For the epidemic prevention of avian influenza, there exist lots of differences between
ideality and reality. This is why the epidemic is usually out of control. One of the reasons is …

This article proposes a deep learning approach for small surface water recognition using
multispectral satellite imaging, which reduces the computational complexity by 18.66 times …

We introduce a novel neural network architecture—spectral encoder for sensor
independence (SEnSeI)—by which several multispectral instruments, each with different …

Nanosatellites are being widely used in various missions, including remote sensing
applications. However, the difficulty lies in mission operation due to downlink speed …

A paradigm shift is underway in Earth observation, as deep learning (DL) replaces other
methods for many predictive tasks. Nevertheless, most DL classification models for Earth …

The comprehensive understanding of outdoor scenes is a necessary requirement for a wide
variety of applications. For example, semantic segmentation enables applications such as …

Remote sensing has witnessed impressive progress of computer vision and state of art deep
learning methods on satellite imagery analysis. Image classification, semantic segmentation …

The current value-chain that caters to users of remote sensing includes multiple assets,
players and processes due to which there are time and cost inefficiencies. The usage of …

Checking cloud images conditions assume an urgent matter in climate wellbeing, mainly
when a cataclysmic event occurs. Conventional systems regularly utilize manual …

Abstract Geographic Information System (GIS) extracts data from satellite images by utilising
three fundamental cycles. Researchers are devoting a great deal of time and effort to the …