Detection and thinning of street trees for calculation of morphological parameters using mobile laser scanner data

Husain, Arshad; Vaishya, Rakesh Chandra

doi:10.1016/j.rsase.2018.12.007

Cited by 10 publications

(7 citation statements)

References 14 publications

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“…Unlike image-based methods, MLS systems are invariant to variability in lighting and environmental conditions [5,6]. These HD maps have been used for the extraction and classification of road features such as road markings, drivable regions, road signs and others present in a vehicles MLS defined surrounding environment [7,8], as well as the development of methods for automated infrastructure assessment [9][10][11]. For example, Jung et al [12] uses an MLS system to extract drivable regions, road markings and road lines.…”

Section: Mobile Laser Systemmentioning

confidence: 99%

“…Street trees were first detected in the MLS system-generated point-cloud before being thinned by slicing on each x, y and z axis to reduce computations. After the instance of street trees were detected and processed morphological parameters such as diameters and heights were computed [9]. Other methods follow the steps of detection, classification, processing and estimation of geometric characteristics.…”

Section: Road Environment Geometric Parameter Estimationmentioning

confidence: 99%

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3D lidar point-cloud projection operator and transfer machine learning for effective road surface features detection and segmentation

Todd

Bielski

et al. 2021

Vis Comput

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The classification and extraction of road markings and lanes are of critical importance to infrastructure assessment, planning and road safety. We present a pipeline for the accurate segmentation and extraction of rural road surface objects in 3D lidar point-cloud, as well as a method to extract geometric parameters belonging to tar seal. To decrease the computational resources needed, the point-clouds were aggregated into a 2D image space before being transformed using affine transformations. The Mask R-CNN algorithm is then applied to the transformed image space to localize, segment and classify the road objects. The segmentation results for road surfaces and markings can then be used for geometric parameter estimation such as road widths estimation, while the segmentation results show that the efficacy of the existing Mask R-CNN to segment needle-type objects is improved by our proposed transformations.

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Section: Mobile Laser Systemmentioning

confidence: 99%

Section: Road Environment Geometric Parameter Estimationmentioning

confidence: 99%

3D lidar point-cloud projection operator and transfer machine learning for effective road surface features detection and segmentation

Todd

Bielski

et al. 2021

Vis Comput

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“…The trees on the streets are important component of urban vegetation as creating shades, decorating roads, alleviating urban environmental pollution, reducing street noise, decreasing CO2 emissions and building energy consumption, moderating heat accumulation in urban street canyons [1][2][3][4]. However, growth conditions of street trees can be very harsh as they have little space on the roadsides, and they can be affected by spread of diseases besides many natural and abiotic factors in single-species plantations [2,5].…”

Section: Introductionmentioning

confidence: 99%

“…The recent advent of the LiDAR systems provides rapid and costeffective three-dimensional (3D) data acquisition of street trees [1]. Several segmentation approaches have been recommended to detect single trees using airborne laser scanning data [3,7]. The initial techniques for identification of individual trees from LiDAR point cloud have been based on the methods which were developed to process optical imagery [8][9][10].…”

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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“…However, the poor mobility and occlusion problems of TLS make it almost impossible for data collection on an urban scale. Conversely, MLS has been used extensively in recent years for the collection and analysis of tree information in urban areas but with the main focus on street trees [23][24][25]. With regard to the limitations of the vehicle's sphere of activities, MLS suffers from the inconvenience of detecting trees in traffic-unfriendly areas and struggles to cover the entire urban area.…”

Section: Introductionmentioning

confidence: 99%

Tree Extraction from Airborne Laser Scanning Data in Urban Areas

You

et al. 2021

Remote Sensing

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Tree information in urban areas plays a significant role in many fields of study, such as ecology and environmental management. Airborne LiDAR scanning (ALS) excels at the fast and efficient acquisition of spatial information in urban-scale areas. Tree extraction from ALS data is an essential part of tree structural studies. Current raster-based methods that use canopy height models (CHMs) suffer from the loss of 3D structure information, whereas the existing point-based methods are non-robust in complex environments. Aiming at making full use of the canopy’s 3D structure information that is provided by point cloud data, and ensuring the method’s suitability in complex scenes, this paper proposes a new point-based method for tree extraction that is based on 3D morphological features. Considering the elevation deviations of the ALS data, we propose a neighborhood search method to filter out the ground and flat-roof points. A coarse extraction method, combining planar projection with a point density-filtering algorithm is applied to filter out distracting objects, such as utility poles and cars. After that, a Euclidean cluster extraction (ECE) algorithm is used as an optimization strategy for coarse extraction. In order to verify the robustness and accuracy of the method, airborne LiDAR data from Zhangye, Gansu, China and unmanned aircraft vehicle (UAV) LiDAR data from Xinyang, Henan, China were tested in this study. The experimental results demonstrated that our method was suitable for extracting trees in complex urban scenes with either high or low point densities. The extraction accuracy obtained for the airborne LiDAR data and UAV LiDAR data were 99.4% and 99.2%, respectively. In addition, a further study found that the aberrant vertical structure of the artificially pruned canopy was the main cause of the error. Our method achieved desirable results in different scenes, with only one adjustable parameter, making it an easy-to-use method for urban area studies.

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Detection and thinning of street trees for calculation of morphological parameters using mobile laser scanner data

Cited by 10 publications

References 14 publications

3D lidar point-cloud projection operator and transfer machine learning for effective road surface features detection and segmentation

3D lidar point-cloud projection operator and transfer machine learning for effective road surface features detection and segmentation

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

Tree Extraction from Airborne Laser Scanning Data in Urban Areas

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