On Enhancing Ground Surface Detection from Sparse Lidar Point Cloud

Abstract. The accurate three-dimensional road surface information is highly useful for health assessment and maintenance of roads. It is basic information for further analysis in several applications including road surface settlement, pavement condition assessment and slope collapse. Mobile LiDAR system (MLS) is frequently used now a days to collect detail road surface and its surrounding information in terms three-dimensional (3D) point cloud. Extraction of road surface from volumetric point cloud data is still in infancy stage because of heavy data processing requirement and the complexity in the road environment. The extraction of roads especially rural road, where road-curb is not present is very tedious job especially in Indian roadway settings. Only a few studies are available, and none for Indian roads, in the literature for rural road detection. The limitations of existing studies are in terms of their lower accuracy, very slow speed of data processing and detection of other objects having similar characteristics as the road surface. A fast and accurate method is proposed for LiDAR data points of road surface detection, keeping in mind the essence of road surface extraction especially for Indian rural roads. The Mobile LiDAR data in XYZI format is used as input in the proposed method. First square gridding is performed and ground points are roughly extracted. Then planar surface detection using mathematical framework of principal component analysis (PCA) is performed and further road surface points are detected using similarity in intensity and height difference of road surface pointe in their neighbourhood.A case study was performed on the MLS data points captured along wide-street (two-lane road without curb) of 156m length along rural roadway site in the outskirt of Bengaluru city (South-West of India). The proposed algorithm was implemented on the MLS data of test site and its performance was evaluated it terms of recall, precision and overall accuracy that were 95.27%, 98.85% and 94.23%, respectively. The algorithm was found computationally time efficient. A 7.6 million MLS data points of size 27.1MB from test site were processed in 24 minutes using the available computational resources. The proposed method is found to work even for worst case scenarios, i.e., complex road environments and rural roads, where road boundary is not clear and generally merged with road-side features.

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AN EFFICIENT DEEP LEARNING APPROACH FOR GROUND POINT FILTERING IN AERIAL LASER SCANNING POINT CLOUDS

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xliii-b1-2021-31-2021 ◽

2021 ◽

Vol XLIII-B1-2021 ◽

pp. 31-38

Author(s):

A. Nurunnabi ◽

F. N. Teferle ◽

J. Li ◽

R. C. Lindenbergh ◽

A. Hunegnaw

Keyword(s):

Deep Learning ◽

Point Cloud ◽

Laser Scanning ◽

Three Dimensional ◽

Ground Surface ◽

Point Clouds ◽

Urban Environments ◽

Training Data ◽

Ground Point ◽

End To End

Abstract. Ground surface extraction is one of the classic tasks in airborne laser scanning (ALS) point cloud processing that is used for three-dimensional (3D) city modelling, infrastructure health monitoring, and disaster management. Many methods have been developed over the last three decades. Recently, Deep Learning (DL) has become the most dominant technique for 3D point cloud classification. DL methods used for classification can be categorized into end-to-end and non end-to-end approaches. One of the main challenges of using supervised DL approaches is getting a sufficient amount of training data. The main advantage of using a supervised non end-to-end approach is that it requires less training data. This paper introduces a novel local feature-based non end-to-end DL algorithm that generates a binary classifier for ground point filtering. It studies feature relevance, and investigates three models that are different combinations of features. This method is free from the limitations of point clouds’ irregular data structure and varying data density, which is the biggest challenge for using the elegant convolutional neural network. The new algorithm does not require transforming data into regular 3D voxel grids or any rasterization. The performance of the new method has been demonstrated through two ALS datasets covering urban environments. The method successfully labels ground and non-ground points in the presence of steep slopes and height discontinuity in the terrain. Experiments in this paper show that the algorithm achieves around 97% in both F1-score and model accuracy for ground point labelling.

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Development Of Ground Surface Detection System Using Microwave Radar Technology For Use With Mechanical Wild Blueberry Harvesting

10.32393/csme.2021.118 ◽

2021 ◽

Author(s):

Muhammad Saad ◽

Travis Esau ◽

Qamar Zaman ◽

Aitazaz Farooque ◽

Arnold Schumann

Keyword(s):

Detection System ◽

Ground Surface ◽

Surface Detection ◽

Microwave Radar ◽

Wild Blueberry

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Modeling Insolation, Multi-Spectral Imagery and LiDAR Point-Cloud Metrics to Predict Plant Diversity in a Temperate Montane Forest

Geographies ◽

10.3390/geographies1020006 ◽

2021 ◽

Vol 1 (2) ◽

pp. 79-103

Author(s):

Paul Christian Dunn ◽

Leonhard Blesius

Keyword(s):

Solar Radiation ◽

Plant Diversity ◽

Point Cloud ◽

Forest Canopy ◽

Ground Surface ◽

Vegetation Density ◽

Radiation Attenuation ◽

Area Index ◽

Spatial Continuity ◽

Response Variables

Incident solar radiation (insolation) passing through the forest canopy to the ground surface is either absorbed or scattered. This phenomenon, known as radiation attenuation, is measured using the extinction coefficient (K). The amount of radiation reaching the ground surface of a given site is effectively controlled by the canopy’s surface and structure, determining its suitability for plant species. Menhinick’s and Simpson’s biodiversity indexes were selected as spatially explicit response variables for the regression equation using canopy structure metrics as predictors. Independent variables include modeled area solar radiation, LiDAR-derived canopy height, effective leaf area index data derived from multi-spectral imagery and canopy strata metrics derived from LiDAR point-cloud data. The results support the hypothesis that (1) canopy surface and strata variability may be associated with understory species diversity due to radiation attenuation and the resultant habitat partitioning and that, (2) such a model can predict both this relationship and biodiversity clustering. The study data yielded significant correlations between predictor and response variables and were used to produce a multiple–linear model comprising canopy relief, the texture of heights, and vegetation density to predict understory plant diversity. When analyzed for spatial autocorrelation, the predicted biodiversity data exhibited non-random spatial continuity.

