In the past few decades, most urban areas in the world have been facing the pressure of an
increasing population living in poverty. A recent study has shown that up to 80% of the …

In the remote sensing domain, it is crucial to complete semantic segmentation on the raster
images, eg, river, building, forest, etc., on raster images. A deep convolutional encoder …

This work explores the combination of free cloud computing, free open-source software, and
deep learning methods to analyze a real, large-scale problem: the automatic country-wide …

Urbanization is a substantial contributor to anthropogenic environmental change, and often
occurs at a rapid pace that demands frequent and accurate monitoring. Time series of …

The differentiation of autoimmune pancreatitis (AIP) and pancreatic ductal adenocarcinoma
(PDAC) poses a relevant diagnostic challenge and can lead to misdiagnosis and …

Remote Sensing (RS) image classification has recently attracted great attention for its
application in different tasks, including environmental monitoring, battlefield surveillance …

The focus of this paper is to bring to light the vital issue of energy poverty alleviation and
how big data could improve the data collection quality and mechanism. It also explains the …

Fully Convolutional Networks (FCN) has shown better performance than other classifiers like
Random Forest (RF), Support Vector Machine (SVM) and patch-based Deep Convolutional …

Background: Surgical treatment of Graves' disease (GD) has a potentially increased
incidence of postoperative hypoparathyroidism, recurrent laryngeal nerve palsy (RLNP) and …

Hyperspectral imaging is a rich source of data, allowing for a multitude of effective
applications. However, such imaging remains challenging because of large data dimension …