Unsupervised semantic segmentation of high-resolution UAV imagery for
  road scene parsing
Zihan Ma Yongshang Li Ronggui Ma Chen Liang
Two challenges are presented when parsing road scenes in UAV images. First,
the high resolution of UAV images makes processing difficult. Second,
supervised deep learning methods require a large amount of manual annotations
to train robust and accurate models. In this paper, an unsupervised road
parsing framework that leverages recent advances in vision language models and
fundamental computer vision model is introduced.Initially, a vision language
model is employed to efficiently process ultra-large resolution UAV images to
quickly detect road regions of interest in the images. Subsequently, the vision
foundation model SAM is utilized to generate masks for the road regions without
category information. Following that, a self-supervised representation learning
network extracts feature representations from all masked regions. Finally, an
unsupervised clustering algorithm is applied to cluster these feature
representations and assign IDs to each 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. The proposed
method achieves an impressive 89.96% mIoU on the development dataset without
relying on any manual annotation. Particularly noteworthy is the extraordinary
flexibility of the proposed method, which even goes beyond the limitations of
human-defined categories and is able to acquire knowledge of new categories
from the dataset itself.