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RSGPT: A Remote Sensing Vision Language Model and Benchmark

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arxiv 2307.15266 v1 pith:NMVGROBD submitted 2023-07-28 cs.CV

Yuan Hu , Jianlong Yuan , Congcong Wen , Xiaonan Lu , Xiang Li This is my paper · ORCID
classification cs.CV
keywords vlmslanguagelargedatasetmodelsvisioncaptionsdatasets
verification ladder T0 review T1 audit T2 compute T3 formal
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The emergence of large-scale large language models, with GPT-4 as a prominent example, has significantly propelled the rapid advancement of artificial general intelligence and sparked the revolution of Artificial Intelligence 2.0. In the realm of remote sensing (RS), there is a growing interest in developing large vision language models (VLMs) specifically tailored for data analysis in this domain. However, current research predominantly revolves around visual recognition tasks, lacking comprehensive, large-scale image-text datasets that are aligned and suitable for training large VLMs, which poses significant challenges to effectively training such models for RS applications. In computer vision, recent research has demonstrated that fine-tuning large vision language models on small-scale, high-quality datasets can yield impressive performance in visual and language understanding. These results are comparable to state-of-the-art VLMs trained from scratch on massive amounts of data, such as GPT-4. Inspired by this captivating idea, in this work, we build a high-quality Remote Sensing Image Captioning dataset (RSICap) that facilitates the development of large VLMs in the RS field. Unlike previous RS datasets that either employ model-generated captions or short descriptions, RSICap comprises 2,585 human-annotated captions with rich and high-quality information. This dataset offers detailed descriptions for each image, encompassing scene descriptions (e.g., residential area, airport, or farmland) as well as object information (e.g., color, shape, quantity, absolute position, etc). To facilitate the evaluation of VLMs in the field of RS, we also provide a benchmark evaluation dataset called RSIEval. This dataset consists of human-annotated captions and visual question-answer pairs, allowing for a comprehensive assessment of VLMs in the context of RS.

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Cited by 9 Pith papers

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    A few-shot RLVR method using only rule-based rewards lifts a 2B vision-language model's remote sensing accuracy by double digits, with 128 examples rivaling thousands.

  3. Enhancing Remote Sensing Vision-Language Models Through MLLM and LLM-Based High-Quality Image-Text Dataset Generation

    cs.CV 2025-07 conditional novelty 6.0 of 10

    A two-stage method (MpGI) produces a 210K-image, 1.26M-caption remote sensing dataset and state-of-the-art CLIP and CoCa models.

  4. GeoMag: A Vision-Language Model for Pixel-level Fine-Grained Remote Sensing Image Parsing

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    A remote sensing vision-language model that uses task-aware resolution adjustment and attention-based cropping to perform pixel-level segmentation alongside image- and region-level tasks.

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  8. Chain-of-Talkers (CoTalk): Fast Human Annotation of Dense Image Captions

    cs.CL 2025-05 conditional novelty 6.0 of 10

    Chain-of-Talkers (CoTalk) has annotators sequentially dictate only the missing visual details, and it reports modest gains in annotation speed and caption density over parallel typed annotation.

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