Relevant bibliographies by topics / Automated lidar

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers
  5. Reports

Academic literature on the topic 'Automated lidar'

Author: Grafiati
Published: 2 June 2025
Last updated: 7 July 2025

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Journal articles on the topic "Automated lidar"

1

Thorsen, Tyler J., and Qiang Fu. "Automated Retrieval of Cloud and Aerosol Properties from the ARM Raman Lidar. Part II: Extinction." Journal of Atmospheric and Oceanic Technology 32, no. 11 (2015): 1999–2023. http://dx.doi.org/10.1175/jtech-d-14-00178.1.

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AbstractA feature detection and extinction retrieval (FEX) algorithm for the Atmospheric Radiation Measurement Program’s (ARM) Raman lidar (RL) has been developed. Presented here is Part II of the FEX algorithm: the retrieval of cloud and aerosol extinction profiles. The directly retrieved extinction profiles using the Raman method are supplemented by other retrieval methods developed for elastic backscatter lidars. Portions of features where the extinction-to-backscatter ratios (i.e., lidar ratios) can be obtained are used to infer the lidar ratios for the regions where no such estimate can be made. When neither directly retrieved nor an inferred value can be determined, a climatological lidar ratio is used. This best-estimate approach results in the need to use climatological lidar ratios for less than about 5% of features, except for thin cirrus at the ARM tropical western Pacific Darwin site, where above 12 km, about 20% of clouds use a climatological lidar ratio. A classification of feature type is made, guided by the atmosphere’s thermodynamic state and the feature’s scattering properties: lidar ratio, backscatter, and depolarization. The contribution of multiple scattering is explicitly considered for each of the ARM RL channels. A comparison between aerosol optical depth from FEX and that from collocated sun photometers over multiple years at two ARM sites shows an agreement (in terms of bias error) of about −0.3% to −4.3% (relative to the sun photometer).
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Xue, Yuquan, Liming Wang, and Longmei Li. "Research on Automatic Recharging Technology for Automated Guided Vehicles Based on Multi-Sensor Fusion." Applied Sciences 14, no. 19 (2024): 8606. http://dx.doi.org/10.3390/app14198606.

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Automated guided vehicles (AGVs) play a critical role in indoor environments, where battery endurance and reliable recharging are essential. This study proposes a multi-sensor fusion approach that integrates LiDAR, depth cameras, and infrared sensors to address challenges in autonomous navigation and automatic recharging. The proposed system overcomes the limitations of LiDAR’s blind spots in near-field detection and the restricted range of vision-based navigation. By combining LiDAR for precise long-distance measurements, depth cameras for enhanced close-range visual positioning, and infrared sensors for accurate docking, the AGV’s ability to locate and autonomously connect to charging stations is significantly improved. Experimental results show a 25% increase in docking success rate (from 70% with LiDAR-only to 95%) and a 70% decrease in docking error (from 10 cm to 3 cm). These improvements demonstrate the effectiveness of the proposed sensor fusion method, ensuring more reliable, efficient, and precise operations for AGVs in complex indoor environments.
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Jamali, A., P. Kumar, and A. Abdul Rahman. "AUTOMATED EXTRACTION OF BUILDINGS FROM AERIAL LIDAR POINT CLOUDS AND DIGITAL IMAGING DATASETS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W16 (October 1, 2019): 303–8. http://dx.doi.org/10.5194/isprs-archives-xlii-4-w16-303-2019.

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Abstract. To acquire 3D geospatial information, LiDAR technology provides the rapid, continuous and cost-effective capability. In this paper, two automated approaches for extracting building features from the integrated aerial LiDAR point cloud and digital imaging datasets are proposed. The assumption of the two approaches is that the LiDAR data can be used to distinguish between high- and low-rise objects while the multispectral dataset can be used to filter out vegetation from the data. Object-based image analysis techniques are applied to the extracted building objects. The two automated buildings extraction approaches are tested on a fusion of aerial LiDAR point cloud and digital imaging datasets of Istanbul city. The object-based automated technique presents better results compared to the threshold-based technique for extraction of building objects in term of visual interpretation.
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Widyaningrum, E., and B. G. H. Gorte. "CHALLENGES AND OPPORTUNITIES: ONE STOP PROCESSING OF AUTOMATIC LARGE-SCALE BASE MAP PRODUCTION USING AIRBORNE LIDAR DATA WITHIN GIS ENVIRONMENT. CASE STUDY: MAKASSAR CITY, INDONESIA." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-1/W1 (May 31, 2017): 365–69. http://dx.doi.org/10.5194/isprs-archives-xlii-1-w1-365-2017.

