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

Understand

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.