Global context information is essential for the semantic segmentation of remote sensing (RS)
images. However, most existing methods rely on a convolutional neural network (CNN) …

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 …

Large-scale vision foundation models have made significant progress in visual tasks on
natural images, with vision transformers (ViTs) being the primary choice due to their good …

Deep learning approaches have contributed to the rapid development of remote sensing
(RS) image interpretation. The most widely used training paradigm is to use ImageNet …

Deep learning approaches have shown promising results in remote sensing high spatial
resolution (HSR) land-cover mapping. However, urban and rural scenes can show …

Deep learning has largely reshaped remote sensing (RS) research for aerial image
understanding and made a great success. Nevertheless, most of the existing deep models …

Segmentation is an essential step for remote sensing image processing. This study aims to
advance the application of the Segment Anything Model (SAM), an innovative image …

The success of the Segment Anything Model (SAM) demonstrates the significance of data-
centric machine learning. However, due to the difficulties and high costs associated with …

High spatial resolution (HSR) remote sensing images contain complex foreground-
background relationships, which makes the remote sensing land cover segmentation a …

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