Central to the looming paradigm shift toward data-intensive science, machine-learning
techniques are becoming increasingly important. In particular, deep learning has proven to …

In recent years, deep learning (DL), a rebranding of neural networks (NNs), has risen to the
top in numerous areas, namely computer vision (CV), speech recognition, and natural …

Deep-learning (DL) algorithms, which learn the representative and discriminative features in
a hierarchical manner from the data, have recently become a hotspot in the machine …

In this work, we investigate various methods to deal with semantic labeling of very high
resolution multi-modal remote sensing data. Especially, we study how deep fully …

Very high resolution (VHR) remote sensing imagery has been used for land cover
classification, and it tends to a transition from land-use classification to pixel-level semantic …

We propose a deep Convolutional Neural Network (CNN) for land cover mapping in remote
sensing images, with a focus on urban areas. In remote sensing, class imbalance represents …

This work investigates the use of deep fully convolutional neural networks (DFCNN) for pixel-
wise scene labeling of Earth Observation images. Especially, we train a variant of the …

Recently, approaches based on fully convolutional networks (FCN) have achieved state-of-
the-art performance in the semantic segmentation of very high resolution (VHR) remotely …

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 …

In remote sensing images, the absolute orientation of objects is arbitrary. Depending on an
object's orientation and on a sensor's flight path, objects of the same semantic class can be …