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

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arxiv 2410.19552 v2 pith:RIL5RORU submitted 2024-10-25 cs.CV

Hosam Elgendy , Ahmed Sharshar , Ahmed Aboeitta , Yasser Ashraf , Mohsen Guizani This is my paper
classification cs.CV
keywords temporalmodelsremotesensingchangesgeographicallikelora
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Cited by 3 Pith papers

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