Unmanned aerial vehicles (UAVs) are frequently used for aerial mapping and general
monitoring tasks. Recent progress in deep learning enabled automated semantic …

Drone semantic segmentation is a challenging task in computer vision, mainly due to
inherent complexities associated with aerial imagery. This paper presents a comprehensive …

Ultra-high resolution image segmentation poses a formidable challenge for UAVs with
limited computation resources. Moreover, with multiple deployed tasks (eg, mapping …

In recent times, semantic segmentation for VHR aerial images has become an emerging
research topic due to its widespread applications in disaster management, environmental …

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 …

Semantic segmentation for unmanned aerial vehicle (UAV) remote sensing images has
become one of the research focuses in the field of remote sensing at present, which could …

Deep learning requires large amounts of data for training models. For the task of semantic
segmentation, manual annotation is time-consuming and difficult. With the recent advances …

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 …

Semantic segmentation of remote sensing images encounters four significant difficulties: 1)
complex backgrounds, 2) large-scale differences, 3) numerous small objects, and 4) …

Semantic segmentation in aerial imagery is still an important, yet challenging task due to the
complex characteristics of remote-sensing data. The critical issues consist of: 1) extreme …