) (2024)

## Built on by (most cited first, 9 of 9)

- [TerraMesh: A Planetary Mosaic of Multimodal Earth Observation Data](https://mlsanity.com/paper/2504.11172/) (2025)
- [Beyond the Visible: Multispectral Vision-Language Learning for Earth Observation](https://mlsanity.com/paper/2503.15969/) (2025)
- [NeuCo-Bench: A Novel Benchmark Framework for Neural Embeddings in Earth Observation](https://mlsanity.com/paper/2510.17914/) (2025)
- [TerraFlow: Multimodal, Multitemporal Representation Learning for Earth Observation](https://mlsanity.com/paper/2603.12762/) (2026)
- [CBEN -- A Multimodal Machine Learning Dataset for Cloud Robust Remote Sensing Image Understanding](https://mlsanity.com/paper/2602.12652/) (2026)
- [How to Embed Matters: Evaluation of EO Embedding Design Choices](https://mlsanity.com/paper/2603.10658/) (2026)
- [Geospatial Foundational Embedder: Top-1 Winning Solution on EarthVision Embed2Scale Challenge (CVPR 2025)](https://mlsanity.com/paper/2509.06993/) (2025)
- [TerraCodec: Compressing Optical Earth Observation Data](https://mlsanity.com/paper/2510.12670/) (2025)
- [Far from the Crowd: Scalable Self-Supervised Learning via Geographic Isolation](https://mlsanity.com/paper/2608.19766/) (2026)

## Abstract (arXiv)

This work 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, this extension updates the previous version to fix geospatial alignment inaccuracies and the inefficent data structure. The dataset allows low-barrier, analysis-ready data loading while maintaining the predecessor's spatial coverage of the world's 10,000 largest cities and surrounding geographies, resulting in 246k time series with nearly one million image patches. We package each time series in Zarr file format stored in WebDataset tar shards for efficient data loading and representation of meta-information such as cloud masks. We add new modalities for elevation, land-cover, and vegetation to support multimodal pre-training. 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://huggingface.co/datasets/embed2scale/SSL4EO-S12-v1.1.