Suitability of Different LIDAR Data Sets for 3D Mapping of the Road Environment

2009 ◽  
Vol 2009 (2) ◽  
pp. 117-127
Author(s):  
Friederike Schwarzbach
Keyword(s):  
Lidar Data ◽  
Data Sets ◽  
3D Mapping ◽  
The Road

scholarly journals Segment-Based Approach for Assessing Hazard Risk of Coastal Highways in Hawai‘i

2019 ◽  
Vol 2673 (1) ◽  
pp. 83-91 ◽  
Author(s):  
Harrison Togia ◽  
Oceana P. Francis ◽  
Karl Kim ◽  
Guohui Zhang
Keyword(s):  
Qualitative Method ◽  
Data Sets ◽  
Regional Environment ◽  
The Road ◽  
Hazard Exposure ◽  
Multiple Indicators ◽  
System Data ◽  
Rural Highway ◽  
Geographic Information System Data

Hazards to roadways and travelers can be drastically different because hazards are largely dependent on the regional environment and climate. This paper describes the development of a qualitative method for assessing infrastructure importance and hazard exposure for rural highway segments in Hawai‘i under different conditions. Multiple indicators of roadway importance are considered, including traffic volume, population served, accessibility, connectivity, reliability, land use, and roadway connection to critical infrastructures, such as hospitals and police stations. The method of evaluating roadway hazards and importance can be tailored to fit different regional hazard scenarios. It assimilates data from diverse sources to estimate risks of disruption. A case study for Highway HI83 in Hawai‘i, which is exposed to multiple hazards, is conducted. Weakening of the road by coastal erosion, inundation from sea level rise, and rockfall hazards require adaptation solutions. By analyzing the risk of disruption to highway segments, adaptation approaches can be prioritized. Using readily available geographic information system data sets for the exposure and impacts of potential hazards, this method could be adapted not only for emergency management but also for planning, design, and engineering of resilient highways.

scholarly journals Using Machine Learning Methods to Identify Particle Types from Doppler Lidar Measurements in Iceland

2021 ◽  
Vol 13 (13) ◽  
pp. 2433
Author(s):  
Shu Yang ◽  
Fengchao Peng ◽  
Sibylle von Löwis ◽  
Guðrún Nína Petersen ◽  
David Christian Finger
Keyword(s):  
Machine Learning ◽  
Weather Conditions ◽  
Dust Storms ◽  
Machine Learning Algorithms ◽  
Lidar Data ◽  
Data Sets ◽  
Doppler Lidar ◽  
Lidar Measurements ◽  
Using Data ◽  
Filter Noise

Doppler lidars are used worldwide for wind monitoring and recently also for the detection of aerosols. Automatic algorithms that classify the lidar signals retrieved from lidar measurements are very useful for the users. In this study, we explore the value of machine learning to classify backscattered signals from Doppler lidars using data from Iceland. We combined supervised and unsupervised machine learning algorithms with conventional lidar data processing methods and trained two models to filter noise signals and classify Doppler lidar observations into different classes, including clouds, aerosols and rain. The results reveal a high accuracy for noise identification and aerosols and clouds classification. However, precipitation detection is underestimated. The method was tested on data sets from two instruments during different weather conditions, including three dust storms during the summer of 2019. Our results reveal that this method can provide an efficient, accurate and real-time classification of lidar measurements. Accordingly, we conclude that machine learning can open new opportunities for lidar data end-users, such as aviation safety operators, to monitor dust in the vicinity of airports.

scholarly journals ASSESSING LIDAR TRAINING DATA QUANTITIES FOR CLASSIFICATION MODELS

2021 ◽  
Vol XLVI-4/W4-2021 ◽  
pp. 101-106
Author(s):  
O. Majgaonkar ◽  
K. Panchal ◽  
D. Laefer ◽  
M. Stanley ◽  
Y. Zaki
Keyword(s):  
Point Cloud ◽  
Classification Performance ◽  
Training Data ◽  
Lidar Data ◽  
Data Sets ◽  
Classification Models ◽  
Fundamental Properties ◽  
Point Cloud Classification ◽  
The Impact ◽  
Insight Into

