Floods are among the most severe and impacting natural disasters. Their occurrence rate
and intensity have been significantly increasing worldwide in the last years due to climate …

Explosive growth in geospatial and temporal data as well as the emergence of new
technologies emphasize the need for automated discovery of spatiotemporal knowledge …

Ship classification in synthetic aperture radar (SAR) images is a fundamental and significant
step in ocean surveillance. Recently, with the rise of deep learning (DL), modern abstract …

Floods have been a major cause of destruction, instigating fatalities and massive damage to
the infrastructure and overall economy of the affected country. Flood-related devastation …

The area of disaster management receives increasing attention from multiple disciplines of
research. A key role of computer scientists has been in devising ways to manage and …

In recent years, convolutional neural networks (CNNs) have drawn considerable attention
for the analysis of synthetic aperture radar (SAR) data. In this study, major subareas of SAR …

In this letter, a simple and effective unsupervised approach based on the combined
difference image and k-means clustering is proposed for the synthetic aperture radar (SAR) …

The main focus of this paper is the novel use of Artificial Intelligence (AI) in natural disaster,
more specifically flooding, to improve flood resilience and preparedness. Different types of …

The emerging field of combining compressed sensing (CS) and three-dimensional synthetic
aperture radar (3D SAR) imaging has shown significant potential to reduce sampling rate …

Convolutional neural network (CNN) still suffers from overfitting problems caused by limited
samples in SAR target classification. Few-shot learning (FSL) aims to learn a classifier to …