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

Semantic segmentation of aerial imagery is an important tool for mapping and earth
observation. However, supervised deep learning models for segmentation rely on large …

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 …

In this paper, we address the problem of adaptive path planning for accurate semantic
segmentation of terrain using unmanned aerial vehicles (UAVs). The usage of UAVs for …

Semantic segmentation enables robots to perceive and reason about their environments
beyond geometry. Most of such systems build upon deep learning approaches. As …

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 …

Unmanned Aerial Vehicles (UAVs) are the promising “Flying IoT” devices of the future, which
can be equipped with various sensors and cognitive capabilities to perform numerous tasks …

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 …

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

In recent years semantic segmentation has become a pivotal tool in processing and
interpreting satellite imagery. Yet a prevalent limitation of supervised learning techniques …