Recent advancements in computer vision and deep learning techniques have facilitated
notable progress in scene understanding, thereby assisting rescue teams in achieving …

In this paper, we present a large-scale hurricane Michael dataset for visual perception in
disaster scenarios, and analyze state-of-the-art deep neural network models for semantic …

The detrimental impacts of climate change include stronger and more destructive hurricanes
happening all over the world. Identifying different damaged structures of an area including …

Visual scene understanding is the core task in making any crucial decision in any computer
vision system. Although popular computer vision datasets like Cityscapes, MS-COCO …

In the aftermath of large-scale disasters, such as hurricanes, floods, or earthquakes,
preliminarily damage assessment (PDA) is carried out to determine the impact and …

Real-time semantic segmentation of aerial imagery is essential for unmanned ariel vehicle
applications, including military surveillance, land characterization, and disaster damage …

With the accelerated growth of the UAV industry, researchers are paying close attention to
the flight safety of UAVs. When a UAV loses its GPS signal or encounters unusual …

When a disaster strikes, accurate situational information and a fast, effective response are
critical to save lives. High resolution satellite images enable emergency responders to …

Abstract Unmanned Aerial Vehicle (UAV) has seen a dramatic rise in popularity for remote-
sensing image acquisition and analysis in recent years. It has brought promising results in …

In this paper, we study the problem of efficiently assessing building damage after natural
disasters like hurricanes, floods or fires, through aerial video analysis. We make two main …