scholarly journals Comparative Analysis of the Digital Terrain Models Extracted from Airborne LiDAR Point Clouds Using Different Filtering Approaches in Residential Landscapes

2019 ◽  
Vol 08 (02) ◽  
pp. 51-75
Author(s):  
Fahmy F. F. Asal
Keyword(s):  
Comparative Analysis ◽  
Point Clouds ◽  
Airborne Lidar ◽  
Digital Terrain Models ◽  
Residential Landscapes ◽  
Digital Terrain ◽  
Terrain Models

scholarly journals Accuracy Assessment of Point Clouds from LiDAR and Dense Image Matching Acquired Using the UAV Platform for DTM Creation

2018 ◽  
Vol 7 (9) ◽  
pp. 342 ◽  
Author(s):  
Adam Salach ◽  
Krzysztof Bakuła ◽  
Magdalena Pilarska ◽  
Wojciech Ostrowski ◽  
Konrad Górski ◽  
...  
Keyword(s):  
Land Cover ◽  
Laser Scanning ◽  
Point Clouds ◽  
Data Sources ◽  
Digital Terrain Models ◽  
Vegetation Height ◽  
Digital Terrain ◽  
Terrain Models ◽  
Airborne Laser ◽  
Vertical Accuracy

In this paper, the results of an experiment about the vertical accuracy of generated digital terrain models were assessed. The created models were based on two techniques: LiDAR and photogrammetry. The data were acquired using an ultralight laser scanner, which was dedicated to Unmanned Aerial Vehicle (UAV) platforms that provide very dense point clouds (180 points per square meter), and an RGB digital camera that collects data at very high resolution (a ground sampling distance of 2 cm). The vertical error of the digital terrain models (DTMs) was evaluated based on the surveying data measured in the field and compared to airborne laser scanning collected with a manned plane. The data were acquired in summer during a corridor flight mission over levees and their surroundings, where various types of land cover were observed. The experiment results showed unequivocally, that the terrain models obtained using LiDAR technology were more accurate. An attempt to assess the accuracy and possibilities of penetration of the point cloud from the image-based approach, whilst referring to various types of land cover, was conducted based on Real Time Kinematic Global Navigation Satellite System (GNSS-RTK) measurements and was compared to archival airborne laser scanning data. The vertical accuracy of DTM was evaluated for uncovered and vegetation areas separately, providing information about the influence of the vegetation height on the results of the bare ground extraction and DTM generation. In uncovered and low vegetation areas (0–20 cm), the vertical accuracies of digital terrain models generated from different data sources were quite similar: for the UAV Laser Scanning (ULS) data, the RMSE was 0.11 m, and for the image-based data collected using the UAV platform, it was 0.14 m, whereas for medium vegetation (higher than 60 cm), the RMSE from these two data sources were 0.11 m and 0.36 m, respectively. A decrease in the accuracy of 0.10 m, for every 20 cm of vegetation height, was observed for photogrammetric data; and such a dependency was not noticed in the case of models created from the ULS data.

scholarly journals ENHANCING THE RESOLUTION OF URBAN DIGITAL TERRAIN MODELS USING MOBILE MAPPING SYSTEMS

2018 ◽  
Vol IV-4/W6 ◽  
pp. 11-18
Author(s):  
Y. Feng ◽  
C. Brenner ◽  
M. Sester
Keyword(s):  
Urban Areas ◽  
Laser Scanning ◽  
Overland Flow ◽  
Point Clouds ◽  
Digital Terrain Models ◽  
Mobile Mapping ◽  
Flow Modelling ◽  
Digital Terrain ◽  
Terrain Models ◽  
Mapping Techniques

<p><strong>Abstract.</strong> Digital Terrain Models (DTMs) are essential surveying products for terrain based analyses, especially for overland flow modelling. Nowadays, many high resolution DTM products are generated by Airborne Laser Scanning (ALS). However, DTMs with even higher resolution are of great interest for a more precise overland flow modelling in urban areas. With the help of mobile mapping techniques, we can obtain much denser measurements of the ground in the vicinity of roads. In this research, a study area in Hannover, Germany was measured by a mobile mapping system. Point clouds from 485 scan strips were aligned and a DTM was extracted. In order to achieve a product with completeness, this mobile mapping produced DTM was then merged and adapted with a DTM product with 0.5<span class="thinspace"></span>m resolution from a mapping agency. Systematic evaluations have been conducted with respect to the height accuracy of the DTM products. The results show that the final DTM product achieved a higher resolution (0.1<span class="thinspace"></span>m) near the roads while essentially maintaining its height accuracy.</p>

The Use of Lidar to Facilitate Design and Construction of Pipelines

2008 ◽  
Author(s):  
Bruce Smith ◽  
Yan Wong ◽  
Steve Adam
Keyword(s):  
Survey Data ◽  
Ground Surface ◽  
Airborne Lidar ◽  
Digital Terrain Models ◽  
Pipeline Construction ◽  
Topographic Data ◽  
Digital Terrain ◽  
Terrain Models ◽  
Pipeline Design ◽  
Design And Construction

Within the last decade, airborne lidar (Light Detection And Ranging) equipment has evolved to the point where it can provide accurate ground surface elevations on a dense grid (often 1m by 1m) along pipeline corridors, at a cost that is a fraction of the cost for a comparable ground based topographic survey. This paper explains how lidar is used to acquire topographic data and how the data are converted to digital terrain models referenced to geodetic benchmarks. The accuracy and density of topographic data acquired by lidar surveys can be used to greatly facilitate pipeline design and reduce pipeline construction costs. The major benefits include: 1) The density of ground surface elevations obtained using lidar are significantly better than can be obtained using photogrammetry or conventional ground based survey methods. 2) The survey data can be collected over large areas in a matter of days and with virtually no disturbance to landowners. 3) The digital terrain models derived from lidar survey data can be imported into existing drafting (CAD) software and used to efficiently generate centerline profiles, cross-sections and alignment sheets as required for pipeline design and construction. 4) Hillshade maps derived from lidar data have proven extremely useful in pipeline route studies because they allow surface features to be identified and often avoided, thereby minimizing pipeline construction and operating costs.

