Machine learning, particularly deep learning (DL), has become a central and state-of-the-art
method for several computer vision applications and remote sensing (RS) image …

Abstract Generative Adversarial Networks (GANs) are one of the most creative advances in
Deep Learning (DL) in recent years. The Remote Sensing (RS) community has adopted …

As an important carrier of human productive activities, the extraction of buildings is not only
essential for urban dynamic monitoring but also necessary for suburban construction …

The extraction of urban structures such as buildings from very high-resolution (VHR) remote
sensing imagery has improved dramatically, thanks to recent developments in deep …

Urban building segmentation is a prevalent research domain for very high resolution (VHR)
remote sensing; however, various appearances and complicated background of VHR …

Automatic building extraction from high-resolution remote sensing imagery has various
applications, such as urban planning and land use management. However, the existing …

Building extraction is a fundamental area of research in the field of remote sensing. In this
paper, we propose an efficient model called residual U-Net (RU-Net) to extract buildings. It …

Semantic segmentation of high-resolution remote sensing images plays an important role in
applications for building extraction. However, the current algorithms have some semantic …

Defects in the optical lens directly affect the scattering properties of the optical lens and
decrease the performance of the optical element. Although machine vision instead of …

Satellite images have drawn increasing interest from a wide variety of users, including
business and government, ever since their increased usage in important fields ranging from …