Clustering Guided Domain-Specific Pretrained Foundation Model Very High-Resolution Arctic Remote Sensing
This study introduces an Arctic-focused remote sensing foundation model combining diversity-aware image curation with masked autoencoder (MAE) pretraining on a Vision Transformer. It achieves 5-8% F1 improvement over ImageNet baseline on four Arctic datasets and outperforms Prithvi-EO-2.0 by at least 15%, demonstrating the value of domain-specific pretraining.
[2605.30467] Clustering Guided Domain-Specific Pretrained Foundation Model Very High-Resolution Arctic Remote Sensing
[Submitted on 28 May 2026]
Title:Clustering Guided Domain-Specific Pretrained Foundation Model Very High-Resolution Arctic Remote Sensing
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Abstract:This study introduces a novel Arctic-focused remote sensing foundation model (RSFM) by combining diversity-aware regional-scale image curation with masked autoencoder (MAE) self-supervised pretraining of a Vision Transformer (ViT) encoder for very-high-spatial-resolution (VHSR) satellite image analysis. Spectral and acquisition-metadata descriptors were used in a scalable affinity-propagation clustering workflow to select approximately 3 million chips from 267 TB of Vantor VHSR imagery This curation strategy was designed to reduce oversampling of visually repetitive or low-information areas while preserving broad scene diversity across the study domain. We pretrained a ViT-Large encoder on the curated corpus using a domain-adapted MAE reconstruction objective, producing Arctic-specific transformer weights for downstream feature mapping. The pretrained encoder was integrated into an existing location-aware detection and segmentation framework and evaluated across four hand-labeled Arctic datasets. Compared to ImageNet-initialized ViT-Large baseline, Arctic MAE pretraining produced consistent improvements in foreground mean F1 scores of 0.87, 0.72, 0.93, and 0.87, for infrastructure, IWP, RTS, and TCNs, with approximately 5-8 percentage increase. The proposed model also outperformed Prithvi-EO-2.0 in all downstream comparisons, with the smallest gain corresponding to at least a 15 percentage improvement mean F1, suggesting that domain-specific self-supervised pretraining on curated Arctic VHSR imagery provides more transferable representations for fine-scale Arctic mapping than a general-purpose Earth observation foundation model. These results demonstrate that optimizing the pretraining data distribution at regional scale, while keeping the architecture and MAE objective fixed, can produce a reusable Arctic-domain encoder for multiple VHSR remote sensing applications.
Subjects:
Computer Vision and Pattern Recognition (cs.CV)
ACM classes: I.2.10
Cite as: arXiv:2605.30467 [cs.CV]
(or arXiv:2605.30467v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2605.30467
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Amal Perera [view email] [v1] Thu, 28 May 2026 18:40:32 UTC (3,184 KB)
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