Semantic segmentation is the pixel-wise labeling of an image. Boosted by the extraordinary
ability of convolutional neural networks (CNN) in creating semantic, high-level and …

Precisely identifying pavement cracks from charge-coupled devices (CCDs) captured high-
resolution images faces many challenges. Even though convolutional neural networks …

Semantic segmentation of remotely sensed urban scene images is required in a wide range
of practical applications, such as land cover mapping, urban change detection …

Semantic segmentation of remote sensing images plays an important role in a wide range of
applications, including land resource management, biosphere monitoring, and urban …

The fully convolutional network (FCN) with an encoder-decoder architecture has been the
standard paradigm for semantic segmentation. The encoder-decoder architecture utilizes an …

As an important carrier of human productive activities, the extraction of buildings is not only
essential for urban dynamic monitoring but also necessary for suburban construction …

The attention mechanism can refine the extracted feature maps and boost the classification
performance of the deep network, which has become an essential technique in computer …

Semantic segmentation from very fine resolution (VFR) urban scene images plays a
significant role in several application scenarios including autonomous driving, land cover …

Semantic segmentation of remotely sensed urban scene images is widely demanded in
areas such as land cover mapping, urban change detection, and environmental protection …

Semantic segmentation of remote sensing images is a fundamental task in computer vision,
with significant applications in forest and farmland cover surveys, geological disaster …