Earthquake Disaster Assessment (EDA) plays a critical role in earthquake disaster
prevention, evacuation, and rescue efforts. Deep learning (DL), which boasts advantages in …

Recognizing dynamic variations on the ground, especially changes caused by various
natural disasters, is critical for assessing the severity of the damage and directing the …

Natural disasters pose significant threats to human life and property, exacerbated by their
sudden onset and increasing frequency. This paper conducts a comprehensive bibliometric …

This paper presents damage assessment using a hierarchical transformer architecture
(DAHiTrA), a novel deep‐learning model with hierarchical transformers to classify building …

When a natural disaster occurs, humanitarian organizations need to be prompt, effective,
and efficient to support people whose security is threatened. Satellite imagery offers rich and …

This paper presents exploratory work looking into the effectiveness of attention mechanisms
(AMs) in improving the task of building segmentation based on convolutional neural network …

Aiming at detecting COVID-19 effectively, a multiscale class residual attention (MCRA)
network is proposed via chest X-ray (CXR) image classification. First, to overcome the data …

The accurate and fast assessment of damaged buildings following a disaster is critical for
planning rescue and reconstruction efforts. The damage assessment by the traditional …

Building change detection (BCD) holds significant value in the context of monitoring land
use, whereas building damage assessment (BDA) plays a crucial role in expediting …

Nowadays, unmanned aerial vehicle (UAV) remote sensing (RS) data are key operational
sources used to produce a reliable building damage map (BDM), which is of great …