Precision Agriculture (PA) promises to boost crop productivity while reducing agricultural
costs and environmental footprints, and therefore is attracting ever-increasing interests in …

A convolutional neural network (CNN) is one of the most significant networks in the deep
learning field. Since CNN made impressive achievements in many areas, including but not …

As natural disasters are induced by geodynamic activities or abnormal changes in the
environment, geological hazards tend to wreak havoc on the environment and human …

Deep learning (DL) algorithms have seen a massive rise in popularity for remote-sensing
image analysis over the past few years. In this study, the major DL concepts pertinent to …

Recent applications of Landsat 8 Operational Land Imager (L8/OLI) and Sentinel-2
MultiSpectral Instrument (S2/MSI) data for acquiring information about land use and land …

In this paper we challenge the common assumption that convolutional layers in modern
CNNs are translation invariant. We show that CNNs can and will exploit the absolute spatial …

Currently, updating the change detection (CD) of land use/land cover (LU/LC) geospatial
information with high accuracy outcomes is important and very confusing with the different …

Analyzing land use and land cover (LULC) using remote sensing (RS) imagery is essential
for many environmental and social applications. The increase in availability of RS data has …

Abstract The integration of Deep Learning (DL) and the Internet of Things (IoT) has
revolutionized technology in the twenty-first century, enabling humans and machines to …

Agriculture is facing severe challenges from crop stresses, threatening its sustainable
development and food security. This article exploits aerial visual perception for yellow rust …