Due to the rapid advancement of the transportation industry and the continual increase in
pavement infrastructure, it is difficult to keep up with the huge road maintenance task by …

In this article, we deal with the problem of object detection on remote sensing images.
Previous researchers have developed numerous deep convolutional neural network (CNN) …

Point cloud has emerged as the most popular three-dimensional (3D) data format in recent
years for several scientific and industrial applications. Point cloud semantic segmentation …

The classification of airborne laser scanning (ALS) point clouds is a critical task of remote
sensing and photogrammetry fields. Although recent deep learning-based methods have …

Kernel point convolution (KPConv) can effectively represent the point features of point cloud
data. However, KPConv-based methods just consider the local information of each point …

Semantic labeling is an essential but challenging task when interpreting point clouds of 3D
scenes. As a core step for scene interpretation, semantic labeling is the task of annotating …

With the development of airborne light detection and ranging (LiDAR) technology, it has
become a common and efficient way to collect large-scale 3-D spatial information. However …

Abstract Interpretation of Airborne Laser Scanning (ALS) point clouds is a critical procedure
for producing various geo-information products like 3D city models, digital terrain models …

Interpretation of airborne laser scanning (ALS) point clouds plays a notable role in
geoinformation production. As a critical step for interpretation, accurate semantic …

Semantic segmentation of airborne laser scanning (ALS) point clouds using deep learning is
a hot research in remote sensing and photogrammetry. A current trend is to aggregate …