scholarly journals Generation and Enrichment of Pedestrian Maps with Vertical Shadows in Urban Environments from Mobile Laser Scanning Data

2020 ◽  
Vol 1 (1) ◽  
pp. 14-20
Author(s):  
Jesus Balado Frias ◽  
Lucía Díaz-Vilariño ◽  
Ernesto Frías ◽  
Elena González
Keyword(s):  
Point Cloud ◽  
Input Data ◽  
Laser Scanning ◽  
Prolonged Exposure ◽  
Sun Exposure ◽  
Point Clouds ◽  
Urban Environments ◽  
The Sun ◽  
Input Point ◽  
Accessibility Barriers

Cities are becoming more pedestrian-friendly, reducing traffic and promoting physical activity and walking. However, prolonged exposure to the sun can cause sunburn and skin problems, so minimizing exposure to the sun while travelling is especially relevant at certain latitudes and in the summer months. This paper proposes a method for modelling urban contours and generating pedestrian maps with the location of shaded areas and accessibility barriers. The proposed method uses as input data a point cloud of an urban environment acquired with Mobile Laser Scanning. First, the input point cloud is segmented in ground points, obstacle points, and points causing shadows. Then, the three segmented point clouds are rasterized and the corresponded images are combined to obtain the navigable ground and the shaded areas. Finally, from the navigable ground, a navigation map is generated for pedestrians. To check the usefulness of this navigation map, a pathfinding algorithm is applied. The results show a correct generation of the navigable ground, and routes prioritizing the trajectory by shadow areas. Depending on the weighting between sun and shaded areas, the routes obtained show differences in distance travelled and sun exposure. The proposed method is sensitive to the existence of obstacles and noise in the point clouds.

scholarly journals AN EFFICIENT DEEP LEARNING APPROACH FOR GROUND POINT FILTERING IN AERIAL LASER SCANNING POINT CLOUDS

2021 ◽  
Vol XLIII-B1-2021 ◽  
pp. 31-38
Author(s):  
A. Nurunnabi ◽  
F. N. Teferle ◽  
J. Li ◽  
R. C. Lindenbergh ◽  
A. Hunegnaw
Keyword(s):  
Deep Learning ◽  
Point Cloud ◽  
Laser Scanning ◽  
Three Dimensional ◽  
Ground Surface ◽  
Point Clouds ◽  
Urban Environments ◽  
Training Data ◽  
Ground Point ◽  
End To End

Abstract. Ground surface extraction is one of the classic tasks in airborne laser scanning (ALS) point cloud processing that is used for three-dimensional (3D) city modelling, infrastructure health monitoring, and disaster management. Many methods have been developed over the last three decades. Recently, Deep Learning (DL) has become the most dominant technique for 3D point cloud classification. DL methods used for classification can be categorized into end-to-end and non end-to-end approaches. One of the main challenges of using supervised DL approaches is getting a sufficient amount of training data. The main advantage of using a supervised non end-to-end approach is that it requires less training data. This paper introduces a novel local feature-based non end-to-end DL algorithm that generates a binary classifier for ground point filtering. It studies feature relevance, and investigates three models that are different combinations of features. This method is free from the limitations of point clouds’ irregular data structure and varying data density, which is the biggest challenge for using the elegant convolutional neural network. The new algorithm does not require transforming data into regular 3D voxel grids or any rasterization. The performance of the new method has been demonstrated through two ALS datasets covering urban environments. The method successfully labels ground and non-ground points in the presence of steep slopes and height discontinuity in the terrain. Experiments in this paper show that the algorithm achieves around 97% in both F1-score and model accuracy for ground point labelling.

scholarly journals TOWARDS THE AUTOMATIC 3D PARAMETRIZATION OF NON-PLANAR SURFACES FROM POINT CLOUDS IN HBIM APPLICATIONS

2019 ◽  
Vol XLII-2/W15 ◽  
pp. 1023-1030
Author(s):  
J. Román ◽  
P. M. Lerones ◽  
J. Llamas ◽  
E. Zalama ◽  
J. Gómez-García-Bermejo
Keyword(s):  
Point Cloud ◽  
Input Data ◽  
Laser Scanning ◽  
Middle Age ◽  
Point Clouds ◽  
3D Laser Scanning ◽  
Architectural Elements ◽  
Planar Surfaces ◽  
Architectural Features ◽  
Renaissance Period

