scholarly journals EXTRACTION OF GROUND SURFACE ALONG ROADWAY FROM MOBILE LIDAR DATA

2018 ◽  
Vol IV-5 ◽  
pp. 103-107
Author(s):  
M. Yadav ◽  
A. K. Singh ◽  
B. Lohani
Keyword(s):  
Point Cloud ◽  
Total Error ◽  
Ground Surface ◽  
Test Site ◽  
Lidar Data ◽  
Type I ◽  
Digital Elevation ◽  
Data Points ◽  
Elevation Model ◽  
Road Widening

<p><strong>Abstract.</strong> High quality digital elevation model (DEM) is obtained from mobile LiDAR data and it is used in various applications like road widening, slope measurement of road side surfaces, and inundation of the roadway evaluation. Two steps algorithm is proposed to filter ground points using mobile LiDAR data. Initially unstructured input data is organized then standard deviation and flatness based approach is used to filter ground points. Proposed algorithm is tested on point cloud of test site located along 800<span class="thinspace"></span>m of roadway. Type I, Type II and total error are 2.11%, 2.21% and 2.15%, respectively with kappa is equal to 96.61% are computed using ground filtered points and reference data points.</p>

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 COMPARISON OF TRADITIONAL AND MACHINE LEARNING BASE METHODS FOR GROUND POINT CLOUD LABELING

2019 ◽  
Vol XLII-4/W18 ◽  
pp. 141-145
Author(s):  
S. M. Ayazi ◽  
M. Saadat Seresht
Keyword(s):  
Machine Learning ◽  
Point Cloud ◽  
Type I Error ◽  
Total Error ◽  
Training Data ◽  
Lidar Data ◽  
Type I ◽  
Type Ii ◽  
Type Ii Error ◽  
Digital Photogrammetry

Abstract. Today, a variety of methods have been proposed by researchers to distinguish ground and non-ground points in point cloud data. Most fully automated methods have a common disadvantage which is the lack of proper algorithm response for all areas and levels of the ground, so most of these algorithms have good outcomes in simple landscapes but encounter problems in complex landscapes. Point cloud filtering techniques can be divided into two general rule-based and novel methods. Today, the use of machine learning techniques has improved the results of classification, which has led to significant results, especially when data can be labelled at the presence of training data. In this paper, firstly, altimeter and radiometric features are extracted from the LiDAR data and the point cloud derived from digital photogrammetry. Then, these features are participated in a classification process using SVM learning and random forest methods, and the ground and Non-ground points are classified. The classification results using this method on LiDAR data show a total error of 6.2%, a type I error of 5.4%, and a type II error of 13.2%. The comparison of the proposed method with the results of LASTools software shows a reduction in total error and type I error (while increasing the type II error). This method was also investigated on the dense point cloud obtained from digital photogrammetry and based on this study, the total was 7.2%, the type I error was 6.8%, and the type II error was 10.9%.

scholarly journals PERFORMANCE EVALUATION OF DIFFERENT GROUND FILTERING ALGORITHMS FOR UAV-BASED POINT CLOUDS

2016 ◽  
Vol XLI-B1 ◽  
pp. 245-251 ◽  
Author(s):  
C. Serifoglu ◽  
O. Gungor ◽  
V. Yilmaz
Keyword(s):  
Point Cloud ◽  
Point Clouds ◽  
Good Alternative ◽  
Lidar Data ◽  
Filtering Algorithms ◽  
Aerial Photos ◽  
Digital Elevation ◽  
Aerial Vehicle ◽  
Elevation Model ◽  
Uav Images

