In recent years, remote sensing technologies have played a crucial role in the detection and
management of natural disasters. In this context, deep learning models are of great …

Image semantic segmentation represents a significant area of research within the field of
computer vision. With the advent of deep learning, image semantic segmentation techniques …

Deep learning has revolutionized various fields, yet its efficacy is hindered by overfitting and
the requirement of extensive annotated data, particularly in few-shot learning scenarios …

Accurate object detection in remote sensing images is essential for applications such as
environmental monitoring, urban planning, and disaster management. However, the …

Deep learning has gained significant attention in remote sensing, especially in pixel-or
patch-level applications. Despite initial attempts to integrate deep learning into object-based …

This paper primarily studies the path planning problem for UAV formations guided by
semantic map information. Our aim is to integrate prior information from semantic maps to …

Accurate segmentation of safe navigable areas is crucial for scene parsing in autonomous
driving systems. However, existing segmentation methods often fail to fully leverage the …

The acceleration of global warming and intensifying global climate anomalies have led to a
rise in the frequency of wildfires. However, most existing research on wildfire fields focuses …

The unique challenges of polar ecosystems, coupled with the necessity for high-precision
data, make Unmanned Aerial Vehicles (UAVs) an ideal tool for vegetation monitoring and …

This study highlights the importance of unmanned aerial vehicle (UAV) multispectral (MS)
imagery for the accurate delineation and analysis of wetland ecosystems, which is crucial for …