<p><strong>Abstract.</strong> 3D laser scanning and photogrammetric 3D reconstruction generate point clouds from which the geometry of BIM models can be created. However, a few methods do this automatically for concrete architectural elements, but in no case for the entirety of heritage assets. A novel procedure for the automatic recognition and parametrization of non-planar surfaces of heritage immovable assets is presented using point clouds as raw input data. The methodology is able to detect the most relevant architectural features in a point cloud and their interdependences through the analysis of the intersections of related elements. The non-planar surfaces detected, mainly cylinders, are studied in relation to the neighbouring planar surfaces present in the cloud so that the boundaries of both the planar and the non-planar surfaces are accurately defined. The procedure is applied to the emblematic Castle of Torrelobatón, located in Valladolid (Spain) to allow the cataloguing of required elements, as illustrative example of the European defensive architecture from the Middle age to the Renaissance period. Results and conclusions are reported to evaluate the performance of this approach.</p>

scholarly journals IMPROVING GPS TRAJECTORIES USING 3D CITY MODELS AND KINEMATIC POINT CLOUDS

2019 ◽  
Vol IV-4/W9 ◽  
pp. 35-42 ◽  
Author(s):  
Y. Dehbi ◽  
L. Lucks ◽  
J. Behmann ◽  
L. Klingbeil ◽  
L. Plümer
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Point Clouds ◽  
Autonomous Driving ◽  
Urban Environments ◽  
Support Vector ◽  
Mobile Mapping ◽  
General Applicability ◽  
Data Set ◽  
Building Models

Abstract. Accurate and robust positioning of vehicles in urban environments is of high importance for many applications (e.g. autonomous driving or mobile mapping). In the case of mobile mapping systems, a simultaneous mapping of the environment using laser scanning and an accurate positioning using GNSS is targeted. This requirement is often not guaranteed in shadowed cities where GNSS signals are usually disturbed, weak or even unavailable. Both, the generated point clouds and the derived trajectory are consequently imprecise. We propose a novel approach which incorporates prior knowledge, i.e. 3D building model of the environment, and improves the point cloud and the trajectory. The key idea is to benefit from the complementarity of both GNSS and 3D building models. The point cloud is matched to the city model using a point-to-plane ICP. An informed sampling of appropriate matching points is enabled by a pre-classification step. Support vector machines (SVMs) are used to discriminate between facade and remaining points. Local inconsistencies are tackled by a segment-wise partitioning of the point cloud where an interpolation guarantees a seamless transition between the segments. The full processing chain is implemented from the detection of facades in the point clouds, the matching between them and the building models and the update of the trajectory estimate. The general applicability of the implemented method is demonstrated on an inner city data set recorded with a mobile mapping system.

scholarly journals CREATING PRODUCT MODELS FROM POINT CLOUD OF CIVIL STRUCTURES BASED ON GEOMETRIC SIMILARITY

2015 ◽  
Vol XL-4/W5 ◽  
pp. 137-141 ◽  
Author(s):  
N. Hidaka ◽  
T. Michikawa ◽  
N. Yabuki ◽  
T. Fukuda ◽  
A. Motamedi
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Shape Matching ◽  
Point Clouds ◽  
Point Cloud Data ◽  
Civil Structures ◽  
Cloud Data ◽  
Input Point ◽  
Product Models ◽  
Part Decomposition

The existing civil structures must be maintained in order to ensure their expected lifelong serviceability. Careful rehabilitation and maintenance planning plays a significant role in that effort. Recently, construction information modelling (CIM) techniques, such as product models, are increasingly being used to facilitate structure maintenance. Using this methodology, laser scanning systems can provide point cloud data that are used to produce highly accurate and dense representations of civil structures. However, while numerous methods for creating a single surface exist, part decomposition is required in order to create product models consisting of more than one part. This research aims at the development of a surface reconstruction system that utilizes point cloud data efficiently in order to create complete product models. The research proposes using the application of local shape matching to the input point clouds in order to define a set of representative parts. These representative parts are then polygonized and copied to locations where the same types of parts exist. The results of our experiments show that the proposed method can efficiently create product models using input point cloud data.

scholarly journals A BIG DATA APPROACH FOR COMPREHENSIVE URBAN SHADOW ANALYSIS FROM AIRBORNE LASER SCANNING POINT CLOUDS

2019 ◽  
Vol IV-4/W8 ◽  
pp. 131-137
Author(s):  
A. V. Vo ◽  
D. F. Laefer
Keyword(s):  
Point Cloud ◽  
Input Data ◽  
Laser Scanning ◽  
Point Clouds ◽  
Airborne Laser Scanning ◽  
Data Set ◽  
Cloud Data ◽  
Airborne Laser ◽  
Alternative Approach ◽  
Hadoop Cluster

