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 …

Transformer has shown excellent performance in remote sensing field with long-range
modeling capabilities. Remote sensing video (RSV) moving object detection and tracking …

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 of remotely sensed imagery plays a critical role in many real-world
applications, such as environmental change monitoring, precision agriculture …

Semantic segmentation is a fundamental but challenging problem of pixel-level remote
sensing (RS) data analysis. Semantic segmentation tasks based on aerial and satellite …

Remote sensing images pose distinct challenges for downstream tasks due to their inherent
complexity. While a considerable amount of research has been dedicated to remote sensing …

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 is an extremely challenging task in high-resolution remote sensing
(HRRS) images as objects have complex spatial layouts and enormous variations in …