EarthView为遥感自监督学习提供超大规模数据集与专用模型。
EarthView: A Large Scale Remote Sensing Dataset for Self-Supervision
- 构建15太像素全球遥感数据集，融合多源异构影像。
- 基于自监督训练的EarthMAE模型提升下游任务性能。
- 适合遥感、地球监测方向研究者使用。
本文提出EarthView，一个专为遥感数据自监督学习设计的大规模数据集，旨在提升深度学习在地球监测中的应用。数据集涵盖15太像素的全球遥感影像，整合来自NEON、Sentinel及Satellogic新发布的1米分辨率数据，时间跨度为2017至2022年，覆盖多种传感器和分辨率。数据以Parquet格式统一组织，并通过HuggingFace提供访问。配套提出EarthMAE——一种针对遥感特性定制的掩码自编码器，可处理高光谱、多光谱、地形数据、分割图与时序结构等多模态信息。实验表明，在Satellogic数据上预训练能有效提升下游任务表现。尽管对异构数据的MAE仍有改进空间，但该数据集与模型的结合为地球监测的深度学习发展迈出重要一步。
原文摘要 · Abstract (English)
This paper presents EarthView, a comprehensive dataset specifically designed for self-supervision on remote sensing data, intended to enhance deep learning applications on Earth monitoring tasks. The dataset spans 15 tera pixels of global remote-sensing data, combining imagery from a diverse range of sources, including NEON, Sentinel, and a novel release of 1m spatial resolution data from Satellogic. Our dataset provides a wide spectrum of image data with varying resolutions, harnessed from different sensors and organized coherently into an accessible HuggingFace dataset in parquet format. This data spans five years, from 2017 to 2022. Accompanying the dataset, we introduce EarthMAE, a tailored Masked Autoencoder, developed to tackle the distinct challenges of remote sensing data. Trained in a self-supervised fashion, EarthMAE effectively processes different data modalities such as hyperspectral, multispectral, topographical data, segmentation maps, and temporal structure. This model helps us show that pre-training on Satellogic data improves performance on downstream tasks. While there is still a gap to fill in MAE for heterogeneous data, we regard this innovative combination of an expansive, diverse dataset and a versatile model adapted for self-supervised learning as a stride forward in deep learning for Earth monitoring.
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