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Synthesis of Leaf-on and Leaf-off Unmanned Aerial Vehicle (UAV) Stereo Imagery for the Inventory of Aboveground Biomass of Deciduous Forests

Remote Sensing ◽

10.3390/rs11070889 ◽

2019 ◽

Vol 11 (7) ◽

pp. 889 ◽

Cited By ~ 5

Author(s):

Wenjian Ni ◽

Jiachen Dong ◽

Guoqing Sun ◽

Zhiyu Zhang ◽

Yong Pang ◽

...

Keyword(s):

Aboveground Biomass ◽

Point Cloud ◽

Forest Canopy ◽

Deciduous Forest ◽

Ground Surface ◽

Digital Terrain Model ◽

Canopy Height ◽

Surface Model ◽

Lidar Data ◽

Stereo Imagery

Applications of stereo imagery acquired by cameras onboard unmanned aerial vehicles (UAVs) as practical forest inventory tools are hindered by the unavailability of ground surface elevation. It is still a challenging issue to remove the elevation of ground surface in leaf-on stereo imagery to extract forest canopy height without the help of lidar data. This study proposed a method for the extraction of forest canopy height through the synthesis of UAV stereo imagery of leaf-on and leaf-off, and further demonstrated that the extracted forest canopy height could be used for the inventory of deciduous forest aboveground biomass (AGB). The points cloud of the leaf-on and leaf-off stereo imagery was firstly extracted by an algorithm of structure from motion (SFM) using the same ground control points (GCP). The digital surface model (DSM) was produced by rasterizing the point cloud of UAV leaf-on. The point cloud of UAV leaf-off was processed by iterative median filtering to remove vegetation points, and the digital terrain model (DTM) was generated by the rasterization of the filtered point cloud. The mean canopy height model (MCHM) was derived from the DSM subtracted by the DTM (i.e., DSM-DTM). Forest AGB maps were generated using models developed based on the MCHM and sampling plots of forest AGB and were evaluated by those of lidar. Results showed that forest AGB maps from UAV stereo imagery were highly correlated with those from lidar data with R2 higher than 0.94 and RMSE lower than 10.0 Mg/ha (i.e., relative RMSE 18.8%). These results demonstrated that UAV stereo imagery could be used as a practical inventory tool for deciduous forest AGB.

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Modeling Insolation, Multi-spectral Imagery and LiDAR Point-cloud Metrics to Predict Plant Diversity in a Temperate Montane Forest

10.20944/preprints202108.0078.v1 ◽

2021 ◽

Author(s):

Paul Christian Dunn ◽

Leonhard Blesius

Keyword(s):

Solar Radiation ◽

Plant Diversity ◽

Point Cloud ◽

Forest Canopy ◽

Ground Surface ◽

Vegetation Density ◽

Radiation Attenuation ◽

Area Index ◽

Spatial Continuity ◽

Response Variables

Incident solar radiation (insolation) passing through the forest canopy to the ground surface is either absorbed or scattered. This phenomenon, known as radiation attenuation, is measured using the extinction coefficient (K). The amount of radiation at the ground surface of a given site is effectively controlled by the canopy’s surface and structure, determining its suitability for plant species.Menhinick’s and Simpson biodiversity indexes were selected as spatially explicit response variables for the regression equation using canopy structure metrics as predictors. Independent variables include modeled area solar radiation, LiDAR derived canopy height, effective leaf area index data derived from multi-spectral imagery, and canopy strata metrics derived from LiDAR point-cloud data. The results support the hypothesis that, 1.) canopy surface and strata variability may be associated with understory species diversity due to habitat partitioning and radiation attenuation, and that, 2.) such a model can predict both this relationship and biodiversity clustering.The study data yielded significant correlations between predictor and response variables and was used to produce a multiple-linear model comprising canopy relief, texture of heights, and vegetation density to predict understory plant diversity. When analyzed for spatial autocorrelation, the predicted biodiversity data exhibited non-random spatial continuity.

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EXTRACTION OF GROUND SURFACE ALONG ROADWAY FROM MOBILE LIDAR DATA

ISPRS Annals of Photogrammetry Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-annals-iv-5-103-2018 ◽

2018 ◽

Vol IV-5 ◽

pp. 103-107

Author(s):

M. Yadav ◽

A. K. Singh ◽

B. Lohani

Keyword(s):

Point Cloud ◽

Total Error ◽

Ground Surface ◽

Test Site ◽

Lidar Data ◽

Type I ◽

Digital Elevation ◽

Data Points ◽

Elevation Model ◽

Road Widening

Abstract. High quality digital elevation model (DEM) is obtained from mobile LiDAR data and it is used in various applications like road widening, slope measurement of road side surfaces, and inundation of the roadway evaluation. Two steps algorithm is proposed to filter ground points using mobile LiDAR data. Initially unstructured input data is organized then standard deviation and flatness based approach is used to filter ground points. Proposed algorithm is tested on point cloud of test site located along 800m of roadway. Type I, Type II and total error are 2.11%, 2.21% and 2.15%, respectively with kappa is equal to 96.61% are computed using ground filtered points and reference data points.

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