Deep learning (DL) has great influence on large parts of science and increasingly
established itself as an adaptive method for new challenges in the field of Earth observation …

In this survey, we provide a comprehensive review of more than 200 articles, technical
reports, and GitHub repositories published over the last 10 years on the recent …

Using light-weight architectures or reasoning on low-resolution images, recent methods
realize very fast scene parsing, even running at more than 100 FPS on a single GPU …

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) …

As acquiring pixel-wise annotations of real-world images for semantic segmentation is a
costly process, a model can instead be trained with more accessible synthetic data and …

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 …

We explore the capability of plain Vision Transformers (ViTs) for semantic segmentation and
propose the SegViT. Previous ViT-based segmentation networks usually learn a pixel-level …

A recurrent structure is a popular framework choice for the task of video super-resolution.
The state-of-the-art method BasicVSR adopts bidirectional propagation with feature …

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 …

We study on image super-resolution (SR), which aims to recover realistic textures from a low-
resolution (LR) image. Recent progress has been made by taking high-resolution images as …