Digital Elevation Model (DEM) generation is one of the leading application areas in geomatics. Since a DEM represents the bare earth surface, the very first step of generating a DEM is to separate the ground and non-ground points, which is called ground filtering. Once the point cloud is filtered, the ground points are interpolated to generate the DEM. LiDAR (Light Detection and Ranging) point clouds have been used in many applications thanks to their success in representing the objects they belong to. Hence, in the literature, various ground filtering algorithms have been reported to filter the LiDAR data. Since the LiDAR data acquisition is still a costly process, using point clouds generated from the UAV images to produce DEMs is a reasonable alternative. In this study, point clouds with three different densities were generated from the aerial photos taken from a UAV (Unmanned Aerial Vehicle) to examine the effect of point density on filtering performance. The point clouds were then filtered by means of five different ground filtering algorithms as Progressive Morphological 1D (PM1D), Progressive Morphological 2D (PM2D), Maximum Local Slope (MLS), Elevation Threshold with Expand Window (ETEW) and Adaptive TIN (ATIN). The filtering performance of each algorithm was investigated qualitatively and quantitatively. The results indicated that the ATIN and PM2D algorithms showed the best overall ground filtering performances. The MLS and ETEW algorithms were found as the least successful ones. It was concluded that the point clouds generated from the UAVs can be a good alternative for LiDAR data.

scholarly journals PERFORMANCE EVALUATION OF DIFFERENT GROUND FILTERING ALGORITHMS FOR UAV-BASED POINT CLOUDS

2016 ◽  
Vol XLI-B1 ◽  
pp. 245-251 ◽  
Author(s):  
C. Serifoglu ◽  
O. Gungor ◽  
V. Yilmaz
Keyword(s):  
Point Cloud ◽  
Point Clouds ◽  
Good Alternative ◽  
Lidar Data ◽  
Filtering Algorithms ◽  
Aerial Photos ◽  
Digital Elevation ◽  
Aerial Vehicle ◽  
Elevation Model ◽  
Uav Images

Digital Elevation Model (DEM) generation is one of the leading application areas in geomatics. Since a DEM represents the bare earth surface, the very first step of generating a DEM is to separate the ground and non-ground points, which is called ground filtering. Once the point cloud is filtered, the ground points are interpolated to generate the DEM. LiDAR (Light Detection and Ranging) point clouds have been used in many applications thanks to their success in representing the objects they belong to. Hence, in the literature, various ground filtering algorithms have been reported to filter the LiDAR data. Since the LiDAR data acquisition is still a costly process, using point clouds generated from the UAV images to produce DEMs is a reasonable alternative. In this study, point clouds with three different densities were generated from the aerial photos taken from a UAV (Unmanned Aerial Vehicle) to examine the effect of point density on filtering performance. The point clouds were then filtered by means of five different ground filtering algorithms as Progressive Morphological 1D (PM1D), Progressive Morphological 2D (PM2D), Maximum Local Slope (MLS), Elevation Threshold with Expand Window (ETEW) and Adaptive TIN (ATIN). The filtering performance of each algorithm was investigated qualitatively and quantitatively. The results indicated that the ATIN and PM2D algorithms showed the best overall ground filtering performances. The MLS and ETEW algorithms were found as the least successful ones. It was concluded that the point clouds generated from the UAVs can be a good alternative for LiDAR data.

scholarly journals Flood Potential Mapping of Pesanggrahan River Watershed in South and West Jakarta with LIDAR Data Segmentation

2018 ◽  
Author(s):  
Indra Riyanto ◽  
Lestari Margatama ◽  
S. Samsinar ◽  
Babag Purbantoro ◽  
Luhur Bayuaji ◽  
...  
Keyword(s):  
Natural Disasters ◽  
Ground Surface ◽  
Image Data ◽  
Lidar Data ◽  
Environment Quality ◽  
Surface Data ◽  
Digital Elevation ◽  
Potential Mapping ◽  
Recent Occurrence ◽  
Elevation Model

