Automatic 2d Modelling of Inner Roof Planes Boundaries Starting From Lidar Data

International Journal of Remote Sensing

2020

Despite the large number of studies conducted during the last three decades concerning 3D building modelling starting from Lidar data, two persistent problems still exist. The first one is the absence of some roof details which will not only disappear in the building roof model due to their small areas regarding the point density, but are also considered as undesirable noise among the modelling procedures. The second problem consists in that the involved segmentation algorithms do not perform well in the presence of noise in the building point cloud data. These two problems generate undesirable deformation in the final 3D building model. This paper proposes a new automatic approach for detecting and modelling the missing roof details in addition to improving the building roof segments. In this context, the error map matrix, which presents the deviations of points to their fitting planes, is considered. Moreover, this matrix is analyzed in order to deduce the mask of missing roof details. At this stage, a new numeric factor is defined for estimating the roof segmentation accuracy in addition to the validity of the roof segmentation result. Then, the building point cloud is enhanced in order to decrease the negative noise influence and, consequently, to improve the building roof segments. Finally, the functionality and the accuracy of the proposed approach are tested and discussed.

Section: Building Roof Segmentationmentioning

confidence: 99%

“…hence, the error map matrix is calculated (Tarsha Kurdi et al, 2019). This matrix facilitates 'pixel by pixel' analysis of deformations and the accuracy of the segmentation of building roof.…”

Section: Building Roof Segmentationmentioning

confidence: 99%

Automatic evaluation and improvement of roof segments for modelling missing details using Lidar data

International Journal of Remote Sensing

2020

“…The average values of RMSE are between 1.25 m and 0.5 m. Demir (2018) compares the shifts in X, Y and Z coordinates of roof vertices. Akca et al (2010), Dorninger and Pfeifer (2008), Erberink and Vossleman (2011), Ostrowski et al (2018), Park et al (2019), Sampath and Shan (2010), Tarsha and Tarsha Kurdi et al (2019) suggest using the Lidar point cloud as the reference data.…”

Section: Plane Boundaries and Roof Verticesmentioning

confidence: 99%

Comparison of LiDAR Building Point Cloud with Reference Model for Deep Comprehension of Cloud Structure

Canadian Journal of Remote Sensing

2020

This paper studies the fidelity level of the extracted Lidar (LIght Detection And Ranging) building point cloud in relation to the original building.In this context, the building point cloud is compared with a reference model. This comparison allows a deep understanding of the point cloud structure with respect to both the actual building and the constructed model. Consequently, the source of the incompatibility in a (reference or constructed) building model is determined and described. Thus, this study considers four aspects of the building point cloud.First, the errors of building point cloud extraction and the undesirable points are quantified. Hence, it is found that the percentages of undesirable points are sometimes considerable (between 15% and 70%). Second, the evaluation of the fit of the altimetry with the reference model shows that the roof plane equations calculated from Lidar data can be more precise than those of reference model, Third, it is noted that the segmentability level between different point densities and building typologies are variable. Finally, the per plane comparison mentions the incompatibility of the plane boundaries of point cloud with reference model. Moreover, considerable differences are noted between the theoretical and the true point densities.

“…First, Tarsha Kurdi, Awrangjeb, and Munir (2019) calculate the building digital surface model (DSM) by resembling that removes the undesirable points related to vertical elements such as building facades. Sometimes this operation does not eliminate all these points, which is why Park, Lee, Yoo, and Lee (2019) eliminate the unnecessary objects by applying the cube operator to segment the building point cloud into roof surface patches, including superstructures, removing unnecessary objects, detecting the boundaries of buildings, and determining the model key points for building modeling.…”

Section: Related Workmentioning

confidence: 99%

“…In order to estimate the accuracy of building models calculated from LiDAR data, Dorninger and Pfeifer (2008), Akca, Freeman, Sargent, and Gruen (2010), Sampath and Shan (2010), Elberink and Vossleman (2011), Park et al (2019), and Tarsha Kurdi et al (2019) suggest using the LiDAR point cloud as reference data. This choice has been adopted because it allows the study of deformation generated only by the modeling algorithm, independent of the quality of the input building point cloud.…”

Section: Automatic Calculation Of 2d Building Modelsmentioning

confidence: 99%

Automatic filtering and 2D modeling of airborne laser scanning building point cloud

Munir

2020

Transactions in GIS

This article suggests a new approach to automatic building footprint modeling using exclusively airborne LiDAR data. The first part of the suggested approach is the filtering of the building point cloud using the bias of the Z‐coordinate histogram. This operation aims to detect the points of roof class from the building point cloud. Hence, eight rules for histogram interpretation are suggested. The second part of the suggested approach is the roof modeling algorithm. It starts by detecting the roof planes and calculating their adjacency matrix. Hence, the roof plane boundaries are classified into four categories: (1) outer boundary; (2) inner plane boundaries; (3) roof detail boundaries; and (4) boundaries related to the missing planes. Finally, the junction relationships of roof plane boundaries are analyzed for detecting the roof vertices. With regard to the resulting accuracy quantification, the average values of the correctness and the completeness indices are employed in both approaches. In the filtering algorithm, their values are respectively equal to 97.5 and 98.6%, whereas they are equal to 94.0 and 94.0% in the modeling approach. These results reflect the high efficacy of the suggested approach.