A Knowledge Base for Automatic Feature Recognition from Point Clouds in an Urban Scene

Xing, Xufeng; Mostafavi, Mir Abolfazl; Chavoshi, Seyed Hossein

doi:10.3390/ijgi7010028

Cited by 14 publications

(14 citation statements)

References 43 publications

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“…The difference in the leaf inclination angle distribution between different parts within a tree was observed, and a detailed tree structural analysis was conducted. We found that this method enables accurate and efficient leaf inclination angle distribution.A 3D scanner called lidar (light detection and ranging) provides highly accurate and dense 3D point measurements [15,16]. The lidar is very useful for the retrieval of plant structural parameters.…”

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confidence: 99%

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Estimation of Leaf Inclination Angle in Three-Dimensional Plant Images Obtained from Lidar

Itakura

Hosoi

2019

Remote Sensing

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The leaf inclination angle is a fundamental variable for determining the plant profile. In this study, the leaf inclination angle was estimated automatically from voxel-based three-dimensional (3D) images obtained from lidar (light detection and ranging). The distribution of the leaf inclination angle within a tree was then calculated. The 3D images were first converted into voxel coordinates. Then, a plane was fitted to some voxels surrounding the point (voxel) of interest. The inclination angle and azimuth angle were obtained from the normal. The measured leaf inclination angle and its actual value were correlated and indicated a high correlation (R 2 = 0.95). The absolute error of the leaf inclination angle estimation was 2.5 • . Furthermore, the leaf inclination angle can be estimated even when the distance between the lidar and leaves is about 20 m. This suggests that the inclination angle estimation of leaves in a top part is reliable. Then, the leaf inclination angle distribution within a tree was calculated. The difference in the leaf inclination angle distribution between different parts within a tree was observed, and a detailed tree structural analysis was conducted. We found that this method enables accurate and efficient leaf inclination angle distribution.A 3D scanner called lidar (light detection and ranging) provides highly accurate and dense 3D point measurements [15,16]. The lidar is very useful for the retrieval of plant structural parameters. Trunk diameter and plant height can be estimated accurately [17][18][19]. Leaf area and parameters related to leaf area can be calculated from the 3D images of plants obtained by the lidar [20][21][22][23][24]. Other than the parameters above, tree volume and species can be estimated [25][26][27]. The detailed and accurate information from lidar provides a useful tool, for example, in the field of plant phenotyping [28]. Guo et al (2018) developed a high-throughput crop phenotyping platform with lidar that can retrieve various crop structural and physiological relevant parameters efficiently [29]. For more detailed information of the lidar application for plant structural parameters, please refer to the comprehensive reviews in [30,31].Some previous studies on leaf inclination angle estimation with 3D images are available [32][33][34][35][36][37]. In these studies, the points that composed each leaf were fitted to a plane by a least-squares method, and normals to the planes were calculated. Although this method is quite effective for leaf inclination angle estimation, each leaf should be selected manually in the 3D images one by one. Thus, exploring the distribution of the leaf inclination angle is very tedious and time-consuming. Further, owing to the manual operation, the number of leaves that can be examined is limited.To overcome this problem, a previous study [38] converted 3D point cloud images obtained with structure from a motion method into a voxel-based 3D image. In voxel-based 3D models, a geographical space is systematically decomposed into a ...

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confidence: 99%

“…A 3D scanner called lidar (light detection and ranging) provides highly accurate and dense 3D point measurements [15,16]. The lidar is very useful for the retrieval of plant structural parameters.…”

mentioning

confidence: 99%

Estimation of Leaf Inclination Angle in Three-Dimensional Plant Images Obtained from Lidar

Itakura

Hosoi

2019

Remote Sensing

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“…[12]. Ontologies have been employed for solving various research problems such as GEographic Object-Based Image Analysis (GEOBIA) [13][14][15], information extraction and retrieval [16,17], information integration [18,19], linked data [20][21][22] and geoprocessing workflows [23], geospatial data provenance on the web [24], interpretation of natural language descriptions [25], automatic feature recognition from point clouds [26], and sketch map interpretation [27].…”

Section: Semantic Information Formalizationmentioning

confidence: 99%

A Review of Geospatial Semantic Information Modeling and Elicitation Approaches

Kokla

Guilbert

2020

IJGI

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The present paper provides a review of two research topics that are central to geospatial semantics: information modeling and elicitation. The first topic deals with the development of ontologies at different levels of generality and formality, tailored to various needs and uses. The second topic involves a set of processes that aim to draw out latent knowledge from unstructured or semi-structured content: semantic-based extraction, enrichment, search, and analysis. These processes focus on eliciting a structured representation of information in various forms such as: semantic metadata, links to ontology concepts, a collection of topics, etc. The paper reviews the progress made over the last five years in these two very active areas of research. It discusses the problems and the challenges faced, highlights the types of semantic information formalized and extracted, as well as the methodologies and tools used, and identifies directions for future research.

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“…Accurate recognition and localization of traffic signs are significant in intelligent traffic-related applications (e.g., autonomous driving [101] and driver-assisted systems), which help machines to respond in a timely and accurate manner in different situations. The high accuracy of points (about 1 cm) in the meter-level area along with imagery provides promising solutions for traffic sign detection (TSD) and recognition (TSR) while using mobile laser scanning techniques.…”

Section: Traffic Sign Detectionmentioning

confidence: 99%

Mobile Laser Scanned Point-Clouds for Road Object Detection and Extraction: A Review

et al. 2018

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The mobile laser scanning (MLS) technique has attracted considerable attention for providing high-density, high-accuracy, unstructured, three-dimensional (3D) geo-referenced point-cloud coverage of the road environment. Recently, there has been an increasing number of applications of MLS in the detection and extraction of urban objects. This paper presents a systematic review of existing MLS related literature. This paper consists of three parts. Part 1 presents a brief overview of the state-of-the-art commercial MLS systems. Part 2 provides a detailed analysis of on-road and off-road information inventory methods, including the detection and extraction of on-road objects (e.g., road surface, road markings, driving lines, and road crack) and off-road objects (e.g., pole-like objects and power lines). Part 3 presents a refined integrated analysis of challenges and future trends. Our review shows that MLS technology is well proven in urban object detection and extraction, since the improvement of hardware and software accelerate the efficiency and accuracy of data collection and processing. When compared to other review papers focusing on MLS applications, we review the state-of-the-art road object detection and extraction methods using MLS data and discuss their performance and applicability. The main contribution of this review demonstrates that the MLS systems are suitable for supporting road asset inventory, ITS-related applications, high-definition maps, and other highly accurate localization services.

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A Knowledge Base for Automatic Feature Recognition from Point Clouds in an Urban Scene

Cited by 14 publications

References 43 publications

Estimation of Leaf Inclination Angle in Three-Dimensional Plant Images Obtained from Lidar

Estimation of Leaf Inclination Angle in Three-Dimensional Plant Images Obtained from Lidar

A Review of Geospatial Semantic Information Modeling and Elicitation Approaches

Mobile Laser Scanned Point-Clouds for Road Object Detection and Extraction: A Review

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