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LHRS-Bot: Empowering Remote Sensing with VGI-Enhanced Large Multimodal Language Model

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arxiv 2402.02544 v4 pith:NDSONT75 submitted 2024-02-04 cs.CV cs.AIcs.LG

Dilxat Muhtar , Zhenshi Li , Feng Gu , Xueliang Zhang , Pengfeng Xiao This is my paper · ORCID
classification cs.CVcs.AIcs.LG
keywords languagelargelhrs-botunderstandingdatasetdiverseimageimages
verification ladder T0 review T1 audit T2 compute T3 formal
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The revolutionary capabilities of large language models (LLMs) have paved the way for multimodal large language models (MLLMs) and fostered diverse applications across various specialized domains. In the remote sensing (RS) field, however, the diverse geographical landscapes and varied objects in RS imagery are not adequately considered in recent MLLM endeavors. To bridge this gap, we construct a large-scale RS image-text dataset, LHRS-Align, and an informative RS-specific instruction dataset, LHRS-Instruct, leveraging the extensive volunteered geographic information (VGI) and globally available RS images. Building on this foundation, we introduce LHRS-Bot, an MLLM tailored for RS image understanding through a novel multi-level vision-language alignment strategy and a curriculum learning method. Additionally, we introduce LHRS-Bench, a benchmark for thoroughly evaluating MLLMs' abilities in RS image understanding. Comprehensive experiments demonstrate that LHRS-Bot exhibits a profound understanding of RS images and the ability to perform nuanced reasoning within the RS domain.

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Cited by 14 Pith papers

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