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The paper introduces EarthView, a large-scale remote sensing dataset comprising 15 tera pixels of multi-source imagery (NEON, Sentinel, Satellogic) spanning 2017-2022, designed for self-supervised learning. To leverage this dataset, the authors develop Earth-MAE, a Masked Autoencoder variant tailored for remote sensing data, capable of handling diverse modalities like hyperspectral, multispectral, and topographical data. Experiments demonstrate that pre-training Earth-MAE on Satellogic data within EarthView improves performance on downstream Earth monitoring tasks.
Unlock better Earth monitoring: a massive, diverse remote sensing dataset and tailored Masked Autoencoder let you pre-train models that actually improve downstream task performance.
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 dataset1 1Available at https://huggingface.co/datasets/satellogic/EarthView in parquet format. This data spans five years, from 2017 to 2022. Accompanying the dataset, we introduce Earth-MAE, 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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