VPGCD-Net: A Visual Prompt-Driven Network for Polar Glacier Change Detection in Remote Sensing Imagery

Monitoring glacier changes is essential for understanding global climate dynamics and assessing their environmental impacts. However, accurate detection remains challenging due to seasonal variations, illumination differences, and heterogeneous textures in remote sensing imagery. To address these issues, we propose VPGCD-Net, a transformer-based dual-branch network that achieves robust glacier change detection through visual prompt engineering. The visual prompting branch integrates threshold segmentation and difference calculation, leveraging a visual prompt transformer (VPT) to encode regions of significant change and generate high-level semantic prompts. Meanwhile, the change detection branch adopts ResNet18 as the backbone to extract dual-temporal features, followed by a transformer module for modeling global spatiotemporal dependencies and a feature-wise linear modulation (FiLM) module for adaptive feature modulation to emphasize real change regions. Complementing the method, we introduce the first polar-glacier-focused dataset specifically designed for deep-learning-based glacier change detection in remote sensing. Experimental results demonstrate that VPGCD-Net outperforms existing state-of-the-art methods, achieving superior accuracy even under complex conditions such as shadow interference. The dataset is publicly available at https://huggingface.co/datasets/cuibinge/Glacier-Dataset