[2605.03175] DINO Soars: DINOv3 for Open-Vocabulary Semantic Segmentation of Remote Sensing Imagery
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Computer Science > Computer Vision and Pattern Recognition
arXiv:2605.03175 (cs)
[Submitted on 4 May 2026]
Title:DINO Soars: DINOv3 for Open-Vocabulary Semantic Segmentation of Remote Sensing Imagery
Authors:Ryan Faulkenberry, Saurabh Prasad
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Abstract:The remote sensing (RS) domain suffers from a lack of densely labeled datasets, which are costly to obtain. Thus, models that can segment RS imagery well without supervised fine-tuning are valuable, but existing solutions fall behind supervised methods. Recently, DINOv3 surpassed SOTA RS foundation models on the GEO-bench segmentation benchmark without pre-training on RS data. Additionally, this http URL has enabled open vocabulary semantic segmentation (OVSS) with the DINOv3 backbone. We leverage these developments to form an OVSS model for RS imagery, free of RS-domain fine-tuning. Our model, CAFe-DINO (Cost Aggregation + Feature Upsampling with DINO) exploits the strong OVSS performance of DINOv3 for RS imagery via cost aggregation and training-free upsampling of text-image similarity scores. The robust latent of the DINOv3 backbone eliminates the need for fine-tuning on RS imagery; we instead fine-tune our model on a RS-targeted subset of COCO-Stuff. CAFe-DINO achieves state-of-the-art performance on key RS segmentation datasets, outperforming OVSS methods fine-tuned on RS data. Our code and data are publicly available at this https URL.
Comments: Accepted at 2026 CVPR MORSE Workshop
Subjects:
Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2605.03175 [cs.CV]
(or arXiv:2605.03175v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2605.03175
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arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Saurabh Prasad [view email] [v1] Mon, 4 May 2026 21:38:37 UTC (18,525 KB)
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