scholarly journals AIRBORNE TO UAS LIDAR: AN ANALYSIS OF UAS LIDAR GROUND CONTROL TARGETS

2019 ◽  
Vol XLII-2/W13 ◽  
pp. 255-262 ◽  
Author(s):  
L. Davidson ◽  
J. P. Mills ◽  
I. Haynes ◽  
C. Augarde ◽  
P. Bryan ◽  
...  
Keyword(s):  
Laser Scanning ◽  
Ground Level ◽  
Airborne Laser Scanning ◽  
Airborne Lidar ◽  
Mission Planning ◽  
Ground Control ◽  
Academic Literature ◽  
Airborne Laser ◽  
Control Target ◽  
Geomorphological Changes

<p><strong>Abstract.</strong> Creating accurate models of the Earth’s surface is an essential step when analysing geomorphological changes through time. Alongside photogrammetry, airborne lidar is an established method for measuring and modelling the Earth’s surface. However, improvements in size, weight and power requirements mean that lidar is now increasingly capable of being operated from Unpiloted Aircraft Systems (UASs). While academic literature is currently weighted towards issues associated with airborne laser scanning, UASs operate under different parameters to piloted aeroplanes and helicopters. In order to achieve desired results from UAS lidar, mission planning parameters and ground control requirements therefore need to be tailored to data collection from UAS platforms. This paper presents the preliminary results of how a variety control target designs responded to a UAS lidar survey flown along different trajectories at different heights above ground level. This research draws upon previous airborne laser scanning work and aims to provide guidance on considerations for UAS lidar specific ground control targets.</p>

scholarly journals Comparison of Airborne Laser Scanning of Low and High Above Ground Level for Selected Infrastructure Objects

2018 ◽  
Vol 8 (2) ◽  
pp. 89-96 ◽  
Author(s):  
J. Siwiec
Keyword(s):  
Laser Scanning ◽  
New Technology ◽  
Ground Level ◽  
Point Clouds ◽  
Airborne Lidar ◽  
Structural Elements ◽  
Transmission Tower ◽  
Deviation Analysis ◽  
Airborne Laser ◽  
Internal Coherence

Abstract Along with the development of the technology of drone construction (UAV - Unmanned Aerial Vehicles), the number of applications of these solutions in the industry also grew. The aim of the research is to check the accuracy of data obtained using the new technology of UAV scanning and to compare them with one that is widely spread - high-altitude airborne Lidar, in terms of quality and spectrum of applications in industry and infrastructure. The research involved two infrastructure objects: a reinforced concrete one-span bridge and Lattice transmission tower with powerlines. The density of measurement, internal and external cohesion of point clouds obtained from both methods were compared. Plane fitting and deviation analysis were used. The data of UAV origin in both cases provided a sufficient density, allowing the recognition of structural elements, and internal coherence and precision of measurements important in modeling. The study shows that UAV mounted scanning may be used in the same applications as Airborne Lidar, as well as in other tasks requiring greater precision.

scholarly journals CLUSTERING OF MULTISPECTRAL AIRBORNE LASER SCANNING DATA USING GAUSSIAN DECOMPOSITION

2017 ◽  
Vol XLII-2/W7 ◽  
pp. 269-276 ◽  
Author(s):  
S. Morsy ◽  
A. Shaker ◽  
A. El-Rabbany
Keyword(s):  
Land Surface ◽  
Laser Scanning ◽  
Vegetation Indices ◽  
Gaussian Function ◽  
Expectation Maximization Algorithm ◽  
Point Clouds ◽  
Airborne Laser Scanning ◽  
Airborne Lidar ◽  
Airborne Laser ◽  
Gaussian Decomposition

