Availability of very high-resolution remote sensing images and advancement of deep
learning methods have shifted the paradigm of image classification from pixel-based and …

Semantic segmentation of remote sensing imagery has been employed in many
applications and is a key research topic for decades. With the success of deep learning …

Deep learning architectures have received much attention in recent years demonstrating
state-of-the-art performance in several segmentation, classification and other computer …

This paper describes a deep learning approach to semantic segmentation of very high
resolution (aerial) images. Deep neural architectures hold the promise of end-to-end …

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

Semantic segmentation plays an important role in remote sensing image understanding.
Great progress has been made in this area with the development of Deep Convolutional …

Semantic segmentation of remote sensing images plays an important role in a wide range of
applications, including land resource management, biosphere monitoring, and urban …

This paper deals with semantic segmentation of high-resolution (aerial) images where a
semantic class label is assigned to each pixel via supervised classification as a basis for …

Scene understanding is an important task in information extraction from high-resolution
aerial images, an operation which is often involved in remote sensing applications …

Image semantic segmentation is more and more being of interest for computer vision and
machine learning researchers. Many applications on the rise need accurate and efficient …