  Semantic segmentation for aerial platforms has been one of the fundamental
scene understanding task for the earth observation. Most of the semantic
segmentation research focused on scenes captured in nadir view, in which
objects have relatively smaller scale variation compared with scenes captured
in oblique view. The huge scale variation of objects in oblique images limits
the performance of deep neural networks (DNN) that process images in a single
scale fashion. In order to tackle the scale variation issue, in this paper, we
propose the novel bidirectional multi-scale attention networks, which fuse
features from multiple scales bidirectionally for more adaptive and effective
feature extraction. The experiments are conducted on the UAVid2020 dataset and
have shown the effectiveness of our method. Our model achieved the
state-of-the-art (SOTA) result with a mean intersection over union (mIoU) score
of 70.80%.
