Gotowe bibliografie tematyczne / LiDAR; Classification; Modelling

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Gotowa bibliografia na temat „LiDAR; Classification; Modelling”

Autor: Grafiati
Data publikacji: 28 lipca 2024
Data aktualizacji: 19 lipca 2025

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Artykuły w czasopismach na temat "LiDAR; Classification; Modelling"

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Yastikli, N., and Z. Cetin. "CLASSIFICATION OF LiDAR DATA WITH POINT BASED CLASSIFICATION METHODS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B3 (June 9, 2016): 441–45. http://dx.doi.org/10.5194/isprs-archives-xli-b3-441-2016.

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LiDAR is one of the most effective systems for 3 dimensional (3D) data collection in wide areas. Nowadays, airborne LiDAR data is used frequently in various applications such as object extraction, 3D modelling, change detection and revision of maps with increasing point density and accuracy. The classification of the LiDAR points is the first step of LiDAR data processing chain and should be handled in proper way since the 3D city modelling, building extraction, DEM generation, etc. applications directly use the classified point clouds. The different classification methods can be seen in recen
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Yastikli, N., and Z. Cetin. "CLASSIFICATION OF LiDAR DATA WITH POINT BASED CLASSIFICATION METHODS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B3 (June 9, 2016): 441–45. http://dx.doi.org/10.5194/isprsarchives-xli-b3-441-2016.

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LiDAR is one of the most effective systems for 3 dimensional (3D) data collection in wide areas. Nowadays, airborne LiDAR data is used frequently in various applications such as object extraction, 3D modelling, change detection and revision of maps with increasing point density and accuracy. The classification of the LiDAR points is the first step of LiDAR data processing chain and should be handled in proper way since the 3D city modelling, building extraction, DEM generation, etc. applications directly use the classified point clouds. The different classification methods can be seen in recen
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Yastikli, N., and Z. Cetin. "AUTOMATIC 3D BUILDING MODEL GENERATIONS WITH AIRBORNE LiDAR DATA." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences IV-4/W4 (November 13, 2017): 411–14. http://dx.doi.org/10.5194/isprs-annals-iv-4-w4-411-2017.

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LiDAR systems become more and more popular because of the potential use for obtaining the point clouds of vegetation and man-made objects on the earth surface in an accurate and quick way. Nowadays, these airborne systems have been frequently used in wide range of applications such as DEM/DSM generation, topographic mapping, object extraction, vegetation mapping, 3 dimensional (3D) modelling and simulation, change detection, engineering works, revision of maps, coastal management and bathymetry. The 3D building model generation is the one of the most prominent applications of LiDAR system, whi
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Cassanelli, Davide, Stefano Cattini, Lorenzo Medici, Luca Ferrari, and Luigi Rovati. "A simple experimental method to estimate and benchmark automotive LIDARs performance in fog." Acta IMEKO 13, no. 4 (2024): 1–8. https://doi.org/10.21014/actaimeko.v13i4.1885.

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LiDARs hold promise for various automotive applications, but their performance in adverse weather conditions remains a severe limitation. Indeed, fog can compromise the ability to perform fundamental tasks such as detection, classification, and tracking. The success of these tasks depends on the quality of the data provided by the LiDAR, i.e., the point cloud, PC, and the algorithms used to analyse that PC. Some previous studies exploited large and sophisticated facilities filled with fog to analyse LiDARs in fog. However, such facilities are intrinsically highly complex and costly. To overcom
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Xu, Hong Gen, Ting Li, and Fang Wu. "Knowledge-Based Classification Method for Urban Area Objects Feature Extraction Based on LIDAR Points." Applied Mechanics and Materials 128-129 (October 2011): 1157–62. http://dx.doi.org/10.4028/www.scientific.net/amm.128-129.1157.

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Laser scanning technology can quickly capture a large area of high-precise 3D spatial data, and get the information of buildings, roads, vegetation and other urban objects from raw data. Based on this information general frame of these objects can be modelling. In this paper, an object-based classification method is proposed for urban objects based on LIDAR points: determine the contents of the objects contained in the scene; extract inherent features of different objects; establish objects feature knowledge database; combine and compare objects’ features and distribution of LIDAR points; deri
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El-Ashmawy, N., and A. Shaker. "Raster Vs. Point Cloud LiDAR Data Classification." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-7 (September 19, 2014): 79–83. http://dx.doi.org/10.5194/isprsarchives-xl-7-79-2014.