<p><strong>Abstract.</strong> Because of the importance of access to sunlight, shadow analysis is a common consideration in urban design, especially for dense urban developments. As shadow computation is computationally expensive, most urban shadow analysis tools have to date circumvented the high computational costs by representing urban complexity only through simplified geometric models. The simplification process removes details and adversely affects the level of realism of the ultimate results. In this paper, an alternative approach is presented by utilizing the highest level of detail and resolution captured in the geometric input data source, which is an extremely high-resolution airborne laser scanning point cloud (300 points/m2). To cope with the high computational demand caused by the use of this dense and detailed input data set, the Comprehensive Urban Shadow algorithm is introduced to distribute the computation for parallel processing on a Hadoop cluster. The proposed comprehensive urban shadow analysis solution is scalable, reasonably fast, and capable of preserving the original resolution and geometric detail of the original point cloud data.</p>

scholarly journals Linear-Based Incremental Co-Registration of MLS and Photogrammetric Point Clouds

2021 ◽  
Vol 13 (11) ◽  
pp. 2195
Author(s):  
Shiming Li ◽  
Xuming Ge ◽  
Shengfu Li ◽  
Bo Xu ◽  
Zhendong Wang
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Point Clouds ◽  
Registration Method ◽  
Cloud Data ◽  
Linear Feature ◽  
Data Registration ◽  
Source Point ◽  
Urban Remote Sensing ◽  
Single Data

Today, mobile laser scanning and oblique photogrammetry are two standard urban remote sensing acquisition methods, and the cross-source point-cloud data obtained using these methods have significant differences and complementarity. Accurate co-registration can make up for the limitations of a single data source, but many existing registration methods face critical challenges. Therefore, in this paper, we propose a systematic incremental registration method that can successfully register MLS and photogrammetric point clouds in the presence of a large number of missing data, large variations in point density, and scale differences. The robustness of this method is due to its elimination of noise in the extracted linear features and its 2D incremental registration strategy. There are three main contributions of our work: (1) the development of an end-to-end automatic cross-source point-cloud registration method; (2) a way to effectively extract the linear feature and restore the scale; and (3) an incremental registration strategy that simplifies the complex registration process. The experimental results show that this method can successfully achieve cross-source data registration, while other methods have difficulty obtaining satisfactory registration results efficiently. Moreover, this method can be extended to more point-cloud sources.

scholarly journals Revealing Changes in the Stem Form and Volume Allocation in Diverse Boreal Forests Using Two-Date Terrestrial Laser Scanning

Forests ◽  
2021 ◽  
Vol 12 (7) ◽  
pp. 835
Author(s):  
Ville Luoma ◽  
Tuomas Yrttimaa ◽  
Ville Kankare ◽  
Ninni Saarinen ◽  
Jiri Pyörälä ◽  
...  
Keyword(s):  
Tree Growth ◽  
Point Cloud ◽  
Laser Scanning ◽  
Terrestrial Laser Scanning ◽  
Point Clouds ◽  
Stem Volume ◽  
Accurate Information ◽  
Stem Form ◽  
Monitoring Period ◽  
Significant Difference

Tree growth is a multidimensional process that is affected by several factors. There is a continuous demand for improved information on tree growth and the ecological traits controlling it. This study aims at providing new approaches to improve ecological understanding of tree growth by the means of terrestrial laser scanning (TLS). Changes in tree stem form and stem volume allocation were investigated during a five-year monitoring period. In total, a selection of attributes from 736 trees from 37 sample plots representing different forest structures were extracted from taper curves derived from two-date TLS point clouds. The results of this study showed the capability of point cloud-based methods in detecting changes in the stem form and volume allocation. In addition, the results showed a significant difference between different forest structures in how relative stem volume and logwood volume increased during the monitoring period. Along with contributing to providing more accurate information for monitoring purposes in general, the findings of this study showed the ability and many possibilities of point cloud-based method to characterize changes in living organisms in particular, which further promote the feasibility of using point clouds as an observation method also in ecological studies.

scholarly journals Comparing High Accuracy t-LiDAR and UAV-SfM Derived Point Clouds for Geomorphological Change Detection

2021 ◽  
Vol 10 (6) ◽  
pp. 367
Author(s):  
Simoni Alexiou ◽  
Georgios Deligiannakis ◽  
Aggelos Pallikarakis ◽  
Ioannis Papanikolaou ◽  
Emmanouil Psomiadis ◽  
...  
Keyword(s):  
Soil Erosion ◽  
Change Detection ◽  
Point Cloud ◽  
Laser Scanning ◽  
Point Clouds ◽  
Mean Value ◽  
Detection Methods ◽  
Mountainous Catchments ◽  
Aerial Vehicle ◽  
Soil Erosion Assessment

