Convolutional neural network (CNN)-based deep learning (DL) is a powerful, recently
developed image classification approach. With origins in the computer vision and image …

The synergistic combination of deep learning (DL) models and Earth observation (EO)
promises significant advances to support the Sustainable Development Goals (SDGs). New …

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 map**, urban change detection …

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

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

The applied research in remote sensing images has been pushed by convolutional neural
network (CNN). Because of the fixed size of the perceptual field, CNN is unable to model …

Deep learning-based coastline detection algorithms have begun to outshine traditional
statistical methods in recent years. However, they are usually trained only as single-purpose …

As the potential of foundation models in visual tasks has garnered significant attention,
pretraining these models before downstream tasks has become a crucial step. The three key …