Abstract. Classifying objects within aerial Light Detection and Ranging (LiDAR) data is an essential task to which machine learning (ML) is applied increasingly. ML has been shown to be more effective on LiDAR than imagery for classification, but most efforts have focused on imagery because of the challenges presented by LiDAR data. LiDAR datasets are of higher dimensionality, discontinuous, heterogenous, spatially incomplete, and often scarce. As such, there has been little examination into the fundamental properties of the training data required for acceptable performance of classification models tailored for LiDAR data. The quantity of training data is one such crucial property, because training on different sizes of data provides insight into a model’s performance with differing data sets. This paper assesses the impact of training data size on the accuracy of PointNet, a widely used ML approach for point cloud classification. Subsets of ModelNet ranging from 40 to 9,843 objects were validated on a test set of 400 objects. Accuracy improved logarithmically; decelerating from 45 objects onwards, it slowed significantly at a training size of 2,000 objects, corresponding to 20,000,000 points. This work contributes to the theoretical foundation for development of LiDAR-focused models by establishing a learning curve, suggesting the minimum quantity of manually labelled data necessary for satisfactory classification performance and providing a path for further analysis of the effects of modifying training data characteristics.

scholarly journals Records in contexts: the road of archives to semantic interoperability

2017 ◽  
Vol 51 (4) ◽  
pp. 387-405 ◽  
Author(s):  
Dunia Llanes-Padrón ◽  
Juan-Antonio Pastor-Sánchez
Keyword(s):  
Semantic Web ◽  
Conceptual Model ◽  
Semantic Interoperability ◽  
Data Sets ◽  
Semantic Web Technologies ◽  
Content Type ◽  
Web Technologies ◽  
The Road ◽  
Owl Ontology

Purpose The purpose of this paper is to examine the Records in Contexts proposal of a conceptual model (RiC-CM) from the International Council on Archives’ (ICA) archival description and to propose an OWL ontology for its implementation in the semantic web. Design/methodology/approach The various elements of the model are studied and are related to earlier norms in order to understand their structure and the modeling of the ontology. Findings The analysis reveals the integrating nature of RiC-CM and the possibilities it offers for greater interoperability of data from archival descriptions. Two versions of an OWL ontology were developed to represent the conceptual model. The first makes a direct transposition of the conceptual model; the second optimizes the properties and relations in order to simplify the use and maintenance of the ontology. Research limitations/implications The proposed ontology will follow the considerations of the final version of the ICA’s RiC-CM. Practical implications The analysis affords an understanding of the role of RiC-CM in publishing online archival data sets, while the ontology is an initial approach to the semantic web technologies involved. Originality/value This paper offers an overview of Records in Contexts with respect to the advantages in the field of semantic interoperability, and supposes the first proposal of an ontology based on the conceptual model.

scholarly journals ROAD SURFACE DETECTION FROM MOBILE LIDAR DATA

2018 ◽  
Vol IV-5 ◽  
pp. 95-101 ◽  
Author(s):  
M. Yadav ◽  
B. Lohani ◽  
A. K. Singh
Keyword(s):  
Point Cloud ◽  
Three Dimensional ◽  
Test Site ◽  
Road Surface ◽  
Lidar Data ◽  
Rural Roads ◽  
The Road ◽  
Surface Detection ◽  
Rural Road ◽  
Data Points

<p><strong>Abstract.</strong> The accurate three-dimensional road surface information is highly useful for health assessment and maintenance of roads. It is basic information for further analysis in several applications including road surface settlement, pavement condition assessment and slope collapse. Mobile LiDAR system (MLS) is frequently used now a days to collect detail road surface and its surrounding information in terms three-dimensional (3D) point cloud. Extraction of road surface from volumetric point cloud data is still in infancy stage because of heavy data processing requirement and the complexity in the road environment. The extraction of roads especially rural road, where road-curb is not present is very tedious job especially in Indian roadway settings. Only a few studies are available, and none for Indian roads, in the literature for rural road detection. The limitations of existing studies are in terms of their lower accuracy, very slow speed of data processing and detection of other objects having similar characteristics as the road surface. A fast and accurate method is proposed for LiDAR data points of road surface detection, keeping in mind the essence of road surface extraction especially for Indian rural roads. The Mobile LiDAR data in <i>XYZI</i> format is used as input in the proposed method. First square gridding is performed and ground points are roughly extracted. Then planar surface detection using mathematical framework of principal component analysis (PCA) is performed and further road surface points are detected using similarity in intensity and height difference of road surface pointe in their neighbourhood.</p><p>A case study was performed on the MLS data points captured along wide-street (two-lane road without curb) of 156<span class="thinspace"></span>m length along rural roadway site in the outskirt of Bengaluru city (South-West of India). The proposed algorithm was implemented on the MLS data of test site and its performance was evaluated it terms of recall, precision and overall accuracy that were 95.27%, 98.85% and 94.23%, respectively. The algorithm was found computationally time efficient. A 7.6 million MLS data points of size 27.1<span class="thinspace"></span>MB from test site were processed in 24 minutes using the available computational resources. The proposed method is found to work even for worst case scenarios, i.e., complex road environments and rural roads, where road boundary is not clear and generally merged with road-side features.</p>

scholarly journals A Hybrid GWR-Based Height Estimation Method for Building Detection in Urban Environments

