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CDChat: A Large Multimodal Model for Remote Sensing Change Description

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arxiv 2409.16261 v1 pith:AXLIYO6P submitted 2024-09-24 cs.CV

Mubashir Noman , Noor Ahsan , Muzammal Naseer , Hisham Cholakkal , Rao Muhammad Anwer , Salman Khan , Fahad Shahbaz Khan This is my paper · ORCID
classification cs.CV
keywords imageschangedescribedescriptioninstructionlmmsperformanceachieve
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Large multimodal models (LMMs) have shown encouraging performance in the natural image domain using visual instruction tuning. However, these LMMs struggle to describe the content of remote sensing images for tasks such as image or region grounding, classification, etc. Recently, GeoChat make an effort to describe the contents of the RS images. Although, GeoChat achieves promising performance for various RS tasks, it struggles to describe the changes between bi-temporal RS images which is a key RS task. This necessitates the development of an LMM that can describe the changes between the bi-temporal RS images. However, there is insufficiency of datasets that can be utilized to tune LMMs. In order to achieve this, we introduce a change description instruction dataset that can be utilized to finetune an LMM and provide better change descriptions for RS images. Furthermore, we show that the LLaVA-1.5 model, with slight modifications, can be finetuned on the change description instruction dataset and achieve favorably better performance.

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Cited by 4 Pith papers

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

  1. EarthDial: Turning Multi-sensory Earth Observations to Interactive Dialogues

    cs.CV 2024-12 conditional novelty 6.0 of 10

    EarthDial is a 4B-parameter remote sensing chatbot trained on 11.11M instruction pairs to handle multi-resolution, multi-spectral, and multi-temporal satellite imagery, and it reports gains over prior VLMs on dozens o...

  2. RSUniVLM: A Unified Vision Language Model for Remote Sensing via Granularity-oriented Mixture of Experts

    cs.CV 2024-12 conditional novelty 6.0 of 10

    RSUniVLM is a 1-billion-parameter remote sensing vision-language model that unifies image-, region-, and pixel-level tasks plus multi-image change analysis, achieving state-of-the-art visual grounding on VRSBench and ...

  3. DeltaVLM: Interactive Remote Sensing Image Change Analysis via Instruction-guided Difference Perception

    cs.CV 2025-07 conditional novelty 5.0 of 10

    DeltaVLM is an instruction-tuned vision-language model trained on a new 105k-pair dataset to answer multi-turn questions about changes in pairs of satellite images.

  4. CCExpert: Advancing MLLM Capability in Remote Sensing Change Captioning with Difference-Aware Integration and a Foundational Dataset

    cs.CV 2024-11 reject novelty 5.0 of 10

    CCExpert reports S*_m=81.80 on LEVIR-CC change captioning using a difference-aware module and a 200k-pair pretraining dataset, but possible test-set contamination undermines the claim.

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