The production of highly accurate land cover maps is one of the primary challenges in
remote sensing, which depends on the spatial resolution of the input images. Sometimes …

Remote sensing data have become increasingly vital in target detection, disaster monitoring,
and military surveillance. Abundant pan-sharpening and super-resolution (SR) methods …

Higher resolution building mapping from lower resolution remote sensing images is in great
demand due to the lack of higher resolution data access, especially in the context of disaster …

Unsupervised domain adaptation (UDA) has become an important technique for cross-
domain semantic segmentation (SS) in the remote sensing community and obtained …

There is a growing interest in the development of automated data processing workflows that
provide reliable, high spatial resolution land cover maps. However, high-resolution remote …

Existing Super-Resolution (SR) methods are typically trained using bicubic degradation
simulations, resulting in unsatisfactory results when applied to remote sensing images that …

Choosing the proper ground sampling distance (GSD) is a vital decision in remote sensing,
which can determine the success or failure of a project. Higher resolutions may be more …

Recent advances and applications of aerial image semantic segmentation have yielded an
increase in their use in day-to-day tasks. However, state-of-art algorithms, composed mostly …

This paper presents the main ideas and initial findings of the PlasticObs+ project. The long-
term goal of the project is to develop an airborne based method for monitoring plastic waste …

Since 2017, many deep learning methods for 3-D point clouds observed by airborne LiDAR
(airborne 3-D point clouds) have been proposed. Moreover, not only a deep learning …