[Submitted on 23 Sep 2026]
Title:GeoNLI - A Natural Language Interpreter for Satellite Imagery
View a PDF of the paper titled GeoNLI - A Natural Language Interpreter for Satellite Imagery, by Ashutosh Gandhe and 7 other authors
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Abstract:Multi-modal multitasking models have shown strong performance on remote sensing datasets. However, because these models are trained on heterogeneous data and vary across tasks, designing a unified model that performs well in captioning, visual question answering (VQA), and visual grounding remains challenging.
In this work, we evaluate several models on the VRS Bench and NWPU-VHR-10 datasets. The EarthMind model demonstrates strong results in both captioning and VQA. For grounding, we propose multiple pipelines - RemoteSAM-SAM-v1, RemoteSAM-SAM-v2, and DiffuSAM - and ultimately adopt a majority-voting ensemble across EarthMind, RemoteSAM, SAM3, Falcon, RemoteSAM-SAM3-v1, RemoteSAM-SAM3-v2, and DiffuSAM predictions.
Our unified, modular pipeline integrates advanced SAM variants with multimodal LLMs to jointly perform captioning, VQA, and grounding. It achieves 82% accuracy on captioning and 83.32% on VQA, with 90.94%, 52.04%, and 92.06% for binary, numeric, and semantic question types respectively. For grounding, it attains 64.94% accuracy. By combining diverse VLMs with our custom RemoteSAM-SAM3 models through ensemble majority voting, the system delivers more accurate and consistent remote-sensing understanding than task-specific approaches.
Subjects:
Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2609.28741 [cs.CV]
(or arXiv:2609.28741v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2609.28741
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Madhav Kotecha [view email] [v1] Wed, 23 Sep 2026 19:31:23 UTC (26,538 KB)
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