scholarly journals Influence of LiDAR Point Cloud Density in the Geometric Characterization of Rooftops for Solar Photovoltaic Studies in Cities

2020 ◽  
Vol 12 (22) ◽  
pp. 3726
Author(s):  
María Sánchez-Aparicio ◽  
Susana Del Pozo ◽  
Jose Antonio Martín-Jiménez ◽  
Enrique González-González ◽  
Paula Andrés-Anaya ◽  
...  
Keyword(s):  
Computation Time ◽  
Point Clouds ◽  
Accurate Estimation ◽  
Lidar Data ◽  
Density Point ◽  
Industrial Buildings ◽  
Significant Difference ◽  
Point Density ◽  
Definition Of

The use of LiDAR (Light Detection and Ranging) data for the definition of the 3D geometry of roofs has been widely exploited in recent years for its posterior application in the field of solar energy. Point density in LiDAR data is an essential characteristic to be taken into account for the accurate estimation of roof geometry: area, orientation and slope. This paper presents a comparative study between LiDAR data of different point densities: 0.5, 1, 2 and 14 points/m2 for the measurement of the area of roofs of residential and industrial buildings. The data used for the study are the LiDAR data freely available by the Spanish Institute of Geography (IGN), which is offered according to the INSPIRE Directive. The results obtained show different behaviors for roofs with an area below and over 200 m2. While the use of low-density point clouds (0.5 point/m2) presents significant errors in the estimation of the area, the use of point clouds with higher density (1 or 2 points/m2) implies a great improvement in the area results, with no significant difference among them. The use of high-density point clouds (14 points/m2) also implies an improvement of the results, although the accuracy does not increase in the same ratio as the increase in density regarding 1 or 2 points/m2. Thus, the conclusion reached is that the geometrical characterization of roofs requires data acquisition with point density of 1 or 2 points/m2, and that higher point densities do not improve the results with the same intensity as they increase computation time.

scholarly journals FAST SEMANTIC SEGMENTATION OF 3D POINT CLOUDS WITH STRONGLY VARYING DENSITY

2016 ◽  
Vol III-3 ◽  
pp. 177-184 ◽  
Author(s):  
Timo Hackel ◽  
Jan D. Wegner ◽  
Konrad Schindler
Keyword(s):  
Computation Time ◽  
Semantic Segmentation ◽  
Point Clouds ◽  
Computationally Efficient ◽  
Terrestrial Lidar ◽  
Semantic Classification ◽  
Multi Scale ◽  
3D Point Clouds ◽  
Point Density ◽  
Careful Handling

We describe an effective and efficient method for point-wise semantic classification of 3D point clouds. The method can handle unstructured and inhomogeneous point clouds such as those derived from static terrestrial LiDAR or photogammetric reconstruction; and it is computationally efficient, making it possible to process point clouds with many millions of points in a matter of minutes. The key issue, both to cope with strong variations in point density and to bring down computation time, turns out to be careful handling of neighborhood relations. By choosing appropriate definitions of a point’s (multi-scale) neighborhood, we obtain a feature set that is both expressive and fast to compute. We evaluate our classification method both on benchmark data from a mobile mapping platform and on a variety of large, terrestrial laser scans with greatly varying point density. The proposed feature set outperforms the state of the art with respect to per-point classification accuracy, while at the same time being much faster to compute.

scholarly journals Mapping Forest Vertical Structure in Sogwang-ri Forest from Full-Waveform Lidar Point Clouds Using Deep Neural Network

2021 ◽  
Vol 13 (18) ◽  
pp. 3736
Author(s):  
Sung-Hwan Park ◽  
Hyung-Sup Jung ◽  
Sunmin Lee ◽  
Eun-Sook Kim
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Vertical Structure ◽  
Point Clouds ◽  
Canopy Height ◽  
Accurate Information ◽  
Lidar Data ◽  
Full Waveform ◽  
Density Maps ◽  
Point Density

The role of forests is increasing because of rapid land use changes worldwide that have implications on ecosystems and the carbon cycle. Therefore, it is necessary to obtain accurate information about forests and build forest inventories. However, it is difficult to assess the internal structure of the forest through 2D remote sensing techniques and fieldwork. In this aspect, we proposed a method for estimating the vertical structure of forests based on full-waveform light detection and ranging (FW LiDAR) data in this study. Voxel-based tree point density maps were generated by estimating the number of canopy height points in each voxel grid from the raster digital terrain model (DTM) and canopy height points after pre-processing the LiDAR point clouds. We applied an unsupervised classification algorithm to the voxel-based tree point density maps and identified seven classes by profile pattern analysis for the forest vertical types. The classification accuracy was found to be 72.73% from the validation from 11 field investigation sites, which was additionally confirmed through comparative analysis with aerial images. Based on this pre-classification reference map, which is assumed to be ground truths, the deep neural network (DNN) model was finally applied to perform the final classification. As a result of accuracy assessment, it showed accuracy of 92.72% with a good performance. These results demonstrate the potential of vertical structure estimation for extensive forests using FW LiDAR data and that the distinction between one-storied and two-storied forests can be clearly represented. This technique is expected to contribute to efficient and effective management of forests based on accurate information derived from the proposed method.

