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 A REGISTRATION METHOD OF POINT CLOUDS COLLECTED BY MOBILE LIDAR USING SOLELY STANDARD LAS FILES INFORMATION

2017 ◽  
Vol XLII-1/W1 ◽  
pp. 121-128 ◽  
Author(s):  
L. Gézero ◽  
C. Antunes
Keyword(s):  
Point Cloud ◽  
Large Scale ◽  
Auxiliary Information ◽  
Point Clouds ◽  
Registration Method ◽  
Digital Terrain ◽  
Terrain Models ◽  
Dense Point ◽  
Fully Automatic ◽  
Method Point

In the last few years, LiDAR sensors installed in terrestrial vehicles have been revealed as an efficient method to collect very dense 3D georeferenced information. The possibility of creating very dense point clouds representing the surface surrounding the sensor, at a given moment, in a very fast, detailed and easy way, shows the potential of this technology to be used for cartography and digital terrain models production in large scale. However, there are still some limitations associated with the use of this technology. When several acquisitions of the same area with the same device, are made, differences between the clouds can be observed. The range of that differences can go from few centimetres to some several tens of centimetres, mainly in urban and high vegetation areas where the occultation of the GNSS system introduces a degradation of the georeferenced trajectory. Along this article a different method point cloud registration is proposed. In addition to the efficiency and speed of execution, the main advantages of the method are related to the fact that the adjustment is continuously made over the trajectory, based on the GPS time. The process is fully automatic and only information recorded in the standard LAS files is used, without the need for any auxiliary information, in particular regarding the trajectory.

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>

Alternative Methods for Developing and Assessing the Accuracy of UAV-Derived DEMs

2019 ◽  
pp. 1175-1196
Author(s):  
Dion J. Wiseman ◽  
Jurjen van der Sluijs
Keyword(s):  
High Resolution ◽  
Accuracy Assessment ◽  
Point Clouds ◽  
Alternative Methods ◽  
Data Sets ◽  
Digital Terrain ◽  
Terrain Models ◽  
Dense Point ◽  
Data Points ◽  
Derived Data

Digital terrain models are invaluable datasets that are frequently used for visualizing, modeling, and analyzing Earth surface processes. Accurate models covering local scale landscape features are often very expensive and have poor temporal resolution. This research investigates the utility of UAV acquired imagery for generating high resolution terrain models and provides a detailed accuracy assessment according to recommended protocols. High resolution UAV imagery was acquired over a localized dune complex in southwestern Manitoba, Canada and two alternative workflows were evaluated for extracting point clouds. UAV-derived data points were then compared to reference data sets acquired using mapping grade GPS receivers and a total station. Results indicated that the UAV imagery was capable of producing dense point clouds and high resolution terrain models with mean errors as low as -0.15 m and RMSE values of 0.42 m depending on the resolution of the image dataset and workflow employed.

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.

Alternative Methods for Developing and Assessing the Accuracy of UAV-Derived DEMs

2015 ◽  
Vol 6 (3) ◽  
pp. 58-77 ◽  
Author(s):  
Dion J. Wiseman ◽  
Jurjen van der Sluijs
Keyword(s):  
High Resolution ◽  
Accuracy Assessment ◽  
Point Clouds ◽  
Alternative Methods ◽  
Data Sets ◽  
Digital Terrain ◽  
Terrain Models ◽  
Dense Point ◽  
Data Points ◽  
Derived Data

Digital terrain models are invaluable datasets that are frequently used for visualizing, modeling, and analyzing Earth surface processes. Accurate models covering local scale landscape features are often very expensive and have poor temporal resolution. This research investigates the utility of UAV acquired imagery for generating high resolution terrain models and provides a detailed accuracy assessment according to recommended protocols. High resolution UAV imagery was acquired over a localized dune complex in southwestern Manitoba, Canada and two alternative workflows were evaluated for extracting point clouds. UAV-derived data points were then compared to reference data sets acquired using mapping grade GPS receivers and a total station. Results indicated that the UAV imagery was capable of producing dense point clouds and high resolution terrain models with mean errors as low as -0.15 m and RMSE values of 0.42 m depending on the resolution of the image dataset and workflow employed.

scholarly journals MULTIGRID CELL SHAPE EVALUATION IN DTM FILTERING OF MOBILE LIDAR POINT CLOUDS

2018 ◽  
Vol XLII-4 ◽  
pp. 225-232
Author(s):  
L. Gézero ◽  
C. Antunes
Keyword(s):  
Information Acquisition ◽  
Cell Shape ◽  
Point Clouds ◽  
Maximum Speed ◽  
Field Collection ◽  
Remote Areas ◽  
Security Issues ◽  
Digital Terrain ◽  
Terrain Models ◽  
Different Shapes

