[2605.03189] Sentinel2Cap: A Human-Annotated Benchmark Dataset for Multimodal Remote Sensing Image Captioning
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Computer Science > Computer Vision and Pattern Recognition
arXiv:2605.03189 (cs)
[Submitted on 4 May 2026]
Title:Sentinel2Cap: A Human-Annotated Benchmark Dataset for Multimodal Remote Sensing Image Captioning
Authors:Lucrezia Tosato, Gianluca Lombardi, Ronny Hansch
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Abstract:Image captioning has become an important task in computer vision, enabling models to generate natural language descriptions of visual content. While several datasets exist for natural images and high-resolution optical remote sensing imagery, the availability of captioning datasets for multimodal satellite data remains limited, particularly for SAR imagery and medium-resolution sensors. We introduce Sentinel2Cap, a human-annotated multimodal captioning dataset containing Sentinel-1 SAR and Sentinel-2 multi-spectral image patches at 10 m and 20 m spatial resolution with diverse land cover compositions. Captions are created manually and carefully validated to ensure both semantic accuracy and linguistic quality. To evaluate Sentinel2Cap, we perform a zero-shot captioning using the Qwen3-VL-8B-Instruct model across three image modalities: RGB, multi-spectral, and SAR pseudo-RGB representations. Results show that RGB images achieve the highest captioning performance, while SAR images remain more challenging for vision-language models. Providing modality-specific contextual prompts consistently improves performance across all metrics. These findings highlight both the challenges of multimodal remote sensing image captioning and the potential value of human-annotated datasets for advancing research in cross-modal scene understanding. All the material is publicly avaiable.
Comments: 10 pages, 6 figures, 5 tables
Subjects:
Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2605.03189 [cs.CV]
(or arXiv:2605.03189v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2605.03189
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arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Lucrezia Tosato [view email] [v1] Mon, 4 May 2026 22:16:11 UTC (6,031 KB)
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