  Semantic segmentation of remotely sensed images plays a crucial role in
precision agriculture, environmental protection, and economic assessment. In
recent years, substantial fine-resolution remote sensing images are available
for semantic segmentation. However, due to the complicated information caused
by the increased spatial resolution, state-of-the-art deep learning algorithms
normally utilize complex network architectures for segmentation, which usually
incurs high computational complexity. Specifically, the high-caliber
performance of the convolutional neural network (CNN) heavily relies on
fine-grained spatial details (fine resolution) and sufficient contextual
information (large receptive fields), both of which trigger high computational
costs. This crucially impedes their practicability and availability in
real-world scenarios that require real-time processing. In this paper, we
propose an Attentive Bilateral Contextual Network (ABCNet), a convolutional
neural network (CNN) with double branches, with prominently lower computational
consumptions compared to the cutting-edge algorithms, while maintaining a
competitive accuracy. Code is available at https://github.com/lironui/ABCNet.
