Characterization of Geomorphological Surface Activities Using Near-Continuous Terrestrial LiDAR Time Series: The classification of 4D objects-by-change

The Earth's landscapes are shaped by processes eroding, transporting, and depositing material over various timespans and spatial scales. To understand these surface activities and mitigate potential hazards they inflict, knowledge is needed of their occurrences and properties. Near-continuous terrestrial laser scanning (TLS) enables the...

Identifying high Alpine geomorphological processes using permanent laser scanning time series

Global climate change affects mountain regions such as the European Alps. Consequently, glacier extents decrease, and proglacial areas, the areas that recently lost their ice cover, increase in size. These proglacial areas are subject to a high frequency and magnitude of geomorphological activity and act as a sediment source for downstream...

Graph Based LiDAR-Inertial Lo- calization with a Low Power Solid State LiDAR

Mapping an environment with a Light Detection and Ranging (LiDAR) sensor through the use of a LiDAR Simultaneous Localization And Mapping (SLAM) algorithm is a powerful technology that allows for the creation of detailed 3D models. Recently various LiDAR sensors have been developed based on Micro-Electro-Mechanical System (MEMS) technology....

Investigation of pointnet for semantic segmentation of large-scale outdoor point clouds

Semantic segmentation of point clouds is indispensable for 3D scene understanding. Point clouds have credibility for capturing geometry of objects including shape, size, and orientation. Deep learning (DL) has been recognized as the most successful approach for image semantic segmentation. Applied to point clouds, performance of the many DL...

Disturbance analysis in the classification of objects obtained from urban lidar point clouds with convolutional neural networks

Mobile Laser Scanning (MLS) systems have proven their usefulness in the rapid and accurate acquisition of the urban environment. From the generated point clouds, street furniture can be extracted and classified without manual intervention. However, this process of acquisition and classification is not error-free, caused mainly by disturbances...

Novel approach to automatic traffic sign inventory based on mobile mapping system data and deep learning

Traffic signs are a key element in driver safety. Governments invest a great amount of resources in maintaining the traffic signs in good condition, for which a correct inventory is necessary. This work presents a novel method for mapping traffic signs based on data acquired with MMS (Mobile Mapping System): images and point clouds. On the...

Web based visualisation of 3D radar and LiDAR data

To improve the interpretation of the low density InSAR data a combination is made with high density airborne and terrestrial laser scanner data. Allowing for improvement of the positioning of the scatterers and the understanding of their behaviour. This combination is implemented as a web application, suitable for researchers to create 3D...