Disaster management (DM) is one of the leading fields that deal with the humanitarian
aspects of emergencies. The field has attracted researchers because of its ever-increasing …

Deep learning techniques through semantic segmentation networks have been widely used
for natural disaster analysis and response. The underlying base of these implementations …

Data-driven machine-learning algorithms generally suffer from a lack of labelled health-state
data, mainly those referring to damage conditions. To address such an issue, population …

The accurate extraction of building damage after destructive natural disasters is critical for
disaster rescue and assessment. To achieve a rapid disaster response, training a model …

Researchers and engineers have increasingly used Deep Learning (DL) for a variety of
Remote Sensing (RS) tasks. However, data from local observations or via ground truth is …

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

Integrated with remote sensing technology, deep learning has been increasingly used for
rapid damage assessment. Despite reportedly having high accuracy, the approach requires …

The accurate and timely identification of the degree of building damage is critical for disaster
emergency response and loss assessment. Although many methods have been proposed …

When natural disasters occur, timely and accurate building damage assessment maps are
vital for disaster management responders to organize their resources efficiently. Pairs of pre …

Although several past studies proposed deep learning methods to extract pre-disaster
building footprints for post-disaster management using remote sensing techniques, building …