  The Agriculture-Vision Challenge at CVPR 2024 aims at leveraging semantic
segmentation models to produce pixel level semantic segmentation labels within
regions of interest for multi-modality satellite images. It is one of the most
famous and competitive challenges for global researchers to break the boundary
between computer vision and agriculture sectors. However, there is a serious
class imbalance problem in the agriculture-vision dataset, which hinders the
semantic segmentation performance. To solve this problem, firstly, we propose a
mosaic data augmentation with a rare class sampling strategy to enrich
long-tail class samples. Secondly, we employ an adaptive class weight scheme to
suppress the contribution of the common classes while increasing the ones of
rare classes. Thirdly, we propose a probability post-process to increase the
predicted value of the rare classes. Our methodology achieved a mean
Intersection over Union (mIoU) score of 0.547 on the test set, securing second
place in this challenge.