2014 ◽  
Vol II-2 ◽  
pp. 23-29 ◽  
Author(s):  
X. Wei ◽  
X. Yao
Keyword(s):  
Urban Areas ◽  
Estimation Method ◽  
Urban Environments ◽  
Lidar Data ◽  
Building Detection ◽  
Building Roof ◽  
Aerial Photos ◽  
The Road ◽  
Urban Modelling ◽  
Gwr Model

LiDAR has become important data sources in urban modelling. Traditional methods of LiDAR data processing for building detection require high spatial resolution data and sophisticated methods. The aerial photos, on the other hand, provide continuous spectral information of buildings. But the segmentation of the aerial photos cannot distinguish between the road surfaces and the building roof. This paper develops a geographically weighted regression (GWR)-based method to identify buildings. The method integrates characteristics derived from the sparse LiDAR data and from aerial photos. In the GWR model, LiDAR data provide the height information of spatial objects which is the dependent variable, while the brightness values from multiple bands of the aerial photo serve as the independent variables. The proposed method can thus estimate the height at each pixel from values of its surrounding pixels with consideration of the distances between the pixels and similarities between their brightness values. Clusters of contiguous pixels with higher estimated height values distinguish themselves from surrounding roads or other surfaces. A case study is conducted to evaluate the performance of the proposed method. It is found that the accuracy of the proposed hybrid method is better than those by image classification of aerial photos along or by height extraction of LiDAR data alone. We argue that this simple and effective method can be very useful for automatic detection of buildings in urban areas.

scholarly journals EARLINET Single Calculus Chain – technical – Part 1: Pre-processing of raw lidar data

2015 ◽  
Vol 8 (10) ◽  
pp. 10387-10428 ◽  
Author(s):  
G. D'Amico ◽  
A. Amodeo ◽  
I. Mattis ◽  
V. Freudenthaler ◽  
G. Pappalardo
Keyword(s):  
Signal To Noise Ratio ◽  
Time Integration ◽  
Synthetic Data ◽  
Added Value ◽  
Lidar Data ◽  
Data Sets ◽  
Dead Time Correction ◽  
Time Correction ◽  
Automatic Tool ◽  
Delay Correction

Abstract. In this paper we describe an automatic tool for the pre-processing of lidar data called ELPP (EARLINET Lidar Pre-Processor). It is one of two calculus modules of the EARLINET Single Calculus Chain (SCC), the automatic tool for the analysis of EARLINET data. The ELPP is an open source module that executes instrumental corrections and data handling of the raw lidar signals, making the lidar data ready to be processed by the optical retrieval algorithms. According to the specific lidar configuration, the ELPP automatically performs dead-time correction, atmospheric and electronic background subtraction, gluing of lidar signals, and trigger-delay correction. Moreover, the signal-to-noise ratio of the pre-processed signals can be improved by means of configurable time integration of the raw signals and/or spatial smoothing. The ELPP delivers the statistical uncertainties of the final products by means of error propagation or Monte Carlo simulations. During the development of the ELPP module, particular attention has been payed to make the tool flexible enough to handle all lidar configurations currently used within the EARLINET community. Moreover, it has been designed in a modular way to allow an easy extension to lidar configurations not yet implemented. The primary goal of the ELPP module is to enable the application of quality-assured procedures in the lidar data analysis starting from the raw lidar data. This provides the added value of full traceability of each delivered lidar product. Several tests have been performed to check the proper functioning of the ELPP module. The whole SCC has been tested with the same synthetic data sets, which were used for the EARLINET algorithm inter-comparison exercise. The ELPP module has been successfully employed for the automatic near-real-time pre-processing of the raw lidar data measured during several EARLINET inter-comparison campaigns as well as during intense field campaigns.

scholarly journals Comparison of Results Obtained by Two Methods of Surface Mapping