Degradation of environment quality is currently the prime cause of the recent occurrence of natural disasters; it also contributes in the increase of the area that is prone to natural disasters. This research is aimed to map the potential of areas around Pesanggrahan river in DKI Jakarta by segmenting the Digital Elevation Model derived from LIDAR data. The objective of this segmentation is to find the watershed lines of the DEM image. Data processing in this research is using LIDAR data which take the ground surface data, which is overlaid with Jakarta river map and subsequently, the data is then segmented the image. The expected result of the research is the flood potential area information, especially along the Pesanggrahan river in South Jakarta.

scholarly journals MAT: GIS-Based Morphometry Assessment Tools for Concave Landforms

2021 ◽  
Vol 13 (14) ◽  
pp. 2810
Author(s):  
Joanna Gudowicz ◽  
Renata Paluszkiewicz
Keyword(s):  
Rapid Development ◽  
Assessment Tools ◽  
Lidar Data ◽  
Automatic Extraction ◽  
Sensing Technology ◽  
Digital Elevation ◽  
Elevation Data ◽  
Elevation Model ◽  
Morphometric Assessment ◽  
Morphometric Evaluation

The rapid development of remote sensing technology for obtaining high-resolution digital elevation models (DEMs) in recent years has made them more and more widely available and has allowed them to be used for morphometric assessment of concave landforms, such as valleys, gullies, glacial cirques, sinkholes, craters, and others. The aim of this study was to develop a geographic information systems (GIS) toolbox for the automatic extraction of 26 morphometric characteristics, which include the geometry, hypsometry, and volume of concave landforms. The Morphometry Assessment Tools (MAT) toolbox in the ArcGIS software was developed. The required input data are a digital elevation model and the form boundary as a vector layer. The method was successfully tested on an example of 21 erosion-denudation valleys located in the young glacial area of northwest Poland. Calculations were based on elevation data collected in the field and LiDAR data. The results obtained with the tool showed differences in the assessment of the volume parameter at the average level of 12%, when comparing the field data and LiDAR data. The algorithm can also be applied to other types of concave forms, as well as being based on other DEM data sources, which makes it a universal tool for morphometric evaluation.

scholarly journals Reconstructing Pre-Industrial Long Distance Roads in a Hilly Region in Germany, Based on Historical and Archaeological Data

2017 ◽  
Vol 1 (2) ◽  
pp. 642-660 ◽  
Author(s):  
Irmela Herzog
Keyword(s):  
Cross Sections ◽  
Lidar Data ◽  
Long Distance ◽  
Early 19Th Century ◽  
Digital Elevation ◽  
Hilly Region ◽  
Local Accuracy ◽  
Cost Paths ◽  
Elevation Model ◽  
The One

The aim of this contribution is on the one hand to map pre-industrial long distance roads located in a hilly region east of Cologne, Germany, as exactly as possible and on the other hand to assess the accuracy of least-cost approaches that are increasingly applied by archaeologists for prehistoric road reconstruction. Probably the earliest map covering the study area east of Cologne dates back to 1575. The map is distorted so that rectification is difficult. But it is possible to assess the local accuracy of the map and to transfer the approximate routes to a modern map manually. Most of the area covered by the 1575 map is also depicted on a set of more accurate maps created in the early 19th century and a somewhat later historical map set (ca. 1842 AD). The historical roads on these rectified historical maps close to the approximate roads were digitized and compared to the outcomes of least-cost analysis, specifically least-cost paths and accessibility maps. Based on these route reconstructions with limited accuracy, Lidar data is checked to identify remains of these roads. Several approaches for visualizing Lidar data are tested to identify appropriate methods for detecting sunken roads. Possible sunken roads detected on the Lidar images were validated by checking cross sections in the digital elevation model and in the field.

scholarly journals CREATING DIGITAL ELEVATION MODEL USING A MOBILE DEVICE

2017 ◽  
Vol IV-4/W4 ◽  
pp. 203-207 ◽  
Author(s):  
A. İ. Durmaz
Keyword(s):  
Mobile Devices ◽  
Digital Elevation Model ◽  
Mobile Device ◽  
Point Clouds ◽  
Lidar Data ◽  
Government Agencies ◽  
Gps Data ◽  
Digital Elevation ◽  
The World ◽  
Elevation Model

