scholarly journals Evaluation of UAV LiDAR for Mapping Coastal Environments

2019 ◽  
Vol 11 (24) ◽  
pp. 2893 ◽  
Author(s):  
Yi-Chun Lin ◽  
Yi-Ting Cheng ◽  
Tian Zhou ◽  
Radhika Ravi ◽  
Seyyed Hasheminasab ◽  
...  
Keyword(s):  
High Resolution ◽  
Lake Michigan ◽  
Point Clouds ◽  
Volume Loss ◽  
Coastal Environments ◽  
Average Volume ◽  
Survey Period ◽  
Shoreline Changes ◽  
Point Density ◽  
One Year

Unmanned Aerial Vehicle (UAV)-based remote sensing techniques have demonstrated great potential for monitoring rapid shoreline changes. With image-based approaches utilizing Structure from Motion (SfM), high-resolution Digital Surface Models (DSM), and orthophotos can be generated efficiently using UAV imagery. However, image-based mapping yields relatively poor results in low textured areas as compared to those from LiDAR. This study demonstrates the applicability of UAV LiDAR for mapping coastal environments. A custom-built UAV-based mobile mapping system is used to simultaneously collect LiDAR and imagery data. The quality of LiDAR, as well as image-based point clouds, are investigated and compared over different geomorphic environments in terms of their point density, relative and absolute accuracy, and area coverage. The results suggest that both UAV LiDAR and image-based techniques provide high-resolution and high-quality topographic data, and the point clouds generated by both techniques are compatible within a 5 to 10 cm range. UAV LiDAR has a clear advantage in terms of large and uniform ground coverage over different geomorphic environments, higher point density, and ability to penetrate through vegetation to capture points below the canopy. Furthermore, UAV LiDAR-based data acquisitions are assessed for their applicability in monitoring shoreline changes over two actively eroding sandy beaches along southern Lake Michigan, Dune Acres, and Beverly Shores, through repeated field surveys. The results indicate a considerable volume loss and ridge point retreat over an extended period of one year (May 2018 to May 2019) as well as a short storm-induced period of one month (November 2018 to December 2018). The foredune ridge recession ranges from 0 m to 9 m. The average volume loss at Dune Acres is 18.2 cubic meters per meter and 12.2 cubic meters per meter within the one-year period and storm-induced period, respectively, highlighting the importance of episodic events in coastline changes. The average volume loss at Beverly Shores is 2.8 cubic meters per meter and 2.6 cubic meters per meter within the survey period and storm-induced period, respectively.

scholarly journals Disturbance Analysis in the Classification of Objects Obtained from Urban LiDAR Point Clouds with Convolutional Neural Networks

2021 ◽  
Vol 13 (11) ◽  
pp. 2135
Author(s):  
Jesús Balado ◽  
Pedro Arias ◽  
Henrique Lorenzo ◽  
Adrián Meijide-Rodríguez
Keyword(s):  
Laser Scanning ◽  
Ambient Noise ◽  
Point Clouds ◽  
Density Variation ◽  
Density Reduction ◽  
Manual Intervention ◽  
Point Density ◽  
Disturbance Analysis ◽  
Street Furniture

Mobile Laser Scanning (MLS) systems have proven their usefulness in the rapid and accurate acquisition of the urban environment. From the generated point clouds, street furniture can be extracted and classified without manual intervention. However, this process of acquisition and classification is not error-free, caused mainly by disturbances. This paper analyses the effect of three disturbances (point density variation, ambient noise, and occlusions) on the classification of urban objects in point clouds. From point clouds acquired in real case studies, synthetic disturbances are generated and added. The point density reduction is generated by downsampling in a voxel-wise distribution. The ambient noise is generated as random points within the bounding box of the object, and the occlusion is generated by eliminating points contained in a sphere. Samples with disturbances are classified by a pre-trained Convolutional Neural Network (CNN). The results showed different behaviours for each disturbance: density reduction affected objects depending on the object shape and dimensions, ambient noise depending on the volume of the object, while occlusions depended on their size and location. Finally, the CNN was re-trained with a percentage of synthetic samples with disturbances. An improvement in the performance of 10–40% was reported except for occlusions with a radius larger than 1 m.

EARLY RESULTS FROM THE HIGH-RESOLUTION FLY's EYE

2001 ◽  
Vol 16 (supp01c) ◽  
pp. 1019-1021
Author(s):  
JOHN BELZ
Keyword(s):  
High Resolution ◽  
Cosmic Ray ◽  
Energy Spectra ◽  
Fluorescence Technique ◽  
Early Results ◽  
One Year ◽  
High Resolution Fly's Eye

The High-Resolution Fly's Eye cosmic ray observatory has been operating in monocular (stereo) mode for about three years (one year), during which time we have observed extensive airshowers with an integrated aperture of ~ 1500 km2-sr-yr (~ 400 km-sr-yr) at 5×1019 eV. We describe the HiRes experiment and the nitrogen fluorescence technique, and present data taken in both monocular and stereo modes including preliminary energy spectra.

