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GeoPixel: Pixel Grounding Large Multimodal Model in Remote Sensing

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arxiv 2501.13925 v1 pith:JHSWTRM3 submitted 2025-01-23 cs.CV

Akashah Shabbir , Mohammed Zumri , Mohammed Bennamoun , Fahad S. Khan , Salman Khan This is my paper · ORCID
classification cs.CV
keywords datageopixelgroundinglmmsconversationgroundedcapabilitycomprehension
verification ladder T0 review T1 audit T2 compute T3 formal
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Recent advances in large multimodal models (LMMs) have recognized fine-grained grounding as an imperative factor of visual understanding and dialogue. However, the benefits of such representation in LMMs are limited to the natural image domain, and these models perform poorly for remote sensing (RS). The distinct overhead viewpoint, scale variation, and presence of small objects in high-resolution RS imagery present a unique challenge in region-level comprehension. Moreover, the development of the grounding conversation capability of LMMs within RS is hindered by the lack of granular, RS domain-specific grounded data. Addressing these limitations, we propose GeoPixel - the first end-to-end high resolution RS-LMM that supports pixel-level grounding. This capability allows fine-grained visual perception by generating interleaved masks in conversation. GeoPixel supports up to 4K HD resolution in any aspect ratio, ideal for high-precision RS image analysis. To support the grounded conversation generation (GCG) in RS imagery, we curate a visually grounded dataset GeoPixelD through a semi-automated pipeline that utilizes set-of-marks prompting and spatial priors tailored for RS data to methodically control the data generation process. GeoPixel demonstrates superior performance in pixel-level comprehension, surpassing existing LMMs in both single-target and multi-target segmentation tasks. Our methodological ablation studies validate the effectiveness of each component in the overall architecture. Our code and data will be publicly released.

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Cited by 10 Pith papers

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

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    cs.CV 2026-08 conditional novelty 6.0 of 10

    CROSS improves remote sensing referring segmentation by combining cascaded SAM distillation with contrastive learning, reporting state-of-the-art cIoU on RefSegRS and RRSIS-D.

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    ChronoBench decomposes long-term remote sensing understanding into four cognitive levels, and the GeoChrono model, using per-location temporal trajectories, achieves 78.34% accuracy—over 20 points above prior MLLMs—bu...

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