Automatic Filtering of Lidar Building Point Cloud in Case of Trees Associated to Building Roof

Kurdi, Fayez Tarsha; Gharineiat, Zahra; Campbell, Glenn; Awrangjeb, Mohammad; Dey, Emon Kumar

doi:10.3390/rs14020430

Cited by 19 publications

(18 citation statements)

References 35 publications

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“…For instance, Shao et al [8] showcased that combining Cloth Simulation Filtering (CSF) and region growing can effectively detect building roofs in large areas. Another approach entailed fusing region growing with elevation histogram analysis to separate roof points from point clouds [9]. Szutor [10] proposed a combination of two clustering methods and that the Fast Fourier Transform (FFT) can also be used for point cloud classification.…”

Section: Introductionmentioning

confidence: 99%

Application of Machine Learning to Detect Building Points in Photogrammetry-based Point Clouds

Hrutka

2024

Period. Polytech. Civil Eng.

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Different point cloud technologies such as Terrestrial Laser Scanners (TLS), Airborne Laser Scanners (ALS), Mobile Mapping Systems (MMS), and Unmanned Aerial Vehicles (UAV) have become increasingly more common in land surveying and geoinformatics over recent years. Thanks to these modern tools, experts can survey large areas cost-effectively with either high resolution or high accuracy. However, processing the point cloud, which consists of millions of points, can be a massive challenge. Manual processing of these large datasets can often be very time-consuming and hardware-demanding, and most of the time, only a limited part of the point cloud is used to derive the final products. The solution can be to automate the process as much as possible. Several advanced mathematical methods, especially Machine Learning (ML) algorithms, allow efficient automated processing of point clouds. This paper presents a processing chain to detect and separate building points from large-scale photogrammetry-based point clouds. The processing is based on the combination of Random Sample Consensus (RANSAC) and Machine Learning (ML) algorithms like Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Multi-Layer Perceptron (MLP). Presented methods were trained and tested on established and open available Heissigheim 3D (H3D) dataset to separate roof and vegetation points with over 90% accuracy in order to enhance the separation of building points on large-scale point clouds.

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Section: Introductionmentioning

confidence: 99%

Application of Machine Learning to Detect Building Points in Photogrammetry-based Point Clouds

Hrutka

2024

Period. Polytech. Civil Eng.

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“…By using single-point observations, an overview of the deformations in the studied construction cannot be obtained with the accuracy offered by the TLS and Unmanned Aerial Systems (UAS) 3D modeling methods [ 9 , 19 ]. The large datasets call for the implementation of suitable methods and techniques in order to process and analyze them [ 22 , 23 ].…”

Section: Introductionmentioning

confidence: 99%

“…One of the most important goals in this field of research is that of automation, to speed up the process as fast as possible, which will result in a decrease in measurement uncertainties by eliminating the errors caused by the human factor. The results should also lead to a decrease in the actual working time and in the costs related to each measurement operation [ 4 , 23 , 24 , 25 , 26 , 27 , 28 ].…”

Section: Introductionmentioning

confidence: 99%

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Non-Destructive Measurements for 3D Modeling and Monitoring of Large Buildings Using Terrestrial Laser Scanning and Unmanned Aerial Systems

Nap

CHIOREAN

Cira

et al. 2023

Sensors

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Along with the development and improvement of measuring technologies and techniques in recent times, new methods have appeared to model and monitor the behavior of land and constructions over time. The main purpose of this research was to develop a new methodology to model and monitor large buildings in a non-invasive way. The methods proposed in this research are non-destructive and can be used to monitor the behavior of buildings over time. A method of comparing point clouds obtained using terrestrial laser scanning combined with aerial photogrammetric methods was used in this study. The advantages and disadvantages of using non-destructive measurement techniques over the classic methods were also analyzed. With a building located in the University of Agricultural Sciences and Veterinary Medicine Cluj-Napoca campus as a case study and with the help of the proposed methods, the deformations over time of the facades of that building were determined. As one of the main conclusions of this case study, it can be stated that the proposed methods are adequate to model and monitor the behavior of constructions over time, ensuring a satisfactory degree of precision and accuracy. The methodology can be successfully applied to other similar projects.

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“…However, even sophisticated techniques will not be able to handle some intrinsic modeling problems [20,21]. The density of point clouds acquired during airborne scanning of urban areas differs sometimes between roofs and walls, and the presence of outliers and noisy data can lead to errors in the process of generating point clouds and incorporating clouds into the reference system [17,22,23].…”

Section: Introductionmentioning

confidence: 99%

Modeling Multi-Rotunda Buildings at LoD3 Level from LiDAR Data

Kurdi

Lewandowicz²,

Gharineiat

et al. 2023

Remote Sensing

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The development of autonomous navigation systems requires digital building models at the LoD3 level. Buildings with atypically shaped features, such as turrets, domes, and chimneys, should be selected as landmark objects in these systems. The aim of this study was to develop a method that automatically transforms segmented LiDAR (Light Detection And Ranging) point cloud to create such landmark building models. A detailed solution was developed for selected buildings that are solids of revolution. The algorithm relies on new methods for determining building axes and cross-sections. To handle the gaps in vertical cross-sections due to the absence of continuous measurement data, a new strategy for filling these gaps was proposed based on their automatic interpretation. In addition, potential points associated with building ornaments were used to improve the model. The results were presented in different stages of the modeling process in graphic models and in a matrix recording. Our work demonstrates that complicated buildings can be represented with a light and regular data structure. Further investigations are needed to estimate the constructed building model with vectorial models.

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Automatic Filtering of Lidar Building Point Cloud in Case of Trees Associated to Building Roof

Cited by 19 publications

References 35 publications

Application of Machine Learning to Detect Building Points in Photogrammetry-based Point Clouds

Application of Machine Learning to Detect Building Points in Photogrammetry-based Point Clouds

Non-Destructive Measurements for 3D Modeling and Monitoring of Large Buildings Using Terrestrial Laser Scanning and Unmanned Aerial Systems

Modeling Multi-Rotunda Buildings at LoD3 Level from LiDAR Data

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