scholarly journals Estimating Forest Structure from UAV-Mounted LiDAR Point Cloud Using Machine Learning

2021 ◽  
Vol 13 (3) ◽  
pp. 352
Author(s):  
Romain Neuville ◽  
Jordan Steven Bates ◽  
François Jonard
Keyword(s):  
Machine Learning ◽  
Clustering Algorithm ◽  
Point Clouds ◽  
Accurate Estimation ◽  
Forest Stands ◽  
Scanning Angle ◽  
Site Specific ◽  
Segment Tree ◽  
Point Density ◽  
Closed Canopy

Monitoring the structure of forest stands is of high importance for forest managers to help them in maintaining ecosystem services. For that purpose, Unmanned Aerial Vehicles (UAVs) open new prospects, especially in combination with Light Detection and Ranging (LiDAR) technology. Indeed, the shorter distance from the Earth’s surface significantly increases the point density beneath the canopy, thus offering new possibilities for the extraction of the underlying semantics. For example, tree stems can now be captured with sufficient detail, which is a gateway to accurately locating trees and directly retrieving metrics—e.g., the Diameter at Breast Height (DBH). Current practices usually require numerous site-specific parameters, which may preclude their use when applied beyond their initial application context. To overcome this shortcoming, the machine learning Hierarchical Density-Based Spatial Clustering of Application of Noise (HDBSCAN) clustering algorithm was further improved and implemented to segment tree stems. Afterwards, Principal Component Analysis (PCA) was applied to extract tree stem orientation for subsequent DBH estimation. This workflow was then validated using LiDAR point clouds collected in a temperate deciduous closed-canopy forest stand during the leaf-on and leaf-off seasons, along with multiple scanning angle ranges. The results show that the proposed methodology can correctly detect up to 82% of tree stems (with a precision of 98%) during the leaf-off season and have a Maximum Scanning Angle Range (MSAR) of 75 degrees, without having to set up any site-specific parameters for the segmentation procedure. In the future, our method could then minimize the omission and commission errors when initially detecting trees, along with assisting further tree metrics retrieval. Finally, this research shows that, under the study conditions, the point density within an approximately 1.3-meter height above the ground remains low within closed-canopy forest stands even during the leaf-off season, thus restricting the accurate estimation of the DBH. As a result, autonomous UAVs that can both fly above and under the canopy provide a clear opportunity to achieve this purpose.

scholarly journals FAST SEMANTIC SEGMENTATION OF 3D POINT CLOUDS WITH STRONGLY VARYING DENSITY

2016 ◽  
Vol III-3 ◽  
pp. 177-184 ◽  
Author(s):  
Timo Hackel ◽  
Jan D. Wegner ◽  
Konrad Schindler
Keyword(s):  
Computation Time ◽  
Semantic Segmentation ◽  
Point Clouds ◽  
Computationally Efficient ◽  
Terrestrial Lidar ◽  
Semantic Classification ◽  
Multi Scale ◽  
3D Point Clouds ◽  
Point Density ◽  
Careful Handling

We describe an effective and efficient method for point-wise semantic classification of 3D point clouds. The method can handle unstructured and inhomogeneous point clouds such as those derived from static terrestrial LiDAR or photogammetric reconstruction; and it is computationally efficient, making it possible to process point clouds with many millions of points in a matter of minutes. The key issue, both to cope with strong variations in point density and to bring down computation time, turns out to be careful handling of neighborhood relations. By choosing appropriate definitions of a point’s (multi-scale) neighborhood, we obtain a feature set that is both expressive and fast to compute. We evaluate our classification method both on benchmark data from a mobile mapping platform and on a variety of large, terrestrial laser scans with greatly varying point density. The proposed feature set outperforms the state of the art with respect to per-point classification accuracy, while at the same time being much faster to compute.

