Automated Reconstruction of Building LoDs from Airborne LiDAR Point Clouds Using an Improved Morphological Scale Space

Wang, Jinhu; Huang, Ronggang; Li, Jianping; Mao, Tian; Dai, Wei; Zhong, Rugang

doi:10.3390/rs9010014

Cited by 46 publications

(31 citation statements)

References 36 publications

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“…The Gestalt laws for the reconstruction were proximity, similarity, continuity, and closure. Detailed information about the preprocessing steps on the roof extraction and segmentation can be found in [46].…”

Section: Overview Of the Proposed Methodsmentioning

confidence: 99%

Towards Reconstructing 3D Buildings from ALS Data Based on Gestalt Laws

Wang

Dong

et al. 2018

Remote Sensing

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3D building models are an essential data infrastructure for various applications in a smart city system, since they facilitate spatial queries, spatial analysis, and interactive visualization. Due to the highly complex nature of building structures, automatically reconstructing 3D buildings from point clouds remains a challenging task. In this paper, a Roof Attribute Graph (RAG) method is proposed to describe the decomposition and topological relations within a complicated roof structure. Furthermore, top-down decomposition and bottom-up refinement processes are proposed to reconstruct roof parts according to the Gestalt laws, generating a complete structural model with a hierarchical topological tree. Two LiDAR datasets from Guangdong (China) and Vaihingen (Germany) with different point densities were used in our study. Experimental results, including the assessment on Vaihingen standardized by the International Society for Photogrammetry and Remote Sensing (ISPRS), show that the proposed method can be used to model 3D building roofs with high quality results as demonstrated by the completeness and correctness metrics presented in this paper.

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Section: Overview Of the Proposed Methodsmentioning

confidence: 99%

Towards Reconstructing 3D Buildings from ALS Data Based on Gestalt Laws

Wang

Dong

et al. 2018

Remote Sensing

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“…The previous studies [9,10,12,13] proposed up to 26 features for the classification of different objects from airborne LiDAR point cloud data. We concentrated these features as the initial whole feature set, as they were validated in the applications of building, tree, and car classification.…”

Section: Differences Between Selected Feature Setsmentioning

confidence: 99%

“…For the local neighborhood types, the spherical, vertical cylindrical, and k-nearest neighborhoods were commonly used in the classification of ground, tree, and buildings from airborne LiDAR points [9][10][11][12][13], but rarely from power lines. It is relatively unknown how such local neighborhood types work for power line extraction.…”

Section: Introductionmentioning

confidence: 99%

Systematic Comparison of Power Line Classification Methods from ALS and MLS Point Cloud Data

et al. 2018

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Power lines classification is important for electric power management and geographical objects extraction using LiDAR (light detection and ranging) point cloud data. Many supervised classification approaches have been introduced for the extraction of features such as ground, trees, and buildings, and several studies have been conducted to evaluate the framework and performance of such supervised classification methods in power lines applications. However, these studies did not systematically investigate all of the relevant factors affecting the classification results, including the segmentation scale, feature selection, classifier variety, and scene complexity. In this study, we examined these factors systematically using airborne laser scanning and mobile laser scanning point cloud data. Our results indicated that random forest and neural network were highly suitable for power lines classification in forest, suburban, and urban areas in terms of the precision, recall, and quality rates of the classification results. In contrast to some previous studies, random forest yielded the best results, while Naïve Bayes was the worst classifier in most cases. Random forest was the more robust classifier with or without feature selection for various LiDAR point cloud data. Furthermore, the classification accuracies were directly related to the selection of the local neighborhood, classifier, and feature set. Finally, it was suggested that random forest should be considered in most cases for power line classification.

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“…[13][14][15][16][17]; evaluation of different sets of characteristics calculated from a point and its neighborhood [18][19][20]; or multiclass classification techniques based on supervised machine learning algorithms [21][22][23]. In this way, it is possible to locate different elements regardless the complexity of the scenario going from simple geometries such as roofs [24] or columns [25] to complex geometries such as trees [26,27], buildings [24,28,29] or vehicles [30].…”

Section: Introductionmentioning

confidence: 99%

Automatic Evaluation of Photovoltaic Power Stations from High-Density RGB-T 3D Point Clouds

et al. 2017

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A low-cost unmanned aerial platform (UAV) equipped with RGB (Red, Green, Blue) and thermographic sensors is used for the acquisition of all the data needed for the automatic detection and evaluation of thermal pathologies on photovoltaic (PV) surfaces and geometric defects in the mounting on photovoltaic power stations. RGB imagery is used for the generation of a georeferenced 3D point cloud through digital image preprocessing, photogrammetric and computer vision algorithms. The point cloud is complemented with temperature values measured by the thermographic sensor and with intensity values derived from the RGB data in order to obtain a multidimensional product (5D: 3D geometry plus temperature and intensity on the visible spectrum). A segmentation workflow based on the proper integration of several state-of-the-art geomatic and mathematic techniques is applied to the 5D product for the detection and sizing of thermal pathologies and geometric defects in the mounting in the PV panels. It consists of a three-step segmentation procedure, involving first the geometric information, then the radiometric (RGB) information, and last the thermal data. No configuration of parameters is required. Thus, the methodology presented contributes to the automation of the inspection of PV farms, through the maximization of the exploitation of the data acquired in the different spectra (visible and thermal infrared bands). Results of the proposed workflow were compared with a ground truth generated according to currently established protocols and complemented with a topographic survey. The proposed methodology was able to detect all pathologies established by the ground truth without adding any false positives. Discrepancies in the measurement of damaged surfaces regarding established ground truth, which can reach the 5% of total panel surface for the visual inspection by an expert operator, decrease with the proposed methodology under the 2%. The geometric evaluation of the facilities presents discrepancies regarding the ground truth lower than one degree for angular parameters (azimuth and tilt) and lower than 0.05 m 2 for the area of each solar panel.

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Automated Reconstruction of Building LoDs from Airborne LiDAR Point Clouds Using an Improved Morphological Scale Space

Cited by 46 publications

References 36 publications

Towards Reconstructing 3D Buildings from ALS Data Based on Gestalt Laws

Towards Reconstructing 3D Buildings from ALS Data Based on Gestalt Laws

Systematic Comparison of Power Line Classification Methods from ALS and MLS Point Cloud Data

Automatic Evaluation of Photovoltaic Power Stations from High-Density RGB-T 3D Point Clouds

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