With the evolution of the LiDAR technology, multispectral airborne laser scanning systems are currently available. The first operational multispectral airborne LiDAR sensor, the Optech Titan, acquires LiDAR point clouds at three different wavelengths (1.550, 1.064, 0.532&amp;thinsp;μm), allowing the acquisition of different spectral information of land surface. Consequently, the recent studies are devoted to use the radiometric information (i.e., intensity) of the LiDAR data along with the geometric information (e.g., height) for classification purposes. In this study, a data clustering method, based on Gaussian decomposition, is presented. First, a ground filtering mechanism is applied to separate non-ground from ground points. Then, three normalized difference vegetation indices (NDVIs) are computed for both non-ground and ground points, followed by histograms construction from each NDVI. The Gaussian function model is used to decompose the histograms into a number of Gaussian components. The maximum likelihood estimate of the Gaussian components is then optimized using Expectation – Maximization algorithm. The intersection points of the adjacent Gaussian components are subsequently used as threshold values, whereas different classes can be clustered. This method is used to classify the terrain of an urban area in Oshawa, Ontario, Canada, into four main classes, namely roofs, trees, asphalt and grass. It is shown that the proposed method has achieved an overall accuracy up to 95.1&amp;thinsp;% using different NDVIs.

scholarly journals AUTOMATIC EXTRACTION AND REGULARIZATION OF BUILDING OUTLINES FROM AIRBORNE LIDAR POINT CLOUDS

2016 ◽  
Vol XLI-B3 ◽  
pp. 555-560
Author(s):  
Bastian Albers ◽  
Martin Kada ◽  
Andreas Wichmann
Keyword(s):  
Laser Scanning ◽  
Viterbi Algorithm ◽  
Point Clouds ◽  
Airborne Laser Scanning ◽  
Airborne Lidar ◽  
Trade Off ◽  
Line Segments ◽  
Alpha Shape ◽  
3D Point Clouds ◽  
Airborne Laser

Building outlines are needed for various applications like urban planning, 3D city modelling and updating cadaster. Their automatic reconstruction, e.g. from airborne laser scanning data, as regularized shapes is therefore of high relevance. Today’s airborne laser scanning technology can produce dense 3D point clouds with high accuracy, which makes it an eligible data source to reconstruct 2D building outlines or even 3D building models. In this paper, we propose an automatic building outline extraction and regularization method that implements a trade-off between enforcing strict shape restriction and allowing flexible angles using an energy minimization approach. The proposed procedure can be summarized for each building as follows: (1) an initial building outline is created from a given set of building points with the alpha shape algorithm; (2) a Hough transform is used to determine the main directions of the building and to extract line segments which are oriented accordingly; (3) the alpha shape boundary points are then repositioned to both follow these segments, but also to respect their original location, favoring long line segments and certain angles. The energy function that guides this trade-off is evaluated with the Viterbi algorithm.

scholarly journals AN ADAPTED CONNECTED COMPONENT LABELING FOR CLUSTERING NON-PLANAR OBJECTS FROM AIRBORNE LIDAR POINT CLOUD

2020 ◽  
Vol XLIII-B2-2020 ◽  
pp. 191-195 ◽  
Author(s):  
B. Aissou ◽  
A. Belhadj Aissa
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Airborne Laser Scanning ◽  
Airborne Lidar ◽  
Support Vector ◽  
Connected Component ◽  
Planar Surfaces ◽  
Airborne Laser ◽  
Connected Component Labeling ◽  
Sensing Technology

Abstract. Light Detection And Ranging (LiDAR) is an active remote sensing technology used for several applications. A segmentation of Airborne Laser Scanning (ALS) point cloud is very important task that still interest many scientists. In this paper, the Connected Component Analysis (CCA), or Connected Component Labeling is proposed for clustering non-planar objects from Airborne Laser Scanning (ALS) LiDAR point cloud. From raw point cloud, sub-surface segmentation method is applied as preliminary filter to remove planar surfaces. Starting from unassigned points , CCA is applied on 3D data considering only neighboring distance as initial parameter. To evaluate the clustering, an interactive labeling of the resulting components is performed. Then, components are classified using Support Vector Machine, Random Forest and Decision Tree. The ALS data used is characterized by a low density (4–6 points/m2), and is covering an urban area, located in residential parts of Vaihingen city in southern Germany. The visualization of the results shown the potential of the proposed method to identify dormers, chimneys and ground class.