2020 ◽  
Vol 16 (1) ◽  
pp. 1-10
Author(s):  
Jozef Melcer ◽  
Eva Merčiaková ◽  
Peter Pisca
Keyword(s):  
Probability Theory ◽  
Primary Source ◽  
Data Sets ◽  
Surface Mapping ◽  
Kinematic Excitation ◽  
The Road ◽  
Comparison Of Results ◽  
Statistics And Probability ◽  
Important Input ◽  
On The Road

AbstractConsidering that the unevenness of the road surface is the primary source of the kinematic excitation of the vehicle, it is necessary to map the unevenness, and then to describe it mathematically. The data sets thus obtained represent an important input for numerical simulations of the motion of vehicles on the road. This paper deals with the analysis and comparison of results from two methods of mapping the surface of the road - exact levelling and spatial scanning. The obtained results are evaluated qualitatively and quantitatively by methods of mathematical statistics and probability theory.

scholarly journals MULTI-SCALE BASED EXTRACION OF VEGETATION FROM TERRESTRIAL LiDAR DATA FOR ASSESSING LOCAL LANDSCAPE

2015 ◽  
Vol II-3/W4 ◽  
pp. 263-270 ◽  
Author(s):  
T. Wakita ◽  
J. Susaki
Keyword(s):  
Urban Areas ◽  
Three Dimensional ◽  
Point Clouds ◽  
Lidar Data ◽  
Data Sets ◽  
Landscape Index ◽  
Terrestrial Lidar ◽  
Multi Scale ◽  
3D Point Clouds ◽  
Set Of Points

In this study, we propose a method to accurately extract vegetation from terrestrial three-dimensional (3D) point clouds for estimating landscape index in urban areas. Extraction of vegetation in urban areas is challenging because the light returned by vegetation does not show as clear patterns as man-made objects and because urban areas may have various objects to discriminate vegetation from. The proposed method takes a multi-scale voxel approach to effectively extract different types of vegetation in complex urban areas. With two different voxel sizes, a process is repeated that calculates the eigenvalues of the planar surface using a set of points, classifies voxels using the approximate curvature of the voxel of interest derived from the eigenvalues, and examines the connectivity of the valid voxels. We applied the proposed method to two data sets measured in a residential area in Kyoto, Japan. The validation results were acceptable, with F-measures of approximately 95% and 92%. It was also demonstrated that several types of vegetation were successfully extracted by the proposed method whereas the occluded vegetation were omitted. We conclude that the proposed method is suitable for extracting vegetation in urban areas from terrestrial light detection and ranging (LiDAR) data. In future, the proposed method will be applied to mobile LiDAR data and the performance of the method against lower density of point clouds will be examined.

scholarly journals AUTOMATIC DETECTION OF FOREST-ROAD DISTANCES TO IMPROVE CLEARING OPERATIONS IN ROAD MANAGEMENT

2019 ◽  
Vol XLII-2/W13 ◽  
pp. 1083-1088 ◽  
Author(s):  
A. Novo ◽  
H. González-Jorge ◽  
J. Martínez-Sánchez ◽  
L. M. González-de Santos ◽  
H. Lorenzo
Keyword(s):  
Forest Management ◽  
Forest Fires ◽  
Sustainable Forest Management ◽  
Lidar Data ◽  
Escape Route ◽  
Forest Road ◽  
The Road ◽  
Complex Relation ◽  
Road Management ◽  
Management Optimization

<p><strong>Abstract.</strong> There is a complex relation between roads and fires. Several major wildfires were ignited near to roads (Morrison 2007) and how they progressed is an important role to understand the importance to forest management in this environment. Nowadays, a sustainable forest management is necessary both for environment and politics. One of the reasons of road management is that these infrastructures provide an effective firewall in case of forest fires and an escape route for the population. Forest management optimization in road surroundings would improve wildfires prevention and mitigate their effects. One of the main indicators of road safety is the distance between road and vegetation.</p><p>The aim of this work is to develop a methodology to determine what areas do not obey current laws about safety distances between forest and roads. The acquisition of LiDAR data is done by Lynx Mobile Mapper System from University of Vigo. The methodology is automated using LiDAR data processing. The developed algorithms are based in height and length segmentation of the road. The objective is classifying vegetation groups by height and calculate the distance to the edges of road. The vegetation is divided in groups of height of 5, 10, 15 and 30&amp;thinsp;m. The minimum distance calculation is 2&amp;thinsp;m, for the vegetation of 5&amp;thinsp;m height and a maximum of 60&amp;thinsp;m for vegetation 30&amp;thinsp;m height. The height of vegetation has a directly relation with the distance separation with the road.</p>

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