This paper introduces an entropy-centric Explainable AI (XAI) method for semantic segmentation in remote sensing imagery. It also proposes a novel XAI evaluation methodology, demonstrating superior performance compared to existing methods in identifying relevant regions for model decisions.

This paper explores the marginal benefits of pre-training and intermediate fine-tuning on a small 5M-parameter Vision Transformer for semantic segmentation. It reveals that while pre-training and fine-tuning provide benefits with diminishing returns, inappropriate intermediate fine-tuning can harm downstream performance.