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LiDAR data acquisition is recognized as one of the fastest solutions to provide basis data for large-scale topographical base maps worldwide. Automatic LiDAR processing is believed one possible scheme to accelerate the large-scale topographic base map provision by the Geospatial Information Agency in Indonesia. As a progressive advanced technology, Geographic Information System (GIS) open possibilities to deal with geospatial data automatic processing and analyses. Considering further needs of spatial data sharing and integration, the one stop processing of LiDAR data in a GIS environment is considered a powerful and efficient approach for the base map provision. The quality of the automated topographic base map is assessed and analysed based on its completeness, correctness, quality, and the confusion matrix.
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Lee, Hyunsuk, and Woojin Chung. "Extrinsic Calibration of Multiple 3D LiDAR Sensors by the Use of Planar Objects." Sensors 22, no. 19 (2022): 7234. http://dx.doi.org/10.3390/s22197234.

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Three-dimensional light detection and ranging (LiDAR) sensors have received much attention in the field of autonomous navigation owing to their accurate, robust, and rich geometric information. Autonomous vehicles are typically equipped with multiple 3D LiDARs because there are many commercially available low-cost 3D LiDARs. Extrinsic calibration of multiple LiDAR sensors is essential in order to obtain consistent geometric information. This paper presents a systematic procedure for the extrinsic calibration of multiple 3D LiDAR sensors using plane objects. At least three independent planes are required within the common field of view of the LiDAR sensors. The planes satisfying the condition can easily be found on objects such as the ground, walls, or columns in indoor and outdoor environments. Therefore, the proposed method does not require environmental modifications such as using artificial calibration objects. Multiple LiDARs typically have different viewpoints to reduce blind spots. This situation increases the difficulty of the extrinsic calibration using conventional registration algorithms. We suggest a plane registration method for cases in which correspondences are not known. The entire calibration process can easily be automated using the proposed registration technique. The presented experimental results clearly show that the proposed method generates more accurate extrinsic parameters than conventional point cloud registration methods.
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Yamamoto, T., and M. Nakagawa. "MERGING AIRBORNE LIDAR DATA AND SATELLITE SAR DATA FOR BUILDING CLASSIFICATION." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-4/W5 (May 13, 2015): 227–32. http://dx.doi.org/10.5194/isprsarchives-xl-4-w5-227-2015.

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A frequent map revision is required in GIS applications, such as disaster prevention and urban planning. In general, airborne photogrammetry and LIDAR measurements are applied to geometrical data acquisition for automated map generation and revision. However, attribute data acquisition and classification depend on manual editing works including ground surveys. In general, airborne photogrammetry and LiDAR measurements are applied to geometrical data acquisition for automated map generation and revision. However, these approaches classify geometrical attributes. Moreover, ground survey and manual editing works are finally required in attribute data classification. On the other hand, although geometrical data extraction is difficult, SAR data have a possibility to automate the attribute data acquisition and classification. The SAR data represent microwave reflections on various surfaces of ground and buildings. There are many researches related to monitoring activities of disaster, vegetation, and urban. Moreover, we have an opportunity to acquire higher resolution data in urban areas with new sensors, such as ALOS2 PALSAR2. Therefore, in this study, we focus on an integration of airborne LIDAR data and satellite SAR data for building extraction and classification.
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Gupta, Himanshu, Henrik Andreasson, Achim J. Lilienthal, and Polina Kurtser. "Robust Scan Registration for Navigation in Forest Environment Using Low-Resolution LiDAR Sensors." Sensors 23, no. 10 (2023): 4736. http://dx.doi.org/10.3390/s23104736.

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Automated forest machines are becoming important due to human operators’ complex and dangerous working conditions, leading to a labor shortage. This study proposes a new method for robust SLAM and tree mapping using low-resolution LiDAR sensors in forestry conditions. Our method relies on tree detection to perform scan registration and pose correction using only low-resolution LiDAR sensors (16Ch, 32Ch) or narrow field of view Solid State LiDARs without additional sensory modalities like GPS or IMU. We evaluate our approach on three datasets, including two private and one public dataset, and demonstrate improved navigation accuracy, scan registration, tree localization, and tree diameter estimation compared to current approaches in forestry machine automation. Our results show that the proposed method yields robust scan registration using detected trees, outperforming generalized feature-based registration algorithms like Fast Point Feature Histogram, with an above 3 m reduction in RMSE for the 16Chanel LiDAR sensor. For Solid-State LiDAR the algorithm achieves a similar RMSE of 3.7 m. Additionally, our adaptive pre-processing and heuristic approach to tree detection increased the number of detected trees by 13% compared to the current approach of using fixed radius search parameters for pre-processing. Our automated tree trunk diameter estimation method yields a mean absolute error of 4.3 cm (RSME = 6.5 cm) for the local map and complete trajectory maps.
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Creek, Tristan, and Barry Mullins. "Analysis of Image Thresholding Algorithms for Automated Machine Learning Training Data Generation." International Conference on Cyber Warfare and Security 17, no. 1 (2022): 449–58. http://dx.doi.org/10.34190/iccws.17.1.57.

