In the realm of emergency disaster relief, it is paramount to attain a thorough comprehension
of the semantic information associated with the local disaster scene for strategic rescue path …

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 …

Efficient data collection methods play a major role in helping us better understand the Earth
and its ecosystems. In many applications, the usage of unmanned aerial vehicles (UAVs) for …

As the application of UAVs becomes more and more widespread, accidents such as
accidental injuries to personnel, property damage, and loss and destruction of UAVs due to …

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

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 …

Deep learning techniques have recently shown remarkable efficacy in the semantic
segmentation of natural and remote sensing (RS) images. However, these techniques …

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 …

Semantic segmentation is crucial for enabling autonomous flight and landing of low-altitude
unmanned aerial vehicles (UAVs) and is indispensable for various intelligent applications …

Unmanned aerial vehicle (UAV) remote sensing images used for semantic segmentation
possess distinct features compared to urban street scene images, including high resolution …