scholarly journals Extraction of linear structures from digital terrain models using deep learning

2021 ◽  
Vol 2 ◽  
pp. 1-14
Author(s):  
Ramish Satari ◽  
Bashir Kazimi ◽  
Monika Sester
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Laser Scanning ◽  
Semantic Segmentation ◽  
Point Clouds ◽  
Linear Structures ◽  
Digital Terrain Models ◽  
Deep Convolutional Neural Networks ◽  
Digital Terrain ◽  
Terrain Models

Abstract. This paper explores the role deep convolutional neural networks play in automated extraction of linear structures using semantic segmentation techniques in Digital Terrain Models (DTMs). DTM is a regularly gridded raster created from laser scanning point clouds and represents elevations of the bare earth surface with respect to a reference. Recent advances in Deep Learning (DL) have made it possible to explore the use of semantic segmentation for detection of terrain structures in DTMs. This research examines two novel and practical deep convolutional neural network architectures i.e. an encoder-decoder network named as SegNet and the recent state-of-the-art high-resolution network (HRNet). This paper initially focuses on the pixel-wise binary classification in order to validate the applicability of the proposed approaches. The networks are trained to distinguish between points belonging to linear structures and those belonging to background. In the second step, multi-class segmentation is carried out on the same DTM dataset. The model is trained to not only detect a linear feature, but also to categorize it as one of the classes: hollow ways, roads, forest paths, historical paths, and streams. Results of the experiment in addition to the quantitative and qualitative analysis show the applicability of deep neural networks for detection of terrain structures in DTMs. From the deep learning models utilized, HRNet gives better results.

scholarly journals AN EFFICIENT METHOD TO CREATE DIGITAL TERRAIN MODELS FROM POINT CLOUDS COLLECTED BY MOBILE LiDAR SYSTEMS

2017 ◽  
Vol XLII-1/W1 ◽  
pp. 289-296 ◽  
Author(s):  
L. Gézero ◽  
C. Antunes
Keyword(s):  
Large Scale ◽  
Point Clouds ◽  
Second Step ◽  
Road Maintenance ◽  
Digital Terrain Models ◽  
Remote Areas ◽  
Digital Terrain ◽  
Terrain Models ◽  
Dense Point ◽  
Set Of Points

The digital terrain models (DTM) assume an essential role in all types of road maintenance, water supply and sanitation projects. The demand of such information is more significant in developing countries, where the lack of infrastructures is higher. In recent years, the use of Mobile LiDAR Systems (MLS) proved to be a very efficient technique in the acquisition of precise and dense point clouds. These point clouds can be a solution to obtain the data for the production of DTM in remote areas, due mainly to the safety, precision, speed of acquisition and the detail of the information gathered. However, the point clouds filtering and algorithms to separate “terrain points” from “no terrain points”, quickly and consistently, remain a challenge that has caught the interest of researchers. This work presents a method to create the DTM from point clouds collected by MLS. The method is based in two interactive steps. The first step of the process allows reducing the cloud point to a set of points that represent the terrain’s shape, being the distance between points inversely proportional to the terrain variation. The second step is based on the Delaunay triangulation of the points resulting from the first step. The achieved results encourage a wider use of this technology as a solution for large scale DTM production in remote areas.

scholarly journals Semi-Global Filtering of Airborne LiDAR Data for Fast Extraction of Digital Terrain Models

2015 ◽  
Vol 7 (8) ◽  
pp. 10996-11015 ◽  
Author(s):  
Xiangyun Hu ◽  
Lizhi Ye ◽  
Shiyan Pang ◽  
Jie Shan
Keyword(s):  
Airborne Lidar ◽  
Lidar Data ◽  
Digital Terrain Models ◽  
Digital Terrain ◽  
Terrain Models ◽  
Fast Extraction ◽  
Airborne Lidar Data

scholarly journals The use of airborne LiDAR data for the analysis of debris flow events in Switzerland

2008 ◽  
Vol 8 (5) ◽  
pp. 1113-1127 ◽  
Author(s):  
C. Scheidl ◽  
D. Rickenmann ◽  
M. Chiari
Keyword(s):  
Debris Flow ◽  
Airborne Lidar ◽  
Lidar Data ◽  
Digital Terrain Models ◽  
Alpine Regions ◽  
Digital Terrain ◽  
Terrain Models ◽  
Digital Elevation ◽  
Airborne Lidar Data ◽  
Volume Estimates

Abstract. A methodology of magnitude estimates for debris flow events is described using airborne LiDAR data. Light Detection And Ranging (LiDAR) is a widely used technology to generate digital elevation information. LiDAR data in alpine regions can be obtained by several commercial companies where the automated filtering process is proprietary and varies from companies to companies. This study describes the analysis of geomorphologic changes using digital terrain models derived from commercial LiDAR data. The estimation of the deposition volumes is based on two digital terrain models covering the same area but differing in their time of survey. In this study two surveyed deposition areas of debris flows, located in the canton of Berne, Switzerland, were chosen as test cases. We discuss different grid interpolating techniques, other preliminary work and the accuracy of the used LiDAR data and volume estimates.

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