Uncertainty quantification (UQ) methods play a pivotal role in reducing the impact of
uncertainties during both optimization and decision making processes. They have been …

The semantic image segmentation task consists of classifying each pixel of an image into an
instance, where each instance corresponds to a class. This task is a part of the concept of …

Due to the difficulty in accessing a large amount of labeled data, semi-supervised learning is
becoming an attractive solution in medical image segmentation. To make use of unlabeled …

The deep learning being a subcategory of the machine learning follows the human instincts
of learning by example to produce accurate results. The deep learning performs training to …

We present a model-agnostic post-processing scheme to improve the boundary quality for
the segmentation result that is generated by any existing segmentation model. Motivated by …

In this paper, we present a patch-based land use and land cover classification approach
using Sentinel-2 satellite images. The Sentinel-2 satellite images are openly and freely …

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 …

Pixel-wise image segmentation is demanding task in computer vision. Classical U-Net
architectures composed of encoders and decoders are very popular for segmentation of …

Building change detection is a very important application in the field of remote sensing.
Recently, deep learning (DL) has been introduced to solve the change detection task and …

This study proposes an automatic building footprint extraction framework that consists of a
convolutional neural network (CNN)-based segmentation and an empirical polygon …