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Airborne Laser Scanning systems with light detection and ranging (LiDAR) technology is one of the fast and accurate 3D point data acquisition techniques. Generating accurate digital terrain and/or surface models (DTM/DSM) is the main application of collecting LiDAR range data. Recently, LiDAR range and intensity data have been used for land cover classification applications. Data range and Intensity, (strength of the backscattered signals measured by the LiDAR systems), are affected by the flying height, the ground elevation, scanning angle and the physical characteristics of the objects surfa
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Muckenhuber, Stefan, Hannes Holzer, and Zrinka Bockaj. "Automotive Lidar Modelling Approach Based on Material Properties and Lidar Capabilities." Sensors 20, no. 11 (2020): 3309. http://dx.doi.org/10.3390/s20113309.

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Development and validation of reliable environment perception systems for automated driving functions requires the extension of conventional physical test drives with simulations in virtual test environments. In such a virtual test environment, a perception sensor is replaced by a sensor model. A major challenge for state-of-the-art sensor models is to represent the large variety of material properties of the surrounding objects in a realistic manner. Since lidar sensors are considered to play an essential role for upcoming automated vehicles, this paper presents a new lidar modelling approach
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El-Ashmawy, N., and A. Shaker. "COMBINED MULTIPLE CLASSIFIED DATASETS CLASSIFICATION APPROACH FOR POINT CLOUD LIDAR DATA." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences IV-2/W5 (May 29, 2019): 349–56. http://dx.doi.org/10.5194/isprs-annals-iv-2-w5-349-2019.

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<p><strong>Abstract.</strong> Airborne Laser scanners using the Light Detection And Ranging (LiDAR) technology is a powerful tool for 3D data acquisition that records the backscattered energy as well. LiDAR has been successfully used in various applications including 3D modelling, feature extraction, and land cover information extraction. Airborne LiDAR data are usually acquired from different flight trajectories producing data in different strips with significant overlapped areas. Combining these data is required to get benefit of the multiple strips’ data that acquired from
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Homainejad, N., S. Zlatanova, and N. Pfeifer. "A VOXEL-BASED METHOD FOR THE THREE-DIMENSIONAL MODELLING OF HEATHLAND FROM LIDAR POINT CLOUDS: FIRST RESULTS." ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences V-3-2022 (May 17, 2022): 697–704. http://dx.doi.org/10.5194/isprs-annals-v-3-2022-697-2022.

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Abstract. Bushfires are an intrinsic part of the New South Wales’ (NSW) environment in Australia, especially in the Blue Mountains region (11400km2), that is dominated by fire prone vegetation that includes heathland. Many of the Australian native plants in this region are fire-prone and combustible, and many species even require fire to regenerate. The classification of the lateral and vertical distribution of living vegetation is necessary to manage the complexity of bushfires. Currently, interpretation of aerial and satellite images is the prevalent method for the classification of vegetati
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Bellakaout, A., M. Cherkaoui, M. Ettarid, and A. Touzani. "Automatic 3D Extraction of Buildings, Vegetation and Roads from LIDAR Data." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B3 (June 9, 2016): 173–80. http://dx.doi.org/10.5194/isprs-archives-xli-b3-173-2016.

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Aerial topographic surveys using Light Detection and Ranging (LiDAR) technology collect dense and accurate information from the surface or terrain; it is becoming one of the important tools in the geosciences for studying objects and earth surface. Classification of Lidar data for extracting ground, vegetation, and buildings is a very important step needed in numerous applications such as 3D city modelling, extraction of different derived data for geographical information systems (GIS), mapping, navigation, etc... Regardless of what the scan data will be used for, an automatic process is great
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Rozprawy doktorskie na temat "LiDAR; Classification; Modelling"

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Höfler, Veit, Christine Wessollek, and Pierre Karrasch. "Knowledge-based modelling of historical surfaces using lidar data." SPIE, 2016. https://tud.qucosa.de/id/qucosa%3A35116.

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Currently in archaeological studies digital elevation models are mainly used especially in terms of shaded reliefs for the prospection of archaeological sites. Hesse (2010) provides a supporting software tool for the determination of local relief models during the prospection using LiDAR scans. Furthermore the search for relicts from WW2 is also in the focus of his research.¹ In James et al. (2006) the determined contour lines were used to reconstruct locations of archaeological artefacts such as buildings.² This study is much more and presents an innovative workflow of determining historical
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Höfler, Veit, Christine Wessollek, and Pierre Karrasch. "Modelling prehistoric terrain Models using LiDAR-data: A geomorphological approach." SPIE, 2015. https://tud.qucosa.de/id/qucosa%3A35056.

