  Artificial Intelligence (XAI) has found numerous applications in computer
vision. While image classification-based explainability techniques have
garnered significant attention, their counterparts in semantic segmentation
have been relatively neglected. Given the prevalent use of image segmentation,
ranging from medical to industrial deployments, these techniques warrant a
systematic look. In this paper, we present the first comprehensive survey on
XAI in semantic image segmentation. This work focuses on techniques that were
either specifically introduced for dense prediction tasks or were extended for
them by modifying existing methods in classification. We analyze and categorize
the literature based on application categories and domains, as well as the
evaluation metrics and datasets used. We also propose a taxonomy for
interpretable semantic segmentation, and discuss potential challenges and
future research directions.
