scholarly journals Aerial LiDAR Data Augmentation for Direct Point-Cloud Visualisation

Sensors ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 2089
Author(s):  
Ciril Bohak ◽  
Matej Slemenik ◽  
Jaka Kordež ◽  
Matija Marolt
Keyword(s):  
Point Cloud ◽  
Data Augmentation ◽  
Lidar Data ◽  
Cloud Processing ◽  
Processing Pipeline ◽  
Point Cloud Processing ◽  
Water Surfaces ◽  
Building Walls ◽  
Acquisition Technique

Direct point-cloud visualisation is a common approach for visualising large datasets of aerial terrain LiDAR scans. However, because of the limitations of the acquisition technique, such visualisations often lack the desired visual appeal and quality, mostly because certain types of objects are incomplete or entirely missing (e.g., missing water surfaces, missing building walls and missing parts of the terrain). To improve the quality of direct LiDAR point-cloud rendering, we present a point-cloud processing pipeline that uses data fusion to augment the data with additional points on water surfaces, building walls and terrain through the use of vector maps of water surfaces and building outlines. In the last step of the pipeline, we also add colour information, and calculate point normals for illumination of individual points to make the final visualisation more visually appealing. We evaluate our approach on several parts of the Slovenian LiDAR dataset.

scholarly journals Airborne LiDAR Point Cloud Processing for Archaeology. Pipeline and QGIS Toolbox

2021 ◽  
Vol 13 (16) ◽  
pp. 3225
Author(s):  
Benjamin Štular ◽  
Stefan Eichert ◽  
Edisa Lozić
Keyword(s):  
Data Processing ◽  
Point Cloud ◽  
State Of The Art ◽  
Airborne Lidar ◽  
Reproducible Research ◽  
Lidar Data ◽  
Archaeological Prospection ◽  
Cloud Processing ◽  
Point Cloud Processing ◽  
Airborne Lidar Data

The use of topographic airborne LiDAR data has become an essential part of archaeological prospection. However, as a step towards theoretically aware, impactful, and reproducible research, a more rigorous and transparent method of data processing is required. To this end, we set out to create a processing pipeline for archaeology-specific point cloud processing and derivation of products that are optimized for general-purpose data. The proposed pipeline improves on ground and building point cloud classification. The main area of innovation in the proposed pipeline is raster grid interpolation. We have improved the state-of-the-art by introducing a hybrid interpolation technique that combines inverse distance weighting with a triangulated irregular network with linear interpolation. State-of-the-art solutions for enhanced visualizations are included and essential metadata and paradata are also generated. In addition, we have introduced a QGIS plug-in that implements the pipeline as a one-step process. It reduces the manual workload by 75 to 90 percent and requires no special skills other than a general familiarity with the QGIS environment. It is intended that the pipeline and tool will contribute to the white-boxing of archaeology-specific airborne LiDAR data processing. In discussion, the role of data processing in the knowledge production process is explored.

scholarly journals COMPARING MULTI-SOURCE PHOTOGRAMMETRIC DATA DURING THE EXAMINATION OF VERTICALITY IN A MONUMENTAL TOWER

2017 ◽  
Vol XLII-2/W3 ◽  
pp. 475-480
Author(s):  
J. Markiewicz ◽  
D. Zawieska ◽  
P. Podlasiak
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Point Cloud ◽  
Laser Scanning ◽  
Terrestrial Laser Scanning ◽  
Point Clouds ◽  
Cloud Processing ◽  
Point Cloud Processing ◽  
Aerial Vehicles ◽  
Geometric Quality

This paper presents an analysis of source photogrammetric data in relation to the examination of verticality in a monumental tower. In the proposed data processing methodology, the geometric quality of the point clouds relating to the monumental tower of the castle in Iłżawas established by using terrestrial laser scanning (Z+F 5006h, Leica C10), terrestrial photographs and digital images sourced via unmanned aerial vehicles (UAV) (Leica Aibot X6 Hexacopter). Tests were performed using the original software, developed by the authors, which allows for the automation of 3D point cloud processing. The software also facilitates the verification of the verticality of the tower and the assessment of the quality of utilized data.

scholarly journals A SERVICE-ORIENTED INDOOR POINT CLOUD PROCESSING PIPELINE

2019 ◽  
Vol XLII-2/W17 ◽  
pp. 339-346
Author(s):  
V. Stojanovic ◽  
M. Trapp ◽  
R. Richter ◽  
J. Döllner
Keyword(s):  
Built Environment ◽  
Mobile Devices ◽  
Point Cloud ◽  
Point Clouds ◽  
Semantic Enrichment ◽  
Cloud Processing ◽  
Processing Pipeline ◽  
3D Point Clouds ◽  
Point Cloud Processing ◽  
Service Oriented