Analysis of two small semi-mountainous catchments in central Evia island, Greece, highlights the advantages of Unmanned Aerial Vehicle (UAV) and Terrestrial Laser Scanning (TLS) based change detection methods. We use point clouds derived by both methods in two sites (S1 & S2), to analyse the effects of a recent wildfire on soil erosion. Results indicate that topsoil’s movements in the order of a few centimetres, occurring within a few months, can be estimated. Erosion at S2 is precisely delineated by both methods, yielding a mean value of 1.5 cm within four months. At S1, UAV-derived point clouds’ comparison quantifies annual soil erosion more accurately, showing a maximum annual erosion rate of 48 cm. UAV-derived point clouds appear to be more accurate for channel erosion display and measurement, while the slope wash is more precisely estimated using TLS. Analysis of Point Cloud time series is a reliable and fast process for soil erosion assessment, especially in rapidly changing environments with difficult access for direct measurement methods. This study will contribute to proper georesource management by defining the best-suited methodology for soil erosion assessment after a wildfire in Mediterranean environments.

scholarly journals Regional Modeling of Forest Fuels and Structural Attributes Using Airborne Laser Scanning Data in Oregon

2021 ◽  
Vol 13 (2) ◽  
pp. 261
Author(s):  
Francisco Mauro ◽  
Andrew T. Hudak ◽  
Patrick A. Fekety ◽  
Bryce Frank ◽  
Hailemariam Temesgen ◽  
...  
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Field Measurements ◽  
Point Clouds ◽  
Semiparametric Models ◽  
Airborne Laser Scanning ◽  
Parametric Models ◽  
Fuel Load ◽  
Forest Fuels ◽  
Airborne Laser

Airborne laser scanning (ALS) acquisitions provide piecemeal coverage across the western US, as collections are organized by local managers of individual project areas. In this study, we analyze different factors that can contribute to developing a regional strategy to use information from completed ALS data acquisitions and develop maps of multiple forest attributes in new ALS project areas in a rapid manner. This study is located in Oregon, USA, and analyzes six forest structural attributes for differences between: (1) synthetic (i.e., not-calibrated), and calibrated predictions, (2) parametric linear and semiparametric models, and (3) models developed with predictors computed for point clouds enclosed in the areas where field measurements were taken, i.e., “point-cloud predictors”, and models developed using predictors extracted from pre-rasterized layers, i.e., “rasterized predictors”. Forest structural attributes under consideration are aboveground biomass, downed woody biomass, canopy bulk density, canopy height, canopy base height, and canopy fuel load. Results from our study indicate that semiparametric models perform better than parametric models if no calibration is performed. However, the effect of the calibration is substantial in reducing the bias of parametric models but minimal for the semiparametric models and, once calibrations are performed, differences between parametric and semiparametric models become negligible for all responses. In addition, minimal differences between models using point-cloud predictors and models using rasterized predictors were found. We conclude that the approach that applies semiparametric models and rasterized predictors, which represents the easiest workflow and leads to the most rapid results, is justified with little loss in accuracy or precision even if no calibration is performed.

Application of close range photogrammetry in structural health monitoring by processing generated point cloud datasets

2021 ◽  
Author(s):  
Ali Mirzazade ◽  
Cosmin Popescu ◽  
Thomas Blanksvärd ◽  
Björn Täljsten
Keyword(s):  
Health Monitoring ◽  
Point Cloud ◽  
Laser Scanning ◽  
Point Clouds ◽  
Optical Data ◽  
Small Scale ◽  
Relevant Parameter ◽  
Bridge Inspection ◽  
Close Range Photogrammetry ◽  
Close Range

<p>In bridge inspection, vertical displacement is a relevant parameter for both short and long-term health monitoring. Assessing change in deflections could also simplify the assessment work for inspectors. Recent developments in digital camera technology and photogrammetry software enables point cloud with colour information (RGB values) to be generated. Thus, close range photogrammetry offers the potential of monitoring big and small-scale damages by point clouds. The current paper aims to monitor geometrical deviations in Pahtajokk Bridge, Northern Sweden, using an optical data acquisition technique. The bridge in this study is scanned two times by almost one year a part. After point cloud generation the datasets were compared to detect geometrical deviations. First scanning was carried out by both close range photogrammetry (CRP) and terrestrial laser scanning (TLS), while second scanning was performed by CRP only. Analyzing the results has shown the potential of CRP in bridge inspection.</p>

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