Although deep learning-based methods for semantic segmentation have achieved
prominent performance in the general image domain, semantic segmentation for high …

When approaching the semantic segmentation of overhead imagery in the decimeter spatial
resolution range, successful strategies usually combine powerful methods to learn the visual …

Semantic segmentation (SS) partitions an image into several coherent semantically
meaningful parts and classifies each part into one of the pre-determined classes. In this …

Semantic segmentation of high-resolution aerial images is of great importance in certain
fields, but the increasing spatial resolution brings large intra-class variance and small inter …

Semantic segmentation in aerial images has become an indispensable part in remote
sensing image understanding for its extensive application prospects. It is crucial to jointly …

Semantic segmentation technology is widely used in autonomous driving, environment
perception, and other fields. With the development of deep learning technology, semantic …

Satellite image semantic segmentation, including extracting road, detecting building, and
identifying land cover types, is essential for sustainable development, agriculture, forestry …

Training semantic segmentation models on geospatial data presents challenges due to
limited labeled data and prolonged training times. A very common practice in deploying …

Errors in semantic segmentation could be classified into two types: the large area
misclassification and inaccurate local boundaries. Previously attention-based methods …

With the considerable advancement of remote sensing technology and computer vision,
automatic scene understanding for very high-resolution aerial (VHR) imagery became a …