Brain-inspired algorithms have become a new trend in next-generation artificial intelligence.
Through research on brain science, the intelligence of remote sensing algorithms can be …

Simultaneous Localisation and Mapping (SLAM) is one of the fundamental problems in
autonomous mobile robots where a robot needs to reconstruct a previously unseen …

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

The precise segmentation of medical images is one of the key challenges in pathology
research and clinical practice. However, many medical image segmentation tasks have …

Semantic segmentation of remote sensing imagery (RSI) is critical in many domains due to
the diverse landscapes and different sizes of geo-objects that RSI contains, making …

Extracting semantic information from very-high-resolution (VHR) aerial images is a
prominent topic in the Earth observation research. An increasing number of different sensor …

As a dense prediction problem, semantic segmentation consumes extensive memory and
computational resources. However, the application of semantic segmentation requires the …

Semantic segmentation based on deep learning has achieved impressive results in recent
years, but these results are supported by a large amount of labeled data, which requires …

Semantic segmentation for urban remote sensing images is one of the most-crucial tasks in
the field of remote sensing. Remote sensing images contain rich information on ground …

Boundary information is essential for the semantic segmentation of remote sensing images.
However, most existing methods were designed to establish strong contextual information …