Unlocking the potential of vision language models on satellite imagery through fine-tuning

Fine-tuning the Pixtral-12B vision-language model on satellite imagery significantly improves its performance on specialized classification tasks. The process utilizes Low-Rank Adaptation (LoRA), an efficient method that adapts model weights for specific domains without needing to retrain the entire model. In a case study classifying images from the Aerial Image Dataset, fine-tuning increased overall accuracy from 0.56 to 0.91. This approach demonstrates how adapting foundation models can unlock better performance for specialized, high-stakes applications like environmental monitoring, agriculture, and defense.