  Long-range contextual information is crucial for the semantic segmentation of
High-Resolution (HR) Remote Sensing Images (RSIs). However, image cropping
operations, commonly used for training neural networks, limit the perception of
long-range contexts in large RSIs. To overcome this limitation, we propose a
Wide-Context Network (WiCoNet) for the semantic segmentation of HR RSIs. Apart
from extracting local features with a conventional CNN, the WiCoNet has an
extra context branch to aggregate information from a larger image area.
Moreover, we introduce a Context Transformer to embed contextual information
from the context branch and selectively project it onto the local features. The
Context Transformer extends the Vision Transformer, an emerging kind of neural
network, to model the dual-branch semantic correlations. It overcomes the
locality limitation of CNNs and enables the WiCoNet to see the bigger picture
before segmenting the land-cover/land-use (LCLU) classes. Ablation studies and
comparative experiments conducted on several benchmark datasets demonstrate the
effectiveness of the proposed method. In addition, we present a new Beijing
Land-Use (BLU) dataset. This is a large-scale HR satellite dataset with
high-quality and fine-grained reference labels, which can facilitate future
studies in this field.
