  Semantic segmentation from very fine resolution (VFR) urban scene images
plays a significant role in several application scenarios including autonomous
driving, land cover classification, and urban planning, etc. However, the
tremendous details contained in the VFR image, especially the considerable
variations in scale and appearance of objects, severely limit the potential of
the existing deep learning approaches. Addressing such issues represents a
promising research field in the remote sensing community, which paves the way
for scene-level landscape pattern analysis and decision making. In this paper,
we propose a Bilateral Awareness Network which contains a dependency path and a
texture path to fully capture the long-range relationships and fine-grained
details in VFR images. Specifically, the dependency path is conducted based on
the ResT, a novel Transformer backbone with memory-efficient multi-head
self-attention, while the texture path is built on the stacked convolution
operation. Besides, using the linear attention mechanism, a feature aggregation
module is designed to effectively fuse the dependency features and texture
features. Extensive experiments conducted on the three large-scale urban scene
image segmentation datasets, i.e., ISPRS Vaihingen dataset, ISPRS Potsdam
dataset, and UAVid dataset, demonstrate the effectiveness of our BANet.
Specifically, a 64.6% mIoU is achieved on the UAVid dataset. Code is available
at https://github.com/WangLibo1995/GeoSeg.
