更新的地球观测预训练数据集，支持多模态多时相分析。
SSL4EO-S12 v1.1: A Multimodal, Multiseasonal Dataset for Pretraining, Updated
- 采用Zarr+WebDataset结构，提升数据加载效率与元信息管理。
- 覆盖全球1万大城市，含24.6万条时间序列近百万图像块。
- 新增高程、地表覆盖和植被模态，适合自监督学习研究者使用。
本文介绍SSL4EO-S12 v1.1，一个面向大规模基础模型预训练的多模态、多时相地球观测数据集。在前版基础上，修复了地理对齐误差与低效数据结构问题。数据集保持原版全球10,000个最大城市及其周边区域的空间覆盖范围，包含246,000条时间序列与近一百万张图像块。每个时间序列以Zarr格式打包并存储于WebDataset tar分片中，支持高效加载与云掩码等元信息表达。新增高程、土地覆盖与植被模态，支持多模态预训练。数据集采用CC-BY-4.0许可发布，可通过https://huggingface.co/datasets/embed2scale/SSL4EO-S12-v1.1在线获取，推动开放研究与自监督学习在地理空间分析中的发展。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。