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Secured compounds often safeguard physical layout details of both internal and external facilities, but these details are at risk due to the growing inclusion of Light Detection and Ranging (LiDAR) sensors in consumer off-the-shelf (COTS) technology such as cell phones. The ability to record detailed distance data with cell phones facilitates the production of high-quality three-dimensional scans in a discrete manner which directly threatens the security of private compounds. Therefore, it behooves the organizations in charge of private compounds to detect LiDAR activity. Many security cameras already detect LiDAR sources as generic light sources in specific conditions, but further analysis must identify these light sources as LiDAR sources in order to alert an organization of a potential security incident. Testing confirms the feasibility of identifying some LiDAR sources based on the color and intensity of light shined directly into a camera sensor, but this analysis proves inadequate for cell phone LiDAR. However, the unique intensity and pattern characteristics of cell phone LiDAR reflected off a surface can potentially be identified by an object identification machine learning model. In order to train a model to identify a LiDAR object, we must first produce a training dataset containing marked and labelled LiDAR objects. To do this, we apply an image thresholding algorithm to isolate the LiDAR object in an image to calculate its bounding box. The image thresholding algorithm directly affects the bounding box accuracy, so we test two different algorithms and find that Otsu’s image thresholding algorithm performs best, resulting in 99.5% accurate bounding boxes.
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Thorsen, Tyler J., Qiang Fu, Rob K. Newsom, David D. Turner, and Jennifer M. Comstock. "Automated Retrieval of Cloud and Aerosol Properties from the ARM Raman Lidar. Part I: Feature Detection." Journal of Atmospheric and Oceanic Technology 32, no. 11 (2015): 1977–98. http://dx.doi.org/10.1175/jtech-d-14-00150.1.

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AbstractA feature detection and extinction retrieval (FEX) algorithm for the Atmospheric Radiation Measurement Program’s (ARM) Raman lidar (RL) has been developed. Presented here is Part I of the FEX algorithm: the detection of features including both clouds and aerosols. The approach of FEX is to use multiple quantities— scattering ratios derived using elastic and nitrogen channel signals from two fields of view, the scattering ratio derived using only the elastic channel, and the total volume depolarization ratio—to identify features using range-dependent detection thresholds. FEX is designed to be context sensitive with thresholds determined for each profile by calculating the expected clear-sky signal and noise. The use of multiple quantities provides complementary depictions of cloud and aerosol locations and allows for consistency checks to improve the accuracy of the feature mask. The depolarization ratio is shown to be particularly effective at detecting optically thin features containing nonspherical particles, such as cirrus clouds. Improvements over the existing ARM RL cloud mask are shown. The performance of FEX is validated against a collocated micropulse lidar and observations from the Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) satellite over the ARM Darwin, Australia, site. While the focus is on a specific lidar system, the FEX framework presented here is suitable for other Raman or high spectral resolution lidars.
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Vaughan, Mark A., Kathleen A. Powell, David M. Winker, et al. "Fully Automated Detection of Cloud and Aerosol Layers in the CALIPSO Lidar Measurements." Journal of Atmospheric and Oceanic Technology 26, no. 10 (2009): 2034–50. http://dx.doi.org/10.1175/2009jtecha1228.1.

