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The current building surface reconstruction from airborne LiDAR point clouds pays more attention to the relationships between roof primitives but ignores the possible existing points of walls and the structural relationships between the roofs and walls. In this paper, we propose a novel approach that reconstructs a building surface model from airborne LiDAR point clouds based on improved structural constraints. Unlike existing hypothesis-and-selected-based methods, which impose the same constraints on all relationships between planar primitives, we design a series of strong and soft structural constraints to promote the representation of geometric and structural details of both roofs and walls in the reconstructed models. To be precise, we generate a set of candidate faces with detected planar primitives of building and vertical planes inferred from the corresponding footprint to complete possibly missing walls. Then, the potential structural representations associated with building walls in the original point clouds are identified by conflict detection between the support planes both existing points of building walls and inferred from the footprint, and on this basis, improved structural constraints are constructed for the structures consisting of different type of support planes in the set of candidate faces. Finally, a compact and watertight model is extracted from the faces of the candidate set by energy minimization with proposed improved structural constraints. Experiments on the various airborne LiDAR point clouds and aerial point clouds datasets have demonstrated that the proposed method could obtain accurate and compact models with the representation of geometric and structural details between building roofs and walls compared with other state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

Accurate road network information is required to study and analyze the relationship between land usage type and land subsidence, and road extraction from remote sensing images is an important data source for updating road networks. This task has been considered a significant semantic segmentation problem, given the many road extraction methods developed for remote sensing images in recent years. Although impressive results have been achieved by classifying each pixel in the remote sensing image using a semantic segmentation network, traditional semantic segmentation methods often lack clear constraints of road features. Consequently, the geometric features of the results might deviate from actual roads, leading to issues like road fractures, rough edges, inconsistent road widths, and more, which hinder their effectiveness in road updates. This paper proposes a novel road semantic segmentation algorithm for remote sensing images based on the joint road angle prediction. By incorporating the angle prediction module and the angle feature fusion module, constraints are added to the angle features of the road. Through the angle prediction and angle feature fusion, the information contained in the remote sensing images can be better utilized. The experimental results show that the proposed method outperforms existing semantic segmentation methods in both quantitative evaluation and visual effects. Furthermore, the extracted roads were consecutive with distinct edges, making them more suitable for mapping road updates. [ABSTRACT FROM AUTHOR]