scholarly journals AUTOMATIC EXTRACTION AND REGULARIZATION OF BUILDING OUTLINES FROM AIRBORNE LIDAR POINT CLOUDS

2016 ◽  
Vol XLI-B3 ◽  
pp. 555-560 ◽  
Author(s):  
Bastian Albers ◽  
Martin Kada ◽  
Andreas Wichmann
Keyword(s):  
Laser Scanning ◽  
Viterbi Algorithm ◽  
Point Clouds ◽  
Airborne Laser Scanning ◽  
Airborne Lidar ◽  
Trade Off ◽  
Line Segments ◽  
Alpha Shape ◽  
3D Point Clouds ◽  
Airborne Laser

Building outlines are needed for various applications like urban planning, 3D city modelling and updating cadaster. Their automatic reconstruction, e.g. from airborne laser scanning data, as regularized shapes is therefore of high relevance. Today’s airborne laser scanning technology can produce dense 3D point clouds with high accuracy, which makes it an eligible data source to reconstruct 2D building outlines or even 3D building models. In this paper, we propose an automatic building outline extraction and regularization method that implements a trade-off between enforcing strict shape restriction and allowing flexible angles using an energy minimization approach. The proposed procedure can be summarized for each building as follows: (1) an initial building outline is created from a given set of building points with the alpha shape algorithm; (2) a Hough transform is used to determine the main directions of the building and to extract line segments which are oriented accordingly; (3) the alpha shape boundary points are then repositioned to both follow these segments, but also to respect their original location, favoring long line segments and certain angles. The energy function that guides this trade-off is evaluated with the Viterbi algorithm.

scholarly journals Test field for airborne laser scanning in Finland

2014 ◽  
Vol XL-1 ◽  
pp. 9-12 ◽  
Author(s):  
E. Ahokas ◽  
H. Kaartinen ◽  
A. Kukko ◽  
P. Litkey
Keyword(s):  
Laser Scanning ◽  
Laser Scanner ◽  
Ground Surface ◽  
Ground Level ◽  
Reference Points ◽  
Airborne Laser Scanning ◽  
Measurement Tool ◽  
Test Field ◽  
Airborne Laser ◽  
Spatial Coverage

Airborne laser scanning (ALS) is a widely spread operational measurement tool for obtaining 3D coordinates of the ground surface. There is a need for calibrating the ALS system and a test field for ALS was established at the end of 2013. The test field is situated in the city of Lahti, about 100 km to the north of Helsinki. The size of the area is approximately 3.5 km &times; 3.2 km. Reference data was collected with a mobile laser scanning (MLS) system assembled on a car roof. Some streets were measured both ways and most of them in one driving direction only. The MLS system of the Finnish Geodetic Institute (FGI) consists of a navigation system (NovAtel SPAN GNSS-IMU) and a laser scanner (FARO Focus3D 120). In addition to the MLS measurements more than 800 reference points were measured using a Trimble R8 VRS-GNSS system. Reference points are along the streets, on parking lots, and white pedestrian crossing line corners which can be used as reference targets. The National Land Survey of Finland has already used this test field this spring for calibrating their Leica ALS-70 scanner. Especially it was easier to determine the encoder scale factor parameter using this test field. Accuracy analysis of the MLS points showed that the point height RMSE is 2.8 cm and standard deviation is 2.6 cm. Our purpose is to measure both more MLS data and more reference points in the test field area to get a better spatial coverage. Calibration flight heights are planned to be 1000 m and 2500 m above ground level. A cross pattern, southwest&ndash;northeast and northwest&ndash;southeast, will be flown both in opposite directions.

scholarly journals New Advances in Automated Urban Modelling from Airborne Laser Scanning Data

2011 ◽  
Vol 5 (3) ◽  
pp. 196-208 ◽  
Author(s):  
D. F. Laefer ◽  
T. Hinks ◽  
H. Carr ◽  
L. Truong-Hong
Keyword(s):  
Laser Scanning ◽  
Airborne Laser Scanning ◽  
Airborne Laser ◽  
Urban Modelling
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