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USat: A Unified Self-Supervised Encoder for Multi-Sensor Satellite Imagery

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arxiv 2312.02199 v1 pith:2DDVGUFU submitted 2023-12-02 cs.CV cs.AIcs.LGeess.IVstat.AP

Jeremy Irvin , Lucas Tao , Joanne Zhou , Yuntao Ma , Langston Nashold , Benjamin Liu , Andrew Y. Ng This is my paper · ORCID
classification cs.CVcs.AIcs.LGeess.IVstat.AP
keywords self-supervisedusatdatamultiplepre-trainingremotesensingencoder
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Large, self-supervised vision models have led to substantial advancements for automatically interpreting natural images. Recent works have begun tailoring these methods to remote sensing data which has rich structure with multi-sensor, multi-spectral, and temporal information providing massive amounts of self-labeled data that can be used for self-supervised pre-training. In this work, we develop a new encoder architecture called USat that can input multi-spectral data from multiple sensors for self-supervised pre-training. USat is a vision transformer with modified patch projection layers and positional encodings to model spectral bands with varying spatial scales from multiple sensors. We integrate USat into a Masked Autoencoder (MAE) self-supervised pre-training procedure and find that a pre-trained USat outperforms state-of-the-art self-supervised MAE models trained on remote sensing data on multiple remote sensing benchmark datasets (up to 8%) and leads to improvements in low data regimes (up to 7%). Code and pre-trained weights are available at https://github.com/stanfordmlgroup/USat .

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. AnySat: One Earth Observation Model for Many Resolutions, Scales, and Modalities

    cs.CV 2024-12 conditional novelty 7.0 of 10

    A single JEPA-based model with scale-adaptive encoders is pre-trained on five heterogeneous Earth observation datasets and reaches state-of-the-art results across nine downstream tasks.

  2. SatMamba: Development of Foundation Models for Remote Sensing Imagery Using State Space Models

    cs.CV 2025-02 conditional novelty 6.0 of 10

    SatMamba shows that a Mamba-based masked autoencoder matches ViT-based MAE on remote sensing segmentation and damage assessment, with efficiency linear in sequence length only at larger inputs.

  3. Deploying Geospatial Foundation Models in the Real World: Lessons from WorldCereal

    cs.LG 2025-07 conditional novelty 5.0 of 10

    A structured protocol for deploying geospatial foundation models is introduced and validated in WorldCereal, where fine-tuned Presto outperforms a fully-supervised CatBoost baseline in crop mapping.

  4. TiMo: Spatiotemporal Foundation Model for Satellite Image Time Series

    cs.CV 2025-05 conditional novelty 5.0 of 10

    TiMo, a hierarchical transformer pretrained on one million Sentinel-2 images with a space-time gyroscope attention, reports state-of-the-art accuracy on deforestation, land cover, crop type, and flood mapping tasks.

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