Machine learning, particularly deep learning (DL), has become a central and state-of-the-art
method for several computer vision applications and remote sensing (RS) image …

Featured Application This paper presents a review of literature on urban resilience,
highlighting research gaps and suggesting solutions such as using asset and disaster risk …

Structural disaster damage detection and characterization is one of the oldest remote
sensing challenges, and the utility of virtually every type of active and passive sensor …

Digital Twins (DT) is the real-time virtual representation of systems, communities, cities, or
even human beings with the substantial potential to revolutionize post-disaster risk …

This study is conducted in accordance with a systematic literature review (SLR) protocol.
SLR is tasked with finding publications, publishers, deep learning types, enhanced and …

Automated classification of building damage in remote sensing images enables the rapid
and spatially extensive assessment of the impact of natural hazards, thus speeding up …

Hyperspectral data is a valuable source of both spectral and spatial information. However, to
enhance the classification accuracy of hyperspectral image features, it is crucial to capture …

The fourth industrial era, known as 'Industry 4.0'(I4. 0), aided and abetted by the digital
revolution, has attracted increasing attention among scholars and practitioners in the last …

Disaster risk management, and post-disaster recovery (PDR) in particular, become
increasingly important to assure resilient development. Yet, PDR is the most poorly …

In recent decades, climate change is exacerbating meteorological disasters around the
world, causing more serious urban flood disaster losses. Many solutions in related research …