Grid-based building outline extraction from ready-made building points

Mahphood, A.; Arefi, Hossein

doi:10.1016/j.autcon.2022.104321

Cited by 12 publications

(6 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Xinjiekou sub-district has achieved an urbanization rate of around 100% [34]. This study employed ArcGIS software to examine the distribution of urban building height based on the building outline data provided [35,36]. Furthermore, this study employed Rhino software to import building outline data in order to quantify the width and height of street canyons, taking into account the fluctuating distance between different structures within the metropolitan region.…”

Section: Study Area and Urban Street Canyon Modelmentioning

confidence: 99%

A Study on the Feasibility and Strategy of Developing Photovoltaic Integrated Shading Devices in Street Canyons

Wang,

et al. 2024

Buildings

View full text Add to dashboard Cite

The measurement and analysis of the spatial attributes of the street canyon hold significant importance in the advancement of photovoltaic integrated shading devices (PVSDs). This study offers the space aspect ratio index AR(h) as a more efficient method for determining the optimal location for installing PVSDs on building facades in various street canyons. The AR(h) index addresses the limitations of the current quantitative index. This study examined the evolving regulations of indoor thermal conditions, natural lighting, and the performance of PVSDs in various street canyons. It assessed the viability of implementing PVSDs in different canyons and suggested development plans based on the variation law. The findings demonstrated that AR(h) is capable of effectively assessing and directing the implementation of PVSDs. When AR(h) is below 0.6, the shade of surrounding buildings has the least impact on the photovoltaic power output and building energy consumption in various street canyons. In this scenario, the building has the largest yearly energy-saving rate, making it highly ideal for implementing PVSDs on the building façade. In summary, the suitability of the AR(h) index in various street sceneries was assessed, offering valuable insights for the widespread implementation of PVSDs and street planning, thereby optimizing the utilization of solar energy. The findings of this study will be advantageous in diminishing the utilization of non-renewable energy sources in urban areas and mitigating carbon emissions to safeguard the environment.

show abstract

Section: Study Area and Urban Street Canyon Modelmentioning

confidence: 99%

A Study on the Feasibility and Strategy of Developing Photovoltaic Integrated Shading Devices in Street Canyons

Wang,

et al. 2024

Buildings

View full text Add to dashboard Cite

show abstract

“…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.

View full text Add to dashboard Cite

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.

show abstract

“…In the pixel-based technique, the points are converted into raster images such as a digital surface model (DSM) (Brédif et al, 2013, Yu et al, 2021 or a binary image (Galvanin andDal Poz, 2011, Yang et al, 2013) or a normalized digital surface model (nDSM) (Haithcoat et al, 2001), or a 3D raster (Gorte and Pfeifer, 2004). In the point-based technique, points are divided into a regular grid of cells in 2D space Arefi, 2017, Mahphood andArefi, 2022) or a grid of voxels (cubic) in 3D space (Xiong et al, 2021, Xu et al, 2021. For the 2D grid, a point is selected instead of the points within the cell.…”

Section: Introductionmentioning

confidence: 99%

“…Usually, the selected points are the centers of the cells or voxels. Nevertheless, the point-based grid technique is used in many applications, such as filtering ground * Corresponding author points (Qin et al, 2018, Mahphood andArefi, 2020a), building boundary extraction (Zhou andNeumann, 2008, Mahphood andArefi, 2022), digital terrain model (DTM) generation (Mongus and Žalik, 2012), segmentation (Deschaud andGoulette, 2010, Wang andTseng, 2011), forest canopy vertical space characterization (Chasmer et al, 2004, Popescu andZhao, 2008), skeletonization (Bucksch et al, 2009), and point cloud registration (Xiong et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

New and Rapid Methods for Gridding of Point Clouds

Mahphood

Arefi²

2023

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

View full text Add to dashboard Cite

Abstract. Point clouds are among the most important photogrammetry and remote sensing data that provide three-dimensional information about objects and features. These points have unorganized, irregular structures and do not have uniform spacing. In this paper, two innovative methods for converting the point cloud into a grid are presented. The first method is called the mesh method. It generates all possible 3D grid points covering the point cloud. Then, the distances between the generated grid points and the original points are measured. Based on these distances, the required grid points are determined. The second method is the resampling method. It uses three equations to resample the original point so that the points are moved to new coordinates to create the grid points. The methods are tested on three different point clouds. The results show that the two methods are very fast. However, the resampling method is much faster, especially for very large areas.

show abstract

Grid-based building outline extraction from ready-made building points

Cited by 12 publications

References 27 publications

A Study on the Feasibility and Strategy of Developing Photovoltaic Integrated Shading Devices in Street Canyons

A Study on the Feasibility and Strategy of Developing Photovoltaic Integrated Shading Devices in Street Canyons

Density-Based Method for Building Detection From Lidar Point Cloud

New and Rapid Methods for Gridding of Point Clouds

Contact Info

Product

Resources

About