Effect of point density and interpolation of LiDAR-derived high-resolution DEMs on landscape scarp identification

2014 ◽  
Vol 51 (6) ◽  
pp. 731-747 ◽  
Author(s):  
Hone-Jay Chu ◽  
Chi-Kuei Wang ◽  
Min-Lang Huang ◽  
Chung-Cheng Lee ◽  
Chun-Yu Liu ◽  
...  
Keyword(s):  
High Resolution ◽  
Point Density

2D quantitative analysis of fractures from high-resolution photos for the geomechanical characterization of rock masses

2021 ◽  
Author(s):  
Emmanuel Wyser ◽  
Lidia Loiotine ◽  
Charlotte Wolff ◽  
Gioacchino Francesco Andriani ◽  
Michel Jaboyedoff ◽  
...  
Keyword(s):  
High Resolution ◽  
Point Clouds ◽  
Mitigation Measures ◽  
Future Perspective ◽  
Rock Masses ◽  
Sampling Errors ◽  
Discontinuity Sets ◽  
Adverse Weather ◽  
Geomechanical Characterization

<p>The identification of discontinuity sets and their properties is among the key factors for the geomechanical characterization of rock masses, which is fundamental for performing stability analyses, and for planning prevention and mitigation measures as well.<br>In practice, discontinuity data are collected throughout difficult and time-consuming field surveys, especially when dealing with areas of wide extension, difficult accessibility, covered by dense vegetation, or with adverse weather conditions. Consequently, even experienced operators may introduce sampling errors or misinterpretations, leading to biased geomechanical models for the investigated rock mass.<br>In the last decades, new remote techniques such as photogrammetry,<em> Light Detection and Ranging</em> (LiDAR), <em>Unmanned Aerial Vehicle</em> (UAV) and <em>InfraRed Thermography </em>(IRT) have been introduced to overcome the limits of conventional surveys. We propose here a new tool for extracting information on the fracture pattern in rock masses, based on <em>remote sensing </em>methods, with particular reference to the analysis of high-resolution georeferenced photos. The first step consists in applying the <em>Structure from Motion</em> (SfM) technique on photos acquired by means of digital cameras and UAV techniques. Once aligned and georeferenced, the orthophotos are exported in a GIS software, to draw the fracture traces at an appropriate scale. We developed a MATLAB routine to extract information on the geostructural setting of rock masses by performing a quantitative 2D analysis of the fracture traces, based on formulas reported in the literature. The code was written by testing few experimental and simple traces and was successively validated on an orthophoto from a real case study.<br>Currently, the script plots the fracture traces as polylines and calculates their orientation (strike) and length. Subsequently, it detects the main discontinuity sets by fitting an experimental composite Gaussian curve on histograms showing the number of discontinuities according to their orientation, and splitting the curve in simpler Gaussian curves, with peaks corresponding to the main discontinuity sets.<br>Then, for each set, a linear scanline intersecting the highest number of traces is plotted, and the apparent and real spacing are calculated. In a second step, a grid of circular scanlines covering the whole area where the traces are located is plotted, and the mean trace intensity, trace density and trace length estimators are calculated.<br>It is expected to test the presented tools on other case studies, in order to optimize them and calculate additional metrics, such as persistence and block sizes, useful to the geomechanical characterization of rock masses.<br>As a future perspective, a similar approach could be investigated for 3D analyses from point clouds.</p>

scholarly journals An unusual association between Mycobacterium tuberculosis and Aspergillus fumigatus

2016 ◽  
Vol 69 (1) ◽  
Author(s):  
R. Agarwal ◽  
N. Singh ◽  
A.N. Aggarwal
Keyword(s):  
Cystic Fibrosis ◽  
Mycobacterium Tuberculosis ◽  
High Resolution ◽  
Lung Diseases ◽  
Allergic Bronchopulmonary Aspergillosis ◽  
The Other ◽  
Volume Loss ◽  
Computed Tomographic ◽  
Unusual Association ◽  
Computed Tomographic Scan

Allergic bronchopulmonary aspergillosis (ABPA) is rarely described outside the setting of asthma or cystic fibrosis. The occurrence of ABPA in other structural lung diseases included scars of old healed pulmonary tuberculosis (PTB) is also unknown. In this case, we report a 62- year old lady treated for PTB 40 years ago who presented with increasing dyspnea on exertion, cough with expectoration of blackish brown mucus plugs and wheezing. High-resolution computed tomographic scan of the thorax showed parenchymal fibrosis and volume loss in left upper lobe while central bronchiectasis, mosaic attenuation, centrilobular nodules with a tree-in-bud pattern were observed in the other lobes. Investigations revealed a diagnosis of ABPA. The patient was treated with prednisolone and showed a significant response. We review the current literature on this unusual association of previous and cured TB with ABPA, and also discuss the hypothesis of this possible relationship.

