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

Frequent and increasingly severe natural disasters due to climate change threaten human
health and infrastructure. The provision of accurate, timely, and understandable information …

Satellite images are an extremely valuable resource in the aftermath of natural disasters
such as hurricanes and tsunamis where they can be used for risk assessment and disaster …

This study presents a new dataset of aerial imagery of natural disaster-affected areas called
AINDAA for visual display in post-disaster scenarios, particularly flood disasters, and …

Due to climate change, we can observe a recent surge of natural disasters all around the
world. These disasters are causing disastrous impact on both nature and human lives …

This study proposes a framework for improving performance and exploring the application of
Deep Convolutional Networks (DCN) using the best parameters and criteria to accurately …

Numerous natural disasters due to climate change pose major threats to the sustainability of
public infrastructure and human lives. For emergency rescue and recovery during a disaster …

Natural disasters have an economic impact, affecting buildings and infrastructures. These
impacts need to be evaluated for easier mitigation analysis. The conventional methods of …

Disaster management operations are information intensive activities due to high uncertainty
and complex information needs. Emergency response planners need to effectively plan …

The detection of natural disasters is a critical step in supporting Sustainable Development
Goal 15. In this paper, we introduce a novel segmentation method to identify the areas …