Abstract. Visualization of point clouds plays an important role in understanding the context of the digital representation of the built environment. Modern commodity mobile devices (e.g., smartphones and tablets), are capable of capturing representations in the form of 3D point clouds, with their depth-sensing and photogrammetry capabilities. Points clouds enable the encoding of important spatial and physical features of the built environment they represent. However, once captured, point clouds need to be processed before they can be used for further semantic enrichment and decision making. An integrated pipeline for such processes is crucial for use in larger and more complex enterprise systems and data analysis platforms, especially within the realm of Facility Management (FM) and Real Estate 4.0. We present and discuss a prototypical implementation for a service-oriented point cloud processing pipeline. The presented processing features focus on detecting and visualizing spatial deviations between as-is versus as-designed representations. We discuss the design and implementation of these processing features, and present experimental results. The presented approach can be used as a lightweight software component for processing indoor point clouds captured using commodity mobile devices, as well as primary deviation analysis, and also provides a processing link for further semantic enrichment of base-data for Building Information Modeling (BIM) and Digital Twin (DT) applications.

scholarly journals EFFICIENT LIDAR POINT CLOUD DATA MANAGING AND PROCESSING IN A HADOOP-BASED DISTRIBUTED FRAMEWORK

2017 ◽  
Vol IV-4/W2 ◽  
pp. 121-124 ◽  
Author(s):  
C. Wang ◽  
F. Hu ◽  
D. Sha ◽  
X. Han
Keyword(s):  
Point Cloud ◽  
Lidar Data ◽  
City Management ◽  
Point Cloud Data ◽  
Cloud Data ◽  
Cloud Processing ◽  
Point Cloud Processing ◽  
Distributed Framework ◽  
3D Lidar ◽  
Parallel Fashion

Light Detection and Ranging (LiDAR) is one of the most promising technologies in surveying and mapping，city management, forestry, object recognition, computer vision engineer and others. However, it is challenging to efficiently storage, query and analyze the high-resolution 3D LiDAR data due to its volume and complexity. In order to improve the productivity of Lidar data processing, this study proposes a Hadoop-based framework to efficiently manage and process LiDAR data in a distributed and parallel manner, which takes advantage of Hadoop’s storage and computing ability. At the same time, the Point Cloud Library (PCL), an open-source project for 2D/3D image and point cloud processing, is integrated with HDFS and MapReduce to conduct the Lidar data analysis algorithms provided by PCL in a parallel fashion. The experiment results show that the proposed framework can efficiently manage and process big LiDAR data.

scholarly journals An Efficient and General Framework for Aerial Point Cloud Classification in Urban Scenarios

2021 ◽  
Vol 13 (10) ◽  
pp. 1985
Author(s):  
Emre Özdemir ◽  
Fabio Remondino ◽  
Alessandro Golkar
Keyword(s):  
Point Cloud ◽  
State Of The Art ◽  
Computational Power ◽  
Specific Data ◽  
Cloud Processing ◽  
Point Cloud Processing ◽  
Current State ◽  
Data Source ◽  
Point Cloud Classification ◽  
And Training

With recent advances in technologies, deep learning is being applied more and more to different tasks. In particular, point cloud processing and classification have been studied for a while now, with various methods developed. Some of the available classification approaches are based on specific data source, like LiDAR, while others are focused on specific scenarios, like indoor. A general major issue is the computational efficiency (in terms of power consumption, memory requirement, and training/inference time). In this study, we propose an efficient framework (named TONIC) that can work with any kind of aerial data source (LiDAR or photogrammetry) and does not require high computational power while achieving accuracy on par with the current state of the art methods. We also test our framework for its generalization ability, showing capabilities to learn from one dataset and predict on unseen aerial scenarios.

scholarly journals Automatic Processing of Aerial LiDAR Data to Detect Vegetation Continuity in the Surroundings of Roads

2020 ◽  
Vol 12 (10) ◽  
pp. 1677 ◽  
Author(s):  
Ana Novo ◽  
Noelia Fariñas-Álvarez ◽  
Joaquin Martínez-Sánchez ◽  
Higinio González-Jorge ◽  
Henrique Lorenzo
Keyword(s):  
Point Cloud ◽  
Point Clouds ◽  
Light Detection And Ranging ◽  
Applicable Law ◽  
Cloud Processing ◽  
Point Cloud Processing ◽  
Preventive Actions ◽  
Total Study Area ◽  
Processing Techniques ◽  
Flammable Material

The optimization of forest management in the surroundings of roads is a necessary task in term of wildfire prevention and the mitigation of their effects. One of the reasons why a forest fire spreads is the presence of contiguous flammable material, both horizontally and vertically and, thus, vegetation management becomes essential in preventive actions. This work presents a methodology to detect the continuity of vegetation based on aerial Light Detection and Ranging (LiDAR) point clouds, in combination with point cloud processing techniques. Horizontal continuity is determined by calculating Cover Canopy Fraction (CCF). The results obtained show 50% of shrubs presence and 33% of trees presence in the selected case of study, with an error of 5.71%. Regarding vertical continuity, a forest structure composed of a single stratum represents 81% of the zone. In addition, the vegetation located in areas around the roads were mapped, taking into consideration the distances established in the applicable law. Analyses show that risky areas range from a total of 0.12 ha in a 2 m buffer and 0.48 ha in a 10 m buffer, representing a 2.4% and 9.5% of the total study area, respectively.

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