Classification of raw LiDAR point cloud using point-based methods with spatial features for 3D building reconstruction

Yastikli, Naci̇; Cetin, Zehra

doi:10.1007/s12517-020-06377-5

Cited by 13 publications

(7 citation statements)

References 36 publications

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“…Airborne LiDAR (Light Detection And Ranging) and photogrammetry point clouds are nowadays employed to produce high-quality features and models. These datasets are very efficient data for different applications in photogrammetry and remote sensing, such as building detection (Yi et al, 2021), 3D building reconstruction (Mahphood and Arefi, 2017, Tarsha Kurdi et al, 2021, Yastikli and Cetin, 2021, digital terrain model (DTM) generation (Mongus and Žalik, 2012), change detection (Liu et al, 2021), ground point filtering (Zeybek and Şanlıoğlu, 2019), point cloud registration (Favre et al, 2021), as well as building boundary extraction (Widyaningrum et al, 2019, Mahphood andArefi, 2022).…”

Section: Introductionmentioning

confidence: 99%

Density-Based Method for Building Detection From Lidar Point Cloud

Mahphood

Arefi

2023

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. In this paper, a new building detection method based on a density of LiDAR point clouds is proposed. In this method, trees, vegetation, and any objects that have points in a vertical plane or column are removed. In the density-based method, a cube is utilized to calculate the density therein. For each point, the cube is used to determine the number of neighbouring points. The density is calculated in two cases: 3D and 2D space. In 3D space, the volumetric density is calculated using the cube. In 2D space, all points are projected onto the horizontal plane, and the surface density is calculated using a square. Next, the two densities are compared and the points with different values in both cases are removed. The method leads to promising results in the removal of vegetation and trees. Moreover, the results achieve more than 94% completeness and correctness at the per-area level.

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Section: Introductionmentioning

confidence: 99%

Density-Based Method for Building Detection From Lidar Point Cloud

Mahphood

Arefi

2023

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…The templatematching [15], wavelet analysis [16], multi-stage filtering [17] and fitting functions [18] are one of the other methods for segmentation of the individual tree crowns. The loss of information owing to the interpolation of initial 3D point cloud to the grid structure is the major drawback of these segmentation methods [10,19].…”

Section: Introductionmentioning

confidence: 99%

Automatic detection of single street trees from airborne LiDAR data based on point segmentation methods

Cetin

Yastikli

2023

International Journal of Engineering and Geosciences

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As a primary element of urban ecosystem, street trees are very essential for environmental quality and aesthetic beauty of urban landscape. Street trees play a crucial role in everyday life of city inhabitants and therefore, comprehensive and accurate inventory information for street trees is required. In this research, an automatic method is proposed to detect single street trees from airborne Light Detection and Ranging (LiDAR) point cloud instead of traditional field work or photo interpretation. Firstly, raw LiDAR point cloud data have been classified to obtain high vegetation class with a hierarchical rule-based classification method. Then, the LiDAR points in high vegetation class were segmented with mean shift and Density Based Spatial Clustering of Applications with Noise (DBSCAN) algorithms to acquire single urban street trees in the Davutpasa Campus of Yildiz Technical University, Istanbul, Turkey. The accuracy assessment of the acquired street trees was also conducted using completeness and correctness analyses. The acquired results from urban study area approved the success of the proposed point-based approach for automatic detection of single street trees using LiDAR point cloud.

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“…Geometrical models characterized by a high LoD can be generated through an unmanned aerial vehicle (UAV) and terrestrial laser scanning (TLS) for data collection. The LiDAR technology integrates a laser scanner, the Global Positioning System (GPS), and inertial navigation systems (INS) to produce point clouds for buildings (Zhou and Gong, 2018;Yastikli and Cetin, 2021). The point clouds can provide high-resolution and accurate geometry information for the whole building, including windows, balconies, and other façade architectural elements.…”

Section: Introductionmentioning

confidence: 99%

Solar energy digitalization at high latitudes: A model chain combining solar irradiation models, a LiDAR scanner, and high-detail 3D building model

et al. 2022

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Solar mapping can contribute to exploiting more efficiently the solar energy potential in cities. Solar maps and 3D solar cadasters consist of visualization tools for solar irradiation analysis on urban surfaces (i.e., orography, roofs, and façades). Recent advancements in solar decomposition and transposition modeling and Light Detection and Ranging (LiDAR) scanning enable high levels of detail in 3D solar cadasters, in which the façade domain is considered beside the roof. In this study, a model chain to estimate solar irradiation impinging on surfaces with different orientations at high latitudes is developed and validated against experimental data. The case study is the Zero Emission Building Laboratory in Trondheim (Norway). The main stages of the workflow concern (1) data acquisition, (2) geometry detection, (3) solar radiation modeling, (4) data quality check, and (5) experimental validation. Data are recorded from seven pyranometers installed on the façades (4), roof (2), and pergola (1) and used to validate the Radiance-based numerical model over the period between June 21st and September 21st. This study investigates to which extent high-resolution data sources for both solar radiation and geometry are suitable to estimate global tilted irradiation at high latitudes. In general, the Radiance-based model is found to overestimate solar irradiation. Nonetheless, the hourly solar irradiation modeled for the two pyranometers installed on the roof has been experimentally validated in accordance with ASHRAE Guideline 14. When monthly outcomes are considered for validation, the east and the south pyranometers are validated as well. The achieved results build the ground for the further development of the 3D solar cadaster of Trondheim.

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Classification of raw LiDAR point cloud using point-based methods with spatial features for 3D building reconstruction

Cited by 13 publications

References 36 publications

Density-Based Method for Building Detection From Lidar Point Cloud

Density-Based Method for Building Detection From Lidar Point Cloud

Automatic detection of single street trees from airborne LiDAR data based on point segmentation methods

Solar energy digitalization at high latitudes: A model chain combining solar irradiation models, a LiDAR scanner, and high-detail 3D building model

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