scholarly journals 3D-MODELING OF VEGETATION FROM LIDAR POINT CLOUDS AND ASSESSMENT OF ITS IMPACT ON FAÇADE SOLAR IRRADIATION

2016 ◽  
Vol XLII-2/W2 ◽  
pp. 67-70 ◽  
Author(s):  
G. Peronato ◽  
E. Rey ◽  
M. Andersen
Keyword(s):  
Confidence Intervals ◽  
Complex Shape ◽  
Energy Systems ◽  
Point Clouds ◽  
Urban Environments ◽  
Solar Irradiation ◽  
3D Geometry ◽  
Building Surfaces ◽  
3D Surfaces

The presence of vegetation can significantly affect the solar irradiation received on building surfaces. Due to the complex shape and seasonal variability of vegetation geometry, this topic has gained much attention from researchers. However, existing methods are limited to rooftops as they are based on 2.5D geometry and use simplified radiation algorithms based on view-sheds. This work contributes to overcoming some of these limitations, providing support for 3D geometry to include facades. Thanks to the use of ray-tracing-based simulations and detailed characterization of the 3D surfaces, we can also account for inter-reflections, which might have a significant impact on façade irradiation. <br><br> In order to construct confidence intervals on our results, we modeled vegetation from LiDAR point clouds as 3D convex hulls, which provide the biggest volume and hence the most conservative obstruction scenario. The limits of the confidence intervals were characterized with some extreme scenarios (e.g. opaque trees and absence of trees). <br><br> Results show that uncertainty can vary significantly depending on the characteristics of the urban area and the granularity of the analysis (sensor, building and group of buildings). We argue that this method can give us a better understanding of the uncertainties due to vegetation in the assessment of solar irradiation in urban environments, and therefore, the potential for the installation of solar energy systems.

scholarly journals AN ENERGY-BASED APPROACH FOR DETECTION AND CHARACTERIZATION OF SUBTLE ENTITIES WITHIN LASER SCANNING POINT-CLOUDS

2016 ◽  
Vol XLI-B3 ◽  
pp. 167-171
Author(s):  
Reuma Arav ◽  
Sagi Filin
Keyword(s):  
Laser Scanning ◽  
Optimization Problem ◽  
Point Clouds ◽  
Urban Environments ◽  
Natural Environments ◽  
Detection Scheme ◽  
Airborne Laser ◽  
The Common ◽  
Geomorphological Features

Airborne laser scans present an optimal tool to describe geomorphological features in natural environments. However, a challenge arises in the detection of such phenomena, as they are embedded in the topography, tend to blend into their surroundings and leave only a subtle signature within the data. Most object-recognition studies address mainly urban environments and follow a general pipeline where the data are partitioned into segments with uniform properties. These approaches are restricted to man-made domain and are capable to handle limited features that answer a well-defined geometric form. As natural environments present a more complex set of features, the common interpretation of the data is still manual at large. In this paper, we propose a data-aware detection scheme, unbound to specific domains or shapes. We define the recognition question as an energy optimization problem, solved by variational means. Our approach, based on the level-set method, characterizes geometrically local surfaces within the data, and uses these characteristics as potential field for minimization. The main advantage here is that it allows topological changes of the evolving curves, such as merging and breaking. We demonstrate the proposed methodology on the detection of collapse sinkholes.

scholarly journals AN ENERGY-BASED APPROACH FOR DETECTION AND CHARACTERIZATION OF SUBTLE ENTITIES WITHIN LASER SCANNING POINT-CLOUDS

2016 ◽  
Vol XLI-B3 ◽  
pp. 167-171
Author(s):  
Reuma Arav ◽  
Sagi Filin
Keyword(s):  
Laser Scanning ◽  
Optimization Problem ◽  
Point Clouds ◽  
Urban Environments ◽  
Natural Environments ◽  
Detection Scheme ◽  
Airborne Laser ◽  
The Common ◽  
Geomorphological Features

Airborne laser scans present an optimal tool to describe geomorphological features in natural environments. However, a challenge arises in the detection of such phenomena, as they are embedded in the topography, tend to blend into their surroundings and leave only a subtle signature within the data. Most object-recognition studies address mainly urban environments and follow a general pipeline where the data are partitioned into segments with uniform properties. These approaches are restricted to man-made domain and are capable to handle limited features that answer a well-defined geometric form. As natural environments present a more complex set of features, the common interpretation of the data is still manual at large. In this paper, we propose a data-aware detection scheme, unbound to specific domains or shapes. We define the recognition question as an energy optimization problem, solved by variational means. Our approach, based on the level-set method, characterizes geometrically local surfaces within the data, and uses these characteristics as potential field for minimization. The main advantage here is that it allows topological changes of the evolving curves, such as merging and breaking. We demonstrate the proposed methodology on the detection of collapse sinkholes.