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Terrain surfaces conserve human activities in terms of textures and structures. With reference to archaeological questions, the geological archive is investigated by means of models regarding anthropogenic traces. In doing so, the high-resolution digital terrain model is of inestimable value for the decoding of the archive. The evaluation of these terrain models and the reconstruction of historical surfaces is still a challenging issue. Due to the data collection by means of LiDAR systems (light detection and ranging) and despite their subsequent pre-processing and filtering, recently anthropo
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SOUTHEE, FLORENCE MARGARET. "Ecological land classification and soil moisture modelling in the boreal forest using LiDAR remote sensing." Thesis, 2010. http://hdl.handle.net/1974/6244.

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Ecological land classification (ELC) is used to classify forest types in Ontario based on ecological gradients of soil moisture and nutrient fertility determined in the field. If ELC could be automated using terrain surfaces generated from airborne Light Detection and Ranging (LiDAR) remote sensing, it would enhance our ability to carry out forest ecosite classification and inventory over large areas. The focus of this thesis was to determine if LiDAR-derived terrain surfaces could be used to accurately quantify soil moisture in the boreal forest at a study site near Timmins, Ontario for use
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Książki na temat "LiDAR; Classification; Modelling"

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Efficiency of Terrestrial Laser Scanning in Survey Works: Assessment, Modelling, and Monitoring. https://juniperpublishers.com/ijesnr/pdf/IJESNR.MS.ID.556334.pdf, 2023.

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Części książek na temat "LiDAR; Classification; Modelling"

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Collin, Antoine, Yves Pastol, Mathilde Letard, et al. "Increasing the Nature-Based Coastal Protection Using Bathymetric Lidar, Terrain Classification, Network Modelling: Reefs of Saint-Malo’s Lagoon?" In European Spatial Data for Coastal and Marine Remote Sensing. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-16213-8_17.

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Barmpoutis, Panagiotis, Tania Stathaki, Jonathan Lloyd, and Magna Soelma Bessera de Moura. "Tropical Tree Species 3D Modelling and Classification Based on LiDAR Technology." In Recent Advances in 3D Imaging, Modeling, and Reconstruction. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-5225-5294-9.ch001.

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Over the last decade or so, laser scanning technology has become an increasingly popular and important tool for forestry inventory, enabling accurate capture of 3D information in a fast and environmentally friendly manner. To this end, the authors propose here a system for tropical tree species classification based on 3D scans of LiDAR sensing technology. In order to exploit the interrelated patterns of trees, skeleton representations of tree point clouds are extracted, and their structures are divided into overlapping equal-sized 3D segments. Subsequently, they represent them as third-order sparse structure tensors setting the value of skeleton coordinates equal to one. Based on the higher-order tensor decomposition of each sparse segment, they 1) estimate the mode-n singular values extracting intra-correlations of tree branches and 2) model tropical trees as linear dynamical systems extracting appearance information and dynamics. The proposed methodology was evaluated in tropical tree species and specifically in a dataset consisting of 26 point clouds of common Caatinga dry-forest trees.
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Barmpoutis, Panagiotis, Tania Stathaki, Jonathan Lloyd, and Magna Soelma Bessera de Moura. "Tropical Tree Species 3D Modelling and Classification Based on LiDAR Technology." In Research Anthology on Ecosystem Conservation and Preserving Biodiversity. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-6684-5678-1.ch017.

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Over the last decade or so, laser scanning technology has become an increasingly popular and important tool for forestry inventory, enabling accurate capture of 3D information in a fast and environmentally friendly manner. To this end, the authors propose here a system for tropical tree species classification based on 3D scans of LiDAR sensing technology. In order to exploit the interrelated patterns of trees, skeleton representations of tree point clouds are extracted, and their structures are divided into overlapping equal-sized 3D segments. Subsequently, they represent them as third-order sparse structure tensors setting the value of skeleton coordinates equal to one. Based on the higher-order tensor decomposition of each sparse segment, they 1) estimate the mode-n singular values extracting intra-correlations of tree branches and 2) model tropical trees as linear dynamical systems extracting appearance information and dynamics. The proposed methodology was evaluated in tropical tree species and specifically in a dataset consisting of 26 point clouds of common Caatinga dry-forest trees.
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Streszczenia konferencji na temat "LiDAR; Classification; Modelling"

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"Statistical analysis of airborne LiDAR data for forest classification in the Strzelecki Ranges, Victoria, Australia." In 19th International Congress on Modelling and Simulation. Modelling and Simulation Society of Australia and New Zealand (MSSANZ), Inc., 2011. http://dx.doi.org/10.36334/modsim.2011.e3.zhang.

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Li, Dawei, and Ye Chen. "A Novel LIDAR Classification Method Based on Ensemble Random Forest and D-S Evidence Synthesis." In 2018 10th International Conference on Modelling, Identification and Control (ICMIC). IEEE, 2018. http://dx.doi.org/10.1109/icmic.2018.8529874.

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