Convolutional neural network (CNN)-based deep learning (DL) is a powerful, recently
developed image classification approach. With origins in the computer vision and image …

High-resolution remotely sensed images pose challenges to traditional semantic
segmentation networks, such as Convolutional Neural Networks (CNNs) and Vision …

The integration of visible and thermal images has demonstrated the potential ability to
enhance crack segmentation accuracy. However, due to the intricate texture of masonry …

Given the prominence of 3-D sensors in recent years, 3-D point clouds are worthy to be
further investigated for environment perception and scene understanding. Learning accurate …

Unmanned aerial systems (UASs) have recently become an affordable means to map
forests at the species level, but research into the performance of different classification …

Forests can be efficiently monitored by automatic semantic segmentation of trees using
satellite and/or aerial images. Still, several challenges can make the problem difficult …

Semantic segmentation of very high-resolution (VHR) remote sensing images is a
fundamental task for many applications. However, large variations in the scales of objects in …

Multispectral images (MSIs) are valuable for precision agriculture due to the extra spectral
information acquired compared to natural color RGB (ncRGB) images. In this paper, we thus …

Point cloud data acquired using terrestrial laser scanning (TLS) often need to be
semantically segmented to support many applications. To this end, various point-, voxel …

The incorporation of building information modeling (BIM) has brought about significant
advancements in civil engineering, enhancing efficiency and sustainability across project life …