GeoLLaVA: Efficient Fine-Tuned Vision-Language Models for Temporal Change Detection in Remote Sensing Article Swipe

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Hosam Elgendy , Ahmed Sharshar , Ahmed Aboeitta , Yasser Ashraf , Mohsen Guizani ·

YOU? · · 2024 · Open Access · · DOI: https://doi.org/10.48550/arxiv.2410.19552

Detecting temporal changes in geographical landscapes is critical for applications like environmental monitoring and urban planning. While remote sensing data is abundant, existing vision-language models (VLMs) often fail to capture temporal dynamics effectively. This paper addresses these limitations by introducing an annotated dataset of video frame pairs to track evolving geographical patterns over time. Using fine-tuning techniques like Low-Rank Adaptation (LoRA), quantized LoRA (QLoRA), and model pruning on models such as Video-LLaVA and LLaVA-NeXT-Video, we significantly enhance VLM performance in processing remote sensing temporal changes. Results show significant improvements, with the best performance achieving a BERT score of 0.864 and ROUGE-1 score of 0.576, demonstrating superior accuracy in describing land-use transformations.

Related Topics

Computer Science

Remote Sensing

Computer Vision

Artificial Intelligence

Geography

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Change detection Computer science Remote sensing Computer vision Artificial intelligence Geography

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Type: preprint
Language: en
Landing Page: http://arxiv.org/abs/2410.19552
PDF: https://arxiv.org/pdf/2410.19552
OA Status: green
Related Works: 10
OpenAlex ID: https://openalex.org/W4404312161

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DOI: https://doi.org/10.48550/arxiv.2410.19552

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Title: GeoLLaVA: Efficient Fine-Tuned Vision-Language Models for Temporal Change Detection in Remote Sensing

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Type: preprint

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Language: en

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Publication year: 2024

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Publication date: 2024-10-25

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Authors: Hosam Elgendy, Ahmed Sharshar, Ahmed Aboeitta, Yasser Ashraf, Mohsen Guizani

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Landing page: https://arxiv.org/abs/2410.19552

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PDF URL: https://arxiv.org/pdf/2410.19552

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OA URL: https://arxiv.org/pdf/2410.19552

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Concepts: Change detection, Computer science, Remote sensing, Computer vision, Artificial intelligence, Geography

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Cited by: 0

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Related works (count): 10

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