DEM (Digital Elevation Models) is the best way to interpret topography on the ground. In recent years, lidar technology allows to create more accurate elevation models. However, the problem is this technology is not common all over the world. Also if Lidar data are not provided by government agencies freely, people have to pay lots of money to reach these point clouds. In this article, we will discuss how we can create digital elevation model from less accurate mobile devices’ GPS data. Moreover, we will evaluate these data on the same mobile device which we collected data to reduce cost of this modeling.

scholarly journals An Improved Ball Pivot Algorithm-Based Ground Filtering Mechanism for LiDAR Data

2019 ◽  
Vol 11 (10) ◽  
pp. 1179 ◽  
Author(s):  
Wei Ma ◽  
Qingquan Li
Keyword(s):  
Spatial Relationship ◽  
Single Layer ◽  
Experimental Results ◽  
Lidar Data ◽  
Initial Surface ◽  
Seed Point ◽  
Pivot Algorithm ◽  
Group Iii ◽  
Digital Elevation ◽  
Elevation Model

Automatic ground filtering is an essential step for Digital Elevation Model (DEM) generation, which has significant application value. However, extraction and classification of ground points from the Light Detection and Ranging (LiDAR) data, especially in multitudinous terrain situations, is a challenging task because it is difficult to determine the set of optimal parameters for removing various non-ground features. In this paper, a new ground filtering technique based on an improved Ball Pivot Algorithm (BPA) is proposed. At the beginning, the LiDAR point cloud dataset was divided into different subsets based on the 2D regular grid. The lowest point in each grid was selected as the seed point to build a single-layer surface. After that, the improved BPA was executed to remove points on the higher location. Then, the rest of the points were calculated and selected as a new seed point according to the spatial relationship with the initial surface. Finally, non-ground points were filtered by means of improved BPA traversing all the grids. Our experimental results on the Benchmark dataset provided by the International Society for Photogrammetry and Remote Sensing (ISPRS) Working Group III/3 showed high accuracy (with a mean kappa coefficient over 80%) in terms of completeness, correctness, and quality for DEM generation. The experimental results demonstrated the proposed method is robust to various terrain situations, as it is more effective and feasible for ground filtering.

scholarly journals A study on the use of planarity for quick identification of potential landslide hazard

2015 ◽  
Vol 15 (5) ◽  
pp. 997-1009 ◽  
Author(s):  
M. H. Baek ◽  
T. H. Kim
Keyword(s):  
Hazard Assessment ◽  
Ground Surface ◽  
Landslide Hazard ◽  
Axial Orientation ◽  
Orientation Data ◽  
Landslide Hazard Assessment ◽  
Geotechnical Monitoring ◽  
Digital Elevation ◽  
Elevation Model ◽  
Geomorphological Features

Abstract. In this study we focused on identifying a geomorphological feature that controls the location of landslides. The representation of the feature is based on a high-resolution digital elevation model derived from the airborne laser altimetry (LiDAR) and evaluated by the statistical analysis of axial orientation data. The main principle of this analysis is generating eigenvalues from axial orientation data and comparing them. The planarity, a ratio of eigenvalues, would tell the degree of irregularity on the ground surface based on their ratios. Results are compared to the recent landslide case in Korea in order to evaluate the feasibility of the proposed methodology in identifying the potential landslide hazard. The preliminary landslide hazard assessment based on the planarity analysis discriminates features between stable and unstable domain in the study area well, especially in the landslide initiation zones. Results also show it is beneficial to build the landslide hazard inventory mapping, especially where no information on historical records of landslides exists. By combining other physical procedures such as geotechnical monitoring, the landslide hazard assessment using geomorphological features promises a better understanding of landslides and their mechanisms and provides an enhanced methodology to evaluate their hazards and appropriate actions.

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