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   Articles with "lidar point" as a keyword



A probabilistic graphical model for the classification of mobile LiDAR point clouds

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recommendations! Published in 2018 at "Isprs Journal of Photogrammetry and Remote Sensing"

DOI: 10.1016/j.isprsjprs.2018.04.018

Abstract: Abstract Mobile Light Detection And Ranging (LiDAR) point clouds have the characteristics of complex and incomplete scenes, uneven point density and noises, which raises great challenges for automatically interpreting 3D scene. Aiming at the problem… read more here.

Keywords: classification; point clouds; model; point ... See more keywords
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From LiDAR point cloud towards digital twin city: Clustering city objects based on Gestalt principles

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recommendations! Published in 2020 at "Isprs Journal of Photogrammetry and Remote Sensing"

DOI: 10.1016/j.isprsjprs.2020.07.020

Abstract: Abstract Recent advancement of remote sensing technologies has brought in accurate, dense, and inexpensive city-scale Light Detection And Ranging (LiDAR) point clouds, which can be utilized to model city objects (e.g., buildings, roads, and automobiles)… read more here.

Keywords: city; digital twin; city objects; lidar point ... See more keywords

LidarCSNet: A Deep Convolutional Compressive Sensing Reconstruction Framework for 3D Airborne Lidar Point Cloud

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recommendations! Published in 2021 at "ISPRS Journal of Photogrammetry and Remote Sensing"

DOI: 10.1016/j.isprsjprs.2021.08.019

Abstract: Abstract Lidar scanning is a widely used surveying and mapping technique ranging across remote-sensing applications involving topological, and topographical information. Typically, lidar point clouds, unlike images, lack inherent consistent structure and store redundant information thus… read more here.

Keywords: reconstruction; classification; point; point cloud ... See more keywords

Individual tree detection from airborne laser scanning data based on supervoxels and local convexity

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recommendations! Published in 2019 at "Remote Sensing Applications: Society and Environment"

DOI: 10.1016/j.rsase.2019.100242

Abstract: Abstract Precise mapping of urban green spaces is critical for sustainable development of urban ecosystem. LiDAR remote sensing technology has been proved to be valuable for capturing geometrical structure of natural and man-made resources. However,… read more here.

Keywords: methodology; individual tree; lidar point; point ... See more keywords

An Adaptive Point Cloud Downsampling Method for Large‐Scale Outdoor LiDAR Point Cloud Registration

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recommendations! Published in 2025 at "Electronics Letters"

DOI: 10.1049/ell2.70363

Abstract: One of the characteristics of outdoor scene point clouds is their large quantity, so it demands substantial computational resources for processing. Sampling thus plays a critical role in efficient processing. Most existing methods overlook scene… read more here.

Keywords: lidar point; point; point cloud; large scale ... See more keywords

Layer Stacking: A Novel Algorithm for Individual Forest Tree Segmentation from LiDAR Point Clouds

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recommendations! Published in 2017 at "Canadian Journal of Remote Sensing"

DOI: 10.1080/07038992.2017.1252907

Abstract: Abstract As light detection and ranging (LiDAR) technology advances, it has become common for datasets to be acquired at a point density high enough to capture structural information from individual trees. To process these data,… read more here.

Keywords: segmentation; point; layer stacking; lidar point ... See more keywords
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Measuring shallow-water bathymetric signal strength in lidar point attribute data using machine learning

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recommendations! Published in 2021 at "International Journal of Geographical Information Science"

DOI: 10.1080/13658816.2020.1867147

Abstract: ABSTRACT The goal of this work was to evaluate if routinely collected but seldom used airborne lidar metadata – ‘point attribute data’ (PAD) – analyzed using machine learning/artificial intelligence can improve extraction of shallow-water (less… read more here.

Keywords: point attribute; attribute data; lidar point; bathymetry ... See more keywords

Geometric transformation of images and LiDAR point clouds under quadratic constraint

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recommendations! Published in 2018 at "Remote Sensing Letters"

DOI: 10.1080/2150704x.2018.1499151

Abstract: ABSTRACT The combination of point and linear features can strengthen the robustness and accuracy of the registration of image and light detection and ranging (LiDAR) point clouds. The key point of registration is the establishment… read more here.

Keywords: geometric transformation; transformation; point; lidar point ... See more keywords
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An Approach to Map Visibility in the Built Environment From Airborne LiDAR Point Clouds

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recommendations! Published in 2021 at "IEEE Access"

DOI: 10.1109/access.2021.3066649

Abstract: Sustainable development can only be achieved with an innovative improvement from the way we currently analyze, design, build and manage our urban spaces. Current digital analysis and design methods for cities, such as visibility analysis,… read more here.

Keywords: point clouds; lidar point; approach map; visibility ... See more keywords

Ground Segmentation Algorithm for Sloped Terrain and Sparse LiDAR Point Cloud

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recommendations! Published in 2021 at "IEEE Access"

DOI: 10.1109/access.2021.3115664

Abstract: Distinguishing obstacles from ground is an essential step for common perception tasks such as object detection-and-tracking or occupancy grid maps. Typical approaches rely on plane fitting or local geometric features, but their performance is reduced… read more here.

Keywords: geometric features; ground; sloped terrain; lidar point ... See more keywords

Noise Point Detection From Airborne LiDAR Point Cloud Based on Spatial Hierarchical Directional Relationship

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recommendations! Published in 2022 at "IEEE Access"

DOI: 10.1109/access.2022.3196388

Abstract: In three-dimensional (3D) airborne light detection and ranging (LiDAR) point-cloud data acquisition, noise point clusters (such as cloud, birds and incomplete scanning ground points) and isolated points are usually generated in the scanning process. Detection… read more here.

Keywords: lidar point; detection; cloud; seed ... See more keywords