Building Opening Detection in Urban Point Clouds

Digital twins (DTs) are a common way for city planners and citizens alike to visualize the impact of new policy decisions, simulate scenarios, and plan for disasters. The more detail these DTs have, the more useful they can be. Windows and doors, also known as building openings, are a critical detail missing in Amsterdam’s DT. While previous...

Road type classification of MLS point clouds using deep learning

Functional classification of the road is important to the construction of sustainable transport systems and proper design of facilities. Mobile laser scanning (MLS) point clouds provide accurate and dense 3D measurements of road scenes, while their massive data volume and lack of structure also bring difficulties in processing. 3D point cloud...

Ontology-based semantic conceptualisation of historical built heritage to generate parametric structured models from point clouds

Nowadays, cultural and historical built heritage can be more effectively preserved, val-orised and documented using advanced geospatial technologies. In such a context, there is a major issue concerning the automation of the process and the extraction of useful information from a huge amount of spatial information acquired by means of...

Classification of airborne laser scanning point cloud using point-based convolutional neural network

In various applications of airborne laser scanning (ALS), the classification of the point cloud is a basic and key step. It requires assigning category labels to each point, such as ground, building or vegetation. Convolutional neural networks have achieved great success in image classification and semantic segmentation, but they cannot be...

The Hessigheim 3D (H3D) benchmark on semantic segmentation of high-resolution 3D point clouds and textured meshes from UAV LiDAR and Multi-View-Stereo

Automated semantic segmentation and object detection are of great importance in geospatial data analysis. However, supervised machine learning systems such as convolutional neural networks require large corpora of annotated training data. Especially in the geospatial domain, such datasets are quite scarce. Within this paper, we aim to alleviate...

Semantic Segmentation of AHN3 Point Clouds with DGCNN

Semantic segmentation of aerial point clouds with high accuracy is significant for many geographical applications, but is not trivial since the data is massive and unstructured. In the past few years, deep learning approaches designed for 3D point cloud data have made great progress. Pointwise neural networks, such as PointNet and its extensions...