Natural disasters are public emergencies characterized by suddenness, universality, and
nonconventionality. Realizing the early warning, monitoring, and intervention of natural …

Earthquake early warning system (EEWS) plays an important role in detecting ground
shaking during an earthquake and alerting the public and authorities to take appropriate …

Disasters can have devastating impacts on communities and economies, underscoring the
urgent need for effective strategic disaster risk management (DRM). Although Artificial …

In-depth exploration of the knowledge linkages between science and technology (S&T) is an
essential prerequisite for accurately understanding the S&T innovation laws, promoting the …

Globally, communities and governments face growing challenges from an increase in
natural disasters and worsening weather extremes. Precision in disaster preparation is …

Multimodality Representation Learning, as a technique of learning to embed information
from different modalities and their correlations, has achieved remarkable success on a …

Natural disasters, such as floods, can cause significant damage to both the environment and
human life. Rapid and accurate identification of affected areas is crucial for effective disaster …

The fusion of synthetic aperture radar (SAR) and optical satellite data is widely used for
deep learning based scene classification. Counter-intuitively such neural networks are still …

We propose a novel unsupervised semantic segmentation method for fast and accurate
flood area detection utilizing color images acquired from unmanned aerial vehicles (UAVs) …

Over the past few decades, disaster assessment and management have made extensive
use of data from social media platforms. Tens of millions of tweets, either text, images, or …