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Abstract Accurate knowledge of the vertical and horizontal extent of clouds and aerosols in the earth’s atmosphere is critical in assessing the planet’s radiation budget and for advancing human understanding of climate change issues. To retrieve this fundamental information from the elastic backscatter lidar data acquired during the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) mission, a selective, iterated boundary location (SIBYL) algorithm has been developed and deployed. SIBYL accomplishes its goals by integrating an adaptive context-sensitive profile scanner into an iterated multiresolution spatial averaging scheme. This paper provides an in-depth overview of the architecture and performance of the SIBYL algorithm. It begins with a brief review of the theory of target detection in noise-contaminated signals, and an enumeration of the practical constraints levied on the retrieval scheme by the design of the lidar hardware, the geometry of a space-based remote sensing platform, and the spatial variability of the measurement targets. Detailed descriptions are then provided for both the adaptive threshold algorithm used to detect features of interest within individual lidar profiles and the fully automated multiresolution averaging engine within which this profile scanner functions. The resulting fusion of profile scanner and averaging engine is specifically designed to optimize the trade-offs between the widely varying signal-to-noise ratio of the measurements and the disparate spatial resolutions of the detection targets. Throughout the paper, specific algorithm performance details are illustrated using examples drawn from the existing CALIPSO dataset. Overall performance is established by comparisons to existing layer height distributions obtained by other airborne and space-based lidars.
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Dissertations / Theses on the topic "Automated lidar"

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Hamraz, Hamid. "AUTOMATED TREE-LEVEL FOREST QUANTIFICATION USING AIRBORNE LIDAR." UKnowledge, 2018. https://uknowledge.uky.edu/cs_etds/69.

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Traditional forest management relies on a small field sample and interpretation of aerial photography that not only are costly to execute but also yield inaccurate estimates of the entire forest in question. Airborne light detection and ranging (LiDAR) is a remote sensing technology that records point clouds representing the 3D structure of a forest canopy and the terrain underneath. We present a method for segmenting individual trees from the LiDAR point clouds without making prior assumptions about tree crown shapes and sizes. We then present a method that vertically stratifies the point cloud to an overstory and multiple understory tree canopy layers. Using the stratification method, we modeled the occlusion of higher canopy layers with respect to point density. We also present a distributed computing approach that enables processing the massive data of an arbitrarily large forest. Lastly, we investigated using deep learning for coniferous/deciduous classification of point cloud segments representing individual tree crowns. We applied the developed methods to the University of Kentucky Robinson Forest, a natural, majorly deciduous, closed-canopy forest. 90% of overstory and 47% of understory trees were detected with false positive rates of 14% and 2% respectively. Vertical stratification improved the detection rate of understory trees to 67% at the cost of increasing their false positive rate to 12%. According to our occlusion model, a point density of about 170 pt/m² is needed to segment understory trees located in the third layer as accurately as overstory trees. Using our distributed processing method, we segmented about two million trees within a 7400-ha forest in 2.5 hours using 192 processing cores, showing a speedup of ~170. Our deep learning experiments showed high classification accuracies (~82% coniferous and ~90% deciduous) without the need to manually assemble the features. In conclusion, the methods developed are steps forward to remote, accurate quantification of large natural forests at the individual tree level.
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Gadre, Mandar M. "Automated building footprint extraction from high resolution LIDAR DEM imagery." Diss., Columbia, Mo. : University of Missouri-Columbia, 2005. http://hdl.handle.net/10355/4320.

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Thesis (M.S.)--University of Missouri-Columbia, 2005.<br>The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file. Title from title screen of research.pdf file viewed on (July 13, 2006) Includes bibliographical references.
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Lach, Stephen R. "Semi-automated DIRSIG scene modeling from 3D lidar and passive imagery /." Online version of thesis, 2008. http://hdl.handle.net/1850/7861.

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Liu, Haijian. "Automated Treetop Detection and Tree Crown Identification Using Discrete-return Lidar Data." Thesis, University of North Texas, 2013. https://digital.library.unt.edu/ark:/67531/metadc271858/.

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Accurate estimates of tree and forest biomass are essential for a wide range of applications. Automated treetop detection and tree crown discrimination using LiDAR data can greatly facilitate forest biomass estimation. Previous work has focused on homogenous or single-species forests, while few studies have focused on mixed forests. In this study, a new method for treetop detection is proposed in which the treetop is the cluster center of selected points rather than the highest point. Based on treetop detection, tree crowns are discriminated through comparison of three-dimensional shape signatures. The methods are first tested using simulated LiDAR point clouds for trees, and then applied to real LiDAR data from the Soquel Demonstration State Forest, California, USA. Results from both simulated and real LiDAR data show that the proposed method has great potential for effective detection of treetops and discrimination of tree crowns.
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Sadeghinaeenifard, Fariba. "Automated Tree Crown Discrimination Using Three-Dimensional Shape Signatures Derived from LiDAR Point Clouds." Thesis, University of North Texas, 2018. https://digital.library.unt.edu/ark:/67531/metadc1157521/.

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Discrimination of different tree crowns based on their 3D shapes is essential for a wide range of forestry applications, and, due to its complexity, is a significant challenge. This study presents a modified 3D shape descriptor for the perception of different