Panoramic segmentation represents a significant advancement in the field of computer vision, integrating the techniques of semantic and instance segmentation. Its applications are numerous and diverse, including but not limited to medicine, autonomous vehicles, and unmanned aircraft. At present, image panorama segmentation technology based on deep learning has become a mainstream approach and has been widely adopted in numerous fields. In this paper, a review of the research outcomes pertaining to deep learning in the domain of panoramic image segmentation is reviewed at first, and the fundamental processing techniques and associated concepts pertinent to panoramic segmentation are introduced, along with a discussion of the commonly utilized panoramic graphic segmentation datasets and evaluation metrics. Subsequently, a deep learning image panoramic segmentation model is outlined based on a network structure for classification. Furthermore, illustrative examples of their typical applications in medicine, autonomous driving, and unmanned aerial vehicles are given. Finally, existing limitations and challenges are identified, and future research directions for panoptic image segmentation are prospected.