Point density exclusion mapping—A useful tool for mapping arrhythmias arising from the endocavitary structures

Today, in image analysis the broadest possible rationalization and economization have become desirable. Basically, there are two approaches for image analysis: The image analysis through the so-called scanning methods which are usually performed without the human eye and the systems of optical semiautomatic analysis completely relying on the human eye.The new MOP AM 01 opto-manual system (fig.) represents one of the very promising approaches in this field. The instrument consists of an electronic counting and storing unit, which incorporates a microprocessor and a keyboard for choice of measuring parameters, well designed for easy use.Using the MOP AM 01 there are three possibilities of image analysis:the manual point counting,the opto-manual point counting andthe measurement of absolute areas and/or length (size distribution analysis included).To determine a point density for the calculation of the corresponding volume density the intercepts lying within the structure are scanned with the light pen.

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Effects of LIDAR Point Density on Bare Earth Extraction and DEM Creation

10.21236/ada534534 ◽

2009 ◽

Author(s):

Angela M. Puetz ◽

R. C. Olsen ◽

Brian Anderson

Keyword(s):

Point Density

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STUDY ON PROPER ACQUISITION POINT DENSITY FOR “ICT DREDGING” (HARBOR CONSTRUCTION)

Journal of Japan Society of Civil Engineers Ser B3 (Ocean Engineering) ◽

10.2208/jscejoe.75.i_79 ◽

2019 ◽

Vol 75 (2) ◽

pp. I_79-I_84

Author(s):

Tatsunori SUZUKI ◽

Shigeru IYAMA ◽

Kenji SAKATA ◽

Megumi MURATA ◽

Eiji YOSHIDA

Keyword(s):

Point Density

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Evaluating the Safety Risk of Rural Roadsides Using a Bayesian Network Method

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph16071166 ◽

2019 ◽

Vol 16 (7) ◽

pp. 1166 ◽

Cited By ~ 3

Author(s):

Tianpei Tang ◽

Senlai Zhu ◽

Yuntao Guo ◽

Xizhao Zhou ◽

Yang Cao

Keyword(s):

Bayesian Network ◽

Low Cost ◽

Road Networks ◽

Access Point ◽

Safety Risk ◽

Crash Data ◽

Risk Levels ◽

The Road ◽

Run Off ◽

Point Density

Evaluating the safety risk of rural roadsides is critical for achieving reasonable allocation of a limited budget and avoiding excessive installation of safety facilities. To assess the safety risk of rural roadsides when the crash data are unavailable or missing, this study proposed a Bayesian Network (BN) method that uses the experts’ judgments on the conditional probability of different safety risk factors to evaluate the safety risk of rural roadsides. Eight factors were considered, including seven factors identified in the literature and a new factor named access point density. To validate the effectiveness of the proposed method, a case study was conducted using 19.42 km long road networks in the rural area of Nantong, China. By comparing the results of the proposed method and run-off-road (ROR) crash data from 2015–2016 in the study area, the road segments with higher safety risk levels identified by the proposed method were found to be statistically significantly correlated with higher crash severity based on the crash data. In addition, by comparing the respective results evaluated by eight factors and seven factors (a new factor removed), we also found that access point density significantly contributed to the safety risk of rural roadsides. These results show that the proposed method can be considered as a low-cost solution to evaluating the safety risk of rural roadsides with relatively high accuracy, especially for areas with large rural road networks and incomplete ROR crash data due to budget limitation, human errors, negligence, or inconsistent crash recordings.

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

Remote Sensing ◽

10.3390/rs13112135 ◽

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.

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The Effect of LiDAR Sampling Density on DTM Accuracy for Areas with Heavy Forest Cover

Forests ◽

10.3390/f12030265 ◽

2021 ◽

Vol 12 (3) ◽

pp. 265

Author(s):

Mihnea Cățeanu ◽

Arcadie Ciubotaru

Keyword(s):

