Searched for: subject%3A%22point%255C%252Bcloud%22
(1 - 11 of 11)
document
Bode, Lukas (author), Weinmann, M. (author), Klein, Reinhard (author)
Extracting high-level structural information from 3D point clouds is challenging but essential for tasks like urban planning or autonomous driving requiring an advanced understanding of the scene at hand. Existing approaches are still not able to produce high-quality results consistently while being fast enough to be deployed in scenarios...
journal article 2023
document
PAPALEXIOU, ANNIE (author)
Although monitoring and maintenance of railways is important to ensure safety and avoid delays and financial losses, it is still mainly based on human inspection. The complexity of a railway along with the large area it extends makes manual monitoring difficult and time-consuming. The increasing availability of 3D acquisition technologies has...
master thesis 2021
document
Widyaningrum, E. (author), Bai, Q. (author), Fajari, Marda K. (author), Lindenbergh, R.C. (author)
Classification of aerial point clouds with high accuracy is significant for many geographical applications, but not trivial as the data are massive and unstructured. In recent years, deep learning for 3D point cloud classification has been actively developed and applied, but notably for indoor scenes. In this study, we implement the point-wise...
journal article 2021
document
Alexandridis, Vasileios (author)
Bathymetric Airborne LiDAR technology is used to map the depth of water bodies. It uses a green light sensor which is able to penetrate the water surface and reach the bottom part of the interesting water areas. However, water conditions affect the capability of the green laser penetration. Factors such as the water clarity, the water turbidity ...
master thesis 2020
document
Hemmes, Tom (author)
There is a paradigm shift from two- to three-dimensional data, from maps to information dense models. Self-driving cars, digitization of historic buildings or maintenance of highway infrastructure are a small selection of many applications that use laser scanning to acquire three-dimensional data of our physical surroundings. Most of these...
master thesis 2018
document
Peters, R.Y. (author)
A geographical point cloud is a detailed three-dimensional representation of the geometry of our geographic environment. Using geographical point cloud modelling, we are able to extract valuable information from geographical point clouds that can be used for applications in asset management, crisis management, city and landscape planning, and...
doctoral thesis 2018
document
Lemmens, M.J.P.M. (author)
A knowledge-based system exploits the knowledge, which a human expert uses for completing a complex task, through a database containing decision rules, and an inference engine. Already in the early nineties knowledge-based systems have been proposed for automated image classification. Lack of success faded out initial interest and enthusiasm,...
journal article 2018
document
Sulzer, Raphael (author), Nourian, Pirouz (author), Palmieri, M. (author), van Gemert, J.C. (author)
This paper investigates automatic prediction of seismic building structural types described by the Global Earthquake Model (GEM) taxonomy, by combining remote sensing, cadastral and inspection data in a supervised machine learning approach. Our focus lies on the extraction of detailed geometric information from a point cloud gained by aerial...
conference paper 2018
document
Zheng, M. (author), Lemmens, M.J.P.M. (author), van Oosterom, P.J.M. (author)
This paper presents our work on automated classification of Mobile Laser Scanning (MLS) point clouds of urban scenes with features derived from cylinders around points of consideration. The core of our method consists of spanning up a cylinder around points and deriving features, such as reflectance, height difference, from the points present...
conference paper 2018
document
Zheng, M. (author), Lemmens, M.J.P.M. (author), van Oosterom, P.J.M. (author)
This paper focusses on the feasibility of classifiers, developed for classifying multispectral images, for assigning classes to point clouds of urban scenes. The motivation of our research is that dense point clouds require fast classification methods to extract meaningful information within a reasonable amount of time and multispectral...
conference paper 2017
document
Zheng, M. (author), Lemmens, M.J.P.M. (author), van Oosterom, P.J.M. (author)
The demand for 3D maps of cities and road networks is steadily growing and mobile laser scanning (MLS) systems are often the preferred geo-data acquisition method for capturing such scenes. Because MLS systems are mounted on cars or vans they can acquire billions of points of road scenes within a few hours of survey. Manual processing of...
conference paper 2017
Searched for: subject%3A%22point%255C%252Bcloud%22
(1 - 11 of 11)