scholarly journals CREATING MULTI-TEMPORAL MAPS OF URBAN ENVIRONMENTS FOR IMPROVED LOCALIZATION OF AUTONOMOUS VEHICLES

2020 ◽  
Vol XLIII-B2-2020 ◽  
pp. 317-323
Author(s):  
J. Schachtschneider ◽  
C. Brenner
Keyword(s):  
Urban Areas ◽  
Autonomous Vehicles ◽  
Weather Conditions ◽  
Point Clouds ◽  
Urban Environments ◽  
Seasonal Effects ◽  
Data Set ◽  
Multi Temporal ◽  
One Year

Abstract. The development of automated and autonomous vehicles requires highly accurate long-term maps of the environment. Urban areas contain a large number of dynamic objects which change over time. Since a permanent observation of the environment is impossible and there will always be a first time visit of an unknown or changed area, a map of an urban environment needs to model such dynamics.In this work, we use LiDAR point clouds from a large long term measurement campaign to investigate temporal changes. The data set was recorded along a 20 km route in Hannover, Germany with a Mobile Mapping System over a period of one year in bi-weekly measurements. The data set covers a variety of different urban objects and areas, weather conditions and seasons. Based on this data set, we show how scene and seasonal effects influence the measurement likelihood, and that multi-temporal maps lead to the best positioning results.

scholarly journals SEMANTIC LABELLING OF ULTRA DENSE MLS POINT CLOUDS IN URBAN ROAD CORRIDORS BASED ON FUSING CRF WITH SHAPE PRIORS

2017 ◽  
Vol XLII-2/W7 ◽  
pp. 971-976 ◽  
Author(s):  
W. Yao ◽  
P. Polewski ◽  
P. Krzystek
Keyword(s):  
Semantic Analysis ◽  
Sampling Rate ◽  
Point Clouds ◽  
Shape Priors ◽  
High Point ◽  
Urban Road ◽  
3D Point Clouds ◽  
Point Density ◽  
Object Classes ◽  
Road Corridors

In this paper, a labelling method for the semantic analysis of ultra-high point density MLS data (up to 4000 points/m<sup>2</sup>) in urban road corridors is developed based on combining a conditional random field (CRF) for the context-based classification of 3D point clouds with shape priors. The CRF uses a Random Forest (RF) for generating the unary potentials of nodes and a variant of the contrastsensitive Potts model for the pair-wise potentials of node edges. The foundations of the classification are various geometric features derived by means of co-variance matrices and local accumulation map of spatial coordinates based on local neighbourhoods. Meanwhile, in order to cope with the ultra-high point density, a plane-based region growing method combined with a rule-based classifier is applied to first fix semantic labels for man-made objects. Once such kind of points that usually account for majority of entire data amount are pre-labeled; the CRF classifier can be solved by optimizing the discriminative probability for nodes within a subgraph structure excluded from pre-labeled nodes. The process can be viewed as an evidence fusion step inferring a degree of belief for point labelling from different sources. The MLS data used for this study were acquired by vehicle-borne Z+F phase-based laser scanner measurement, which permits the generation of a point cloud with an ultra-high sampling rate and accuracy. The test sites are parts of Munich City which is assumed to consist of seven object classes including impervious surfaces, tree, building roof/facade, low vegetation, vehicle and pole. The competitive classification performance can be explained by the diverse factors: e.g. the above ground height highlights the vertical dimension of houses, trees even cars, but also attributed to decision-level fusion of graph-based contextual classification approach with shape priors. The use of context-based classification methods mainly contributed to smoothing of labelling by removing outliers and the improvement in underrepresented object classes. In addition, the routine operation of a context-based classification for such high density MLS data becomes much more efficient being comparable to non-contextual classification schemes.

scholarly journals Evaluation of the Apple iPhone 12 Pro LiDAR for an Application in Geosciences

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Gregor Luetzenburg ◽  
Aart Kroon ◽  
Anders A. Bjørk
Keyword(s):  
High Resolution ◽  
Optical Sensors ◽  
Cost Effective ◽  
Point Clouds ◽  
3D Models ◽  
Absolute Accuracy ◽  
Wide Range ◽  
Financial Investments ◽  
Coastal Cliff ◽  
High Resolution Models

AbstractTraditionally, topographic surveying in earth sciences requires high financial investments, elaborate logistics, complicated training of staff and extensive data processing. Recently, off-the-shelf drones with optical sensors already reduced the costs for obtaining a high-resolution dataset of an Earth surface considerably. Nevertheless, costs and complexity associated with topographic surveying are still high. In 2020, Apple Inc. released the iPad Pro 2020 and the iPhone 12 Pro with novel build-in LiDAR sensors. Here we investigate the basic technical capabilities of the LiDAR sensors and we test the application at a coastal cliff in Denmark. The results are compared to state-of-the-art Structure from Motion Multi-View Stereo (SfM MVS) point clouds. The LiDAR sensors create accurate high-resolution models of small objects with a side length > 10 cm with an absolute accuracy of ± 1 cm. 3D models with the dimensions of up to 130 × 15 × 10 m of a coastal cliff with an absolute accuracy of ± 10 cm are compiled. Overall, the versatility in handling outweighs the range limitations, making the Apple LiDAR devices cost-effective alternatives to established techniques in remote sensing with possible fields of application for a wide range of geo-scientific areas and teaching.

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