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 …

Estimating the damaged area after a forest fire is important for responding to this natural
catastrophe. With the support of aerial remote sensing, typically with unmanned aerial …

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

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 …

This paper addresses the problem of training a deep neural network for satellite image
segmentation so that it can be deployed over images whose statistics differ from those used …

The unmanned aerial vehicle (UAV)-captured panoptic remote sensing images have great
potential to promote robotics-inspired intelligent solutions for land cover mapping, disaster …

Remote sensing image segmentation is a challenging task in remote sensing image
analysis. Remote sensing image segmentation has great significance in urban planning …

Destruction caused by violent conflicts play a big role in understanding the dynamics and
consequences of conflicts, which is now the focus of a large body of ongoing literature in …

Floods are the most frequent natural disasters, occurring almost every year around the
globe. To mitigate the damage caused by a flood, it is important to timely assess the …