Semantic labeling is an active field in remote sensing applications. Although handling high detailed objects in Very High Resolution (VHR) optical image and VHR Digital Surface Model (DSM) is a challenging task, it can improve the accuracy of semantic labeling methods. In this paper, a semantic labeling method is proposed by fusion of optical and normalized DSM data. Spectral and spatial featur...

Spaceborne very high resolution (VHR) synthetic aperture radar (SAR) images with meter and sub-meter resolution permit to extract information from urban areas at the level of individual buildings. In order to exploit this information for various application scenarios (e.g. emergency response after natural disasters), robust building detection and reconstruction methods are essential. Different ...

Deep learning (DL) is currently the dominant approach to image classification and segmentation, but performances of DL methods are remarkably influenced by quantity quality ground truth (GT) used for training. In this article, a method presented deal with semantic segmentation very-high-resolution (VHR) remote-sensing data in case scarce GT. The main idea combine specific type deep convolutiona...

OBJECTIVE We described and validated a quantitative anatomical labeling protocol for extracting clinically relevant quantitative parameters for ventral hernias (VH) from routine computed tomography (CT) scans. This information was then used to predict the need for mesh bridge closure during ventral hernia repair (VHR). METHODS A detailed anatomical labeling protocol was proposed to enable qua...

Semantic role labeling (SRL) aims to extract the arguments for each predicate in an input sentence. Traditional SRL can fail analyze dialogues because it only works on every single sentence, while ellipsis and anaphora frequently occur dialogues. To address this problem, we propose conversational task, where argument be dialogue participants, a phrase history or current As existing datasets are...

The task of semantic role labeling ( SRL ) is dedicated to finding the predicate-argument structure. Previous works on are mostly supervised and do not consider difficulty in each example which can be very expensive time-consuming. In this article, we present first neural unsupervised model for SRL. To decompose as two argument related subtasks, identification clustering, propose a pipeline tha...