  There are two challenges presented in parsing road scenes from UAV images:
the complexity of processing high-resolution images and the dependency on
extensive manual annotations required by traditional supervised deep learning
methods to train robust and accurate models. In this paper, a novel
unsupervised road parsing framework that leverages advancements in vision
language models with fundamental computer vision techniques is introduced to
address these critical challenges. Our approach initiates with a vision
language model that efficiently processes ultra-high resolution images to
rapidly identify road regions of interest. Subsequent application of the vision
foundation model, SAM, generates masks for these regions without requiring
category information. A self-supervised learning network then processes these
masked regions to extract feature representations, which are clustered using an
unsupervised algorithm that assigns unique IDs to each feature cluster. The
masked regions are combined with the corresponding IDs to generate initial
pseudo-labels, which initiate an iterative self-training process for regular
semantic segmentation. Remarkably, the proposed method achieves a mean
Intersection over Union (mIoU) of 89.96% on the development dataset without any
manual annotation, demonstrating extraordinary flexibility by surpassing the
limitations of human-defined categories, and autonomously acquiring knowledge
of new categories from the dataset itself.