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

&lt;p&gt;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.&lt;br&gt;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.&lt;br&gt;In the last decades, new remote techniques such as photogrammetry,&lt;em&gt; Light Detection and Ranging&lt;/em&gt; (LiDAR), &lt;em&gt;Unmanned Aerial Vehicle&lt;/em&gt; (UAV) and &lt;em&gt;InfraRed Thermography &lt;/em&gt;(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 &lt;em&gt;remote sensing &lt;/em&gt;methods, with particular reference to the analysis of high-resolution georeferenced photos. The first step consists in applying the &lt;em&gt;Structure from Motion&lt;/em&gt; (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.&lt;br&gt;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.&lt;br&gt;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.&lt;br&gt;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.&lt;br&gt;As a future perspective, a similar approach could be investigated for 3D analyses from point clouds.&lt;/p&gt;

Design and Characterization of a New MV PLC Coupler for Smart Electric Energy Systems

2018 ◽  
Author(s):  
Giovanni Artale ◽  
Antonio Cataliotti ◽  
Valentina Cosentino ◽  
Salvatore Guaiana ◽  
Dario Di Cara ◽  
...  
Keyword(s):  
Electric Energy ◽  
Energy Systems

scholarly journals URBAN SCENE CLASSIFICATION USING FEATURES EXTRACTED FROM PHOTOGRAMMETRIC POINT CLOUDS ACQUIRED BY UAV

2019 ◽  
Vol XLII-2/W13 ◽  
pp. 511-518 ◽  
Author(s):  
G. G. Pessoa ◽  
R. C. Santos ◽  
A. C. Carrilho ◽  
M. Galo ◽  
A. Amorim
Keyword(s):  
Point Clouds ◽  
Bare Soil ◽  
Urban Environments ◽  
Data Sources ◽  
Classification Error ◽  
Error Matrix ◽  
Urban Scenes ◽  
Optical Images ◽  
Combining Data ◽  
Quantitative Analyses

<p><strong>Abstract.</strong> Images and LiDAR point clouds are the two major data sources used by the photogrammetry and remote sensing community. Although different, the synergy between these two data sources has motivated exploration of the potential for combining data in various applications, especially for classification and extraction of information in urban environments. Despite the efforts of the scientific community, integrating LiDAR data and images remains a challenging task. For this reason, the development of Unmanned Aerial Vehicles (UAVs) along with the integration and synchronization of positioning receivers, inertial systems and off-the-shelf imaging sensors has enabled the exploitation of the high-density photogrammetric point cloud (PPC) as an alternative, obviating the need to integrate LiDAR and optical images. This study therefore aims to compare the results of PPC classification in urban scenes considering radiometric-only, geometric-only and combined radiometric and geometric data applied to the Random Forest algorithm. For this study the following classes were considered: buildings, asphalt, trees, grass, bare soil, sidewalks and power lines, which encompass the most common objects in urban scenes. The classification procedure was performed considering radiometric features (Green band, Red band, NIR band, NDVI and Saturation) and geometric features (Height – nDSM, Linearity, Planarity, Scatter, Anisotropy, Omnivariance and Eigenentropy). The quantitative analyses were performed by means of the classification error matrix using the following metrics: overall accuracy, recall and precision. The quantitative analyses present overall accuracy of 0.80, 0.74 and 0.98 for classification considering radiometric, geometric and both data combined, respectively.</p>

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.

Characterization of Similar Areas of Two 2D Point Clouds

2012 ◽  
pp. 509-516
Author(s):  
Sébastien Mavromatis ◽  
Christophe Palmann ◽  
Jean Sequeira
Keyword(s):  
Point Clouds

Relevance of Chemically Functionalized Nano-Fillers and Modified Nanocomposite in Energy Systems

2019 ◽  
pp. 10-65 ◽  
Author(s):  
Damilola Elizabeth Babatunde ◽  
Iheanacho Henry Denwigwe ◽  
Olubayo Moses Babatunde ◽  
Oluranti Agboola ◽  
Gbemisola Deborah Akinsipe
Keyword(s):  
Energy Efficiency ◽  
Energy Storage ◽  
Energy Saving ◽  
Alternative Energy ◽  
Energy Systems ◽  
Review Article ◽  
Polymeric Matrices ◽  
Energy Applications ◽  
Improved Performance

Reliable energy systems and advances in nanotechnology together will play key role in channeling future cutting edge inventions and developments in all spheres. In this review article, the pertinence of functionalizing nanofillers and modifying nanocomposites for improved performance in various energy applications such as energy conversion, energy efficiency, energy storage, alternative energy and energy saving are expounded. This article also presents structures and unique properties of commonly used nanofillers; advances, improvement potentials and characterization of nanocomposites used in energy systems. Theoretical and experimental literature reviewed revealed that nanofillers engender improved properties in polymeric matrices. Functionalization is applicable to all types of nanofillers in use today, a number of functionalized nanofillers are already commercially available; and more extensive research is needed to achieve optimal improved results with the use of nanofillers and nanocomposites in various fields of applications.

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