scholarly journals A SLIDING WINDOW METHOD FOR DETECTING CORNERS OF OPENINGS FROM TERRESTRIAL LIDAR DATA

2018 ◽  
Vol XLII-4/W10 ◽  
pp. 97-103 ◽  
Author(s):  
J. Li ◽  
B. Xiong ◽  
F. Biljecki ◽  
G. Schrotter
Keyword(s):  
Sliding Window ◽  
Point Clouds ◽  
Lidar Data ◽  
Singular Boundary ◽  
Terrestrial Lidar ◽  
Building Models ◽  
Window Method ◽  
Highly Sensitive ◽  
Point Density ◽  
Sliding Window Method

<p><strong>Abstract.</strong> Architectural building models (LoD3) consist of detailed wall and roof structures including openings, such as doors and windows. Openings are usually identified through corner and edge detection, based on terrestrial LiDAR point clouds. However, singular boundary points are mostly detected by analysing their neighbourhoods within a small search area, which is highly sensitive to noise. In this paper, we present a global-wide sliding window method on a projected fa&amp;ccedil;ade to reduce the influence of noise. We formulate the gradient of point density for the sliding window to inspect the change of fa&amp;ccedil;ade elements. With derived symmetry information from statistical analysis, border lines of the changes are extracted and intersected generating corner points of openings. We demonstrate the performance of the proposed approach on the static and mobile terrestrial LiDAR data with inhomogeneous point density. The algorithm detects the corners of repetitive and neatly arranged openings and also recovers angular points within slightly missing data areas. In the future we will extend the algorithm to detect disordered openings and assist to fa&amp;ccedil;ade modelling, semantic labelling and procedural modelling.</p>

scholarly journals evaluation of intensity augmented ICP for terrestrial LiDAR data registration

2018 ◽  
Author(s):  
Sandeep Sasidharan
Keyword(s):  
Laser Scanner ◽  
Point Clouds ◽  
3D Models ◽  
Accurate Estimation ◽  
Lidar Data ◽  
Terrestrial Lidar ◽  
Data Registration ◽  
Novel Approach ◽  
Coarse Registration ◽  
Scan Registration

Final pub: Lohani, B., &amp; Sasidharan, S. (2017). An evaluation of intensity augmented ICP for terrestrial LiDAR data registration. Journal of Geomatics, 11(2). While using laser scanner for map-making or developing 3D models of objects, it is important to scan asite or an object from multiple viewpoints. These different scans are integrated to generate a complete point cloudwhich is then used for developing the map or 3D model of the site. ICP (Iterative Closest Point) is a standard algorithmfor registration of point clouds. However, in the absence of marked features which are geometrically distinct in thepoint clouds, which are being combined, this method sometime fails. This paper exploits the radiometric data that arealways obtained along with the coordinates and devises a novel approach for scan registration. Before usingradiometric data in registration process, the data are normalized. The algorithm presented in this paper works in twostages- Intensity Augmented ICP (IAICP) for coarse registration stage and conventional geometric ICP at the fineregistration stage. The proposed approach is successfully applied to a few test data captured by Optech ILRIS 36-Dresulting in an accurate estimation of the transformation parameters. A comparison of the conventional and intensityaugmented registration approaches is also presented. The results indicate the supremacy of IAICP over the ICP, as thelatter is found to fail in geometrically confusing cases while the intensity augmented ICP gives satisfactory result insuch cases.

scholarly journals CLASSIFICATION OF MOBILE LIDAR DATA USING VOX-NET AND AUXILIARY TRAINING SAMPLES

2019 ◽  
Vol XLII-2/W13 ◽  
pp. 1001-1006
Author(s):  
H. He ◽  
K. Khoshelham ◽  
C. Fraser
Keyword(s):  
State Of The Art ◽  
Point Clouds ◽  
Classification Performance ◽  
Lidar Data ◽  
Auxiliary Data ◽  
Training Samples ◽  
Recognition Ability ◽  
Point Density ◽  
The Difference

<p><strong>Abstract.</strong> The classification of mobile Lidar data is challenged by the complexity of objects in the point clouds and the limited number of available training samples. Incomplete shape, noise points and uneven point density make the extraction of features from point clouds relatively arduous. Additionally, the difference in point density, and size and shape of objects, restricts the utilization of labelled samples from other sources. To solve this problem, we explore the possibility of improving the classification performance of a state-of-the-art deep learning method, Vox-Net, by using auxiliary training samples from a different dataset. We compare the performance of Vox-Net trained with and without the auxiliary dataset. The comparison shows that more instances can be recognized in classes with auxiliary data. At the same time, the performance in classes without complementary data can deteriorate due to the low number of samples in these categories. To achieve a balance in the performance for different categories, we further replace the classification layer of Vox-Net with AdaBoost. The AdaBoost classification displays good recognition ability in classes with few instances but decreases the overall accuracy.</p>

LABORATORY TESTS OF THYROID FUNCTION IN HYPERTHYROIDISM II.