<p><strong>Abstract.</strong> The georeferenced information acquisition for large scales digital terrain models generation is usually a rather time-consuming and costly process. The use of point clouds gathered by mobile LiDAR systems therefore appears as a possible source for the creation of those models, particularly in remote areas where security issues requires a maximum speed field collection information. However, the filtering challenge to separate the points that represents the terrain surface from the remaining, in an automatic and consistent way, is an open challenge that continues to arouse the interest of researchers. Using a multigrid filtering method, this work presents a comparative analysis of different shapes of regular grid cells, including the implementation algorithms for each cell shape. Also, a coordinate grid concept applied to each cell shape is proposed. Finally, a discussion about the obtained results of the method application using point clouds collected in different environments from different ground LiDAR systems are presented.</p>

scholarly journals Automated snow avalanche release area delineation – validation of existing algorithms and proposition of a new object-based approach for large-scale hazard indication mapping

2018 ◽  
Vol 18 (12) ◽  
pp. 3235-3251 ◽  
Author(s):  
Yves Bühler ◽  
Daniel von Rickenbach ◽  
Andreas Stoffel ◽  
Stefan Margreth ◽  
Lukas Stoffel ◽  
...  
Keyword(s):  
Return Period ◽  
Large Scale ◽  
Data Sets ◽  
Hazard Maps ◽  
Digital Terrain Models ◽  
Avalanche Hazard ◽  
Digital Terrain ◽  
Terrain Models ◽  
Object Based ◽  
Release Area

Abstract. Snow avalanche hazard is threatening people and infrastructure in all alpine regions with seasonal or permanent snow cover around the globe. Coping with this hazard is a big challenge and during the past centuries, different strategies were developed. Today, in Switzerland, experienced avalanche engineers produce hazard maps with a very high reliability based on avalanche database information, terrain analysis, climatological data sets and numerical modeling of the flow dynamics for selected avalanche tracks that might affect settlements. However, for regions outside the considered settlement areas such area-wide hazard maps are not available mainly because of the too high cost, in Switzerland and in most mountain regions around the world. Therefore, hazard indication maps, even though they are less reliable and less detailed, are often the only spatial planning tool available. To produce meaningful and cost-effective avalanche hazard indication maps over large regions (regional to national scale), automated release area delineation has to be combined with volume estimations and state-of-the-art numerical avalanche simulations. In this paper we validate existing potential release area (PRA) delineation algorithms, published in peer-reviewed journals, that are based on digital terrain models and their derivatives such as slope angle, aspect, roughness and curvature. For validation, we apply avalanche data from three different ski resorts in the vicinity of Davos, Switzerland, where experienced ski-patrol staff have mapped most avalanches in detail for many years. After calculating the best fit input parameters for every tested algorithm, we compare their performance based on the reference data sets. Because all tested algorithms do not provide meaningful delineation between individual PRAs, we propose a new algorithm based on object-based image analysis (OBIA). In combination with an automatic procedure to estimate the average release depth (d0), defining the avalanche release volume, this algorithm enables the numerical simulation of thousands of avalanches over large regions applying the well-established avalanche dynamics model RAMMS. We demonstrate this for the region of Davos for two hazard scenarios, frequent (10–30-year return period) and extreme (100–300-year return period). This approach opens the door for large-scale avalanche hazard indication mapping in all regions where high-quality and high-resolution digital terrain models and snow data are available.

Alternative Methods for Developing and Assessing the Accuracy of UAV-Derived DEMs

2019 ◽  
pp. 249-270
Author(s):  
Dion J. Wiseman ◽  
Jurjen van der Sluijs
Keyword(s):  
High Resolution ◽  
Accuracy Assessment ◽  
Point Clouds ◽  
Alternative Methods ◽  
Data Sets ◽  
Digital Terrain ◽  
Terrain Models ◽  
Dense Point ◽  
Data Points ◽  
Derived Data

Digital terrain models are invaluable datasets that are frequently used for visualizing, modeling, and analyzing Earth surface processes. Accurate models covering local scale landscape features are often very expensive and have poor temporal resolution. This research investigates the utility of UAV acquired imagery for generating high resolution terrain models and provides a detailed accuracy assessment according to recommended protocols. High resolution UAV imagery was acquired over a localized dune complex in southwestern Manitoba, Canada and two alternative workflows were evaluated for extracting point clouds. UAV-derived data points were then compared to reference data sets acquired using mapping grade GPS receivers and a total station. Results indicated that the UAV imagery was capable of producing dense point clouds and high resolution terrain models with mean errors as low as -0.15 m and RMSE values of 0.42 m depending on the resolution of the image dataset and workflow employed.

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