Recently, methods based on convolutional neural networks have achieved good results in
the dense prediction of remote-sensing images, particularly when employing normalized …

Abstract Domain adaptation methods are able to transfer knowledge across different
domains, tackling multi-sensor, multi-temporal or cross-regional remote sensing scenarios …

Abstract The CityGML Level of Detail 3 (LoD3), a widely adopted standard for three-
dimensional (3D) city modeling, has been accessible for an extended period. However, its …

With the development of laser scanners and machine learning, point cloud semantic
segmentation plays a significant role in autonomous driving, scene reconstruction, human …

Unsupervised domain adaptation aims to apply knowledge gained from a label-rich domain,
ie, the source domain, to a label-scare domain, ie, the target domain. However, direct …

The semantic segmentation of point clouds is a crucial undertaking in 3D reconstruction and
holds great importance. However, achieving precise semantic segmentation represents a …

The use of deep learning techniques for semantic segmentation in remote sensing has been
increasingly prevalent. Effectively modeling remote contextual information and integrating …

The convolutional neural networks (CNNs) functioning on geometric learning for the urban
large-scale 3D meshes are indispensable because of their substantial, complex, and …

Feature learning of airborne laser scanning (ALS) point clouds is challenged by both the
limited annotated samples and imbalanced class distribution. An intuitive way involves …

This article tackles the problem of requiring a large amount of data annotation in the LiDAR
point cloud semantic segmentation (PCSS) task by proposing OPOCA, a weakly supervised …