1969 ◽  
Vol 62 (4_Suppla) ◽  
pp. S23-S35
Author(s):  
B.-A. Lamberg ◽  
O. P. Heinonen ◽  
K. Liewendahl ◽  
G. Kvist ◽  
M. Viherkoski ◽  
...  
Keyword(s):  
Thyroid Function ◽  
Laboratory Tests ◽  
Goodness Of Fit ◽  
Point Of View ◽  
Accurate Estimation ◽  
Diagnostic Efficiency ◽  
Normal Limits ◽  
Significant Difference ◽  
Crossing Point ◽  
Hyperthyroid Patients

ABSTRACT The distributions of 13 variables based on 10 laboratory tests measuring thyroid function were studied in euthyroid controls and in patients with toxic diffuse or toxic multinodular goitre. Density functions were fitted to the empirical data and the goodness of fit was evaluated by the use of the χ2-test. In a few instances there was a significant difference but the material available was in some respects too small to allow a very accurate estimation. The normal limits for each variable was defined by the 2.5 and 97.5 percentiles. It appears that in some instances these limits are too rigorous from the practical point of view. It is emphasized that the crossing point of the functions for euthyroid controls and hyperthyroid patients may be a better limit to use. In a preliminary analysis of the diagnostic efficiency the variables of total or free hormone concentration in the blood proved clearily superior to all other variables.

Caracterização da Peneira Média em Clones de Coffea canephora

REVISTA FIMCA ◽  
2018 ◽  
Vol 5 (2) ◽  
pp. 28-31
Author(s):  
Darlan Darlan Sanches Barbosa Alves ◽  
Victor Mouzinho Spinelli ◽  
Marcos Santana Moraes ◽  
Carolina Augusto De Souza ◽  
Rodrigo da Silva Ribeiro ◽  
...  
Keyword(s):  
Genetic Improvement ◽  
Block Design ◽  
Coffea Canephora ◽  
Coffee Production ◽  
Randomized Block Design ◽  
Significant Difference ◽  
The Mean ◽  
Four Blocks ◽  
Selection Of

Introdução: O estado de Rondônia se destaca como tradicional produtor de café, sendo o segundo maior produtor brasileiro de C. canephora. No melhoramento genético de C. canephora, a seleção de plantas de elevada peneira média está associada à bebida de qualidade superior. Objetivos: O objetivo desse estudo foi avaliar a variabilidade genética de clones de C. canephora para o tamanho dos grãos, mensurado a partir da avaliação da peneira média (PM). Materiais e Métodos: Para isso, foi conduzido ao longo de dois anos agrícolas experimento no campo experimental da Embrapa no município de Ouro Preto do Oeste-RO, para a avaliação da peneira média de 130 genótipos (clones) com características das variedades botânicas Conilon, Robusta e híbridos intervarietais. O delineamento experimental utilizado foi de blocos ao acaso, com quatro repetições de quatro plantas por parcela. Resultados: Não houve resultados significativos para a interação clones X anos, indicando uma maior consistência no comportamento das plantas ao longo do tempo. Porém foram observadas diferenças significativas para o tamanho dos grãos entre os genótipos avaliados, possibilitando selecionar genótipos superiores. Conclusão: Os genótipos agruparam-se em cinco classes de acordo com o teste de média, subsidiando a caracterização de um gradiente de variabilidade da característica avaliada ABSTRACTIntroduction: Coffea canephora accounts for approximately 35% of the world's coffee production. The state of Rondônia stands out as a traditional coffee producer, being the second largest Brazilian producer of C. canephora. In the classical genetic improvement of C. anephora, the selection of plants of high average sieve is associated with a drink of superior quality. Objectives: The objective of this udy was to evaluate the genetic variability of Coffea canephora clones for the agronomic medium sieve (PM). Materials and Methods: The experiment was conducted in the experimental field of Embrapa, municipality of OuroPreto do Oeste-RO, located at coordinates 10º44'53 "S and 62º12'57". One hundred thirty genotypes (clones) of botanical characteristics Conilon, Robusta and intervarietal hybrids were evaluated in the agricultural years 2013-2014 and 2014-2015. The experimental design was a randomized block design with four blocks and four plants per plot, spacing 3.5 x 1.5 meters between plants. Results: Significant difference was found for the grain size. According to the F test, at 5% probability, the genotypes were grouped into five classes according to the mean test. Conclusion: The results obtained subsidized the characterization of a variability gradient of the evaluated trait.

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