Forest Cover ◽

Initial Point ◽

Ground Surface ◽

Digital Terrain Model ◽

Morphological Data ◽

Sampling Density ◽

Terrain Model ◽

Digital Terrain ◽

Point Density ◽

The Impact

Laser scanning via LiDAR is a powerful technique for collecting data necessary for Digital Terrain Model (DTM) generation, even in densely forested areas. LiDAR observations located at the ground level can be separated from the initial point cloud and used as input for the generation of a Digital Terrain Model (DTM) via interpolation. This paper proposes a quantitative analysis of the accuracy of DTMs (and derived slope maps) obtained from LiDAR data and is focused on conditions common to most forestry activities (rough, steep terrain with forest cover). Three interpolation algorithms were tested: Inverse Distance Weighted (IDW), Natural Neighbour (NN) and Thin-Plate Spline (TPS). Research was mainly focused on the issue of point data density. To analyze its impact on the quality of ground surface modelling, the density of the filtered data set was artificially lowered (from 0.89 to 0.09 points/m2) by randomly removing point observations in 10% increments. This provides a comprehensive method of evaluating the impact of LiDAR ground point density on DTM accuracy. While the reduction of point density leads to a less accurate DTM in all cases (as expected), the exact pattern varies by algorithm. The accuracy of the LiDAR-derived DTMs is relatively good even when LiDAR sampling density is reduced to 0.40–0.50 points/m2 (50–60 % of the initial point density), as long as a suitable interpolation algorithm is used (as IDW proved to be less resilient to density reductions below approximately 0.60 points/m2). In the case of slope estimation, the pattern is relatively similar, except the difference in accuracy between IDW and the other two algorithms is even more pronounced than in the case of DTM accuracy. Based on this research, we conclude that LiDAR is an adequate method for collecting morphological data necessary for modelling the ground surface, even when the sampling density is significantly reduced.

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Comparison of Coniferous Plantation Heights Using Unmanned Aerial Vehicle (UAV) Laser Scanning and Stereo Photogrammetry

Remote Sensing ◽

10.3390/rs13152885 ◽

2021 ◽

Vol 13 (15) ◽

pp. 2885

Author(s):

Mei Li ◽

Zengyuan Li ◽

Qingwang Liu ◽

Erxue Chen

Keyword(s):

Unmanned Aerial Vehicle ◽

Prediction Accuracy ◽

Laser Scanning ◽

Forest Stand ◽

Coniferous Plantation ◽

Point Density ◽

Stereo Photogrammetry ◽

Aerial Vehicle ◽

Stand Height ◽

Median Height

Plantation forests play a critical role in forest products and ecosystems. Unmanned aerial vehicle (UAV) remote sensing has become a promising technology in forest related applications. The stand heights will reflect the growth and competition of individual trees in plantation. UAV laser scanning (ULS) and UAV stereo photogrammetry (USP) can both be used to estimate stand heights using different algorithms. Thus, this study aimed to deeply explore the variations of four kinds of stand heights including mean height, Lorey’s height, dominated height, and median height of coniferous plantations using different models based on ULS and USP data. In addition, the impacts of thinned point density of 30 pts to 10 pts, 5 pts, 1 pts, and 0.8 pts/m2 were also analyzed. Forest stand heights were estimated from ULS and USP data metrics by linear regression and the prediction accuracy was assessed by 10-fold cross validation. The results showed that the prediction accuracy of the stand heights using metrics from USP was basically as good as that of ULS. Lorey’s height had the highest prediction accuracy, followed by dominated height, mean height, and median height. The correlation between height percentiles metrics from ULS and USP increased with the increased height. Different stand heights had their corresponding best height percentiles as variables based on stand height characteristics. Furthermore, canopy height model (CHM)-based metrics performed slightly better than normalized point cloud (NPC)-based metrics. The USP was not able to extract exact terrain information in a continuous coniferous plantation for forest canopy cover (CC) over 0.49. The combination of USP and terrain from ULS can be used to estimate forest stand heights with high accuracy. In addition, the estimation accuracy of each forest stand height was slightly affected by point density, which can also be ignored.

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Evaluation of UAV LiDAR for Mapping Coastal Environments

Remote Sensing ◽

10.3390/rs11242893 ◽

2019 ◽

Vol 11 (24) ◽

pp. 2893 ◽

Cited By ~ 14

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.

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Exclusion mapping of the genetic predisposition for cervical artery dissections by linkage analysis

Annals of Neurology ◽

10.1002/ana.10268 ◽

2002 ◽

Vol 52 (3) ◽

pp. 359-364 ◽

Cited By ~ 14

Author(s):

Caspar Grond-Ginsbach ◽

Bernhard Klima ◽

Ralf Weber ◽

Johannes Striegel ◽

Christine Fischer ◽

...

Keyword(s):

Linkage Analysis ◽

Genetic Predisposition ◽

Cervical Artery ◽

Exclusion Mapping

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

GIScience & Remote Sensing ◽

10.1080/15481603.2014.980086 ◽

2014 ◽

Vol 51 (6) ◽

pp. 731-747 ◽

Cited By ~ 22

Author(s):

Hone-Jay Chu ◽

Chi-Kuei Wang ◽

Min-Lang Huang ◽

Chung-Cheng Lee ◽

Chun-Yu Liu ◽

...

Keyword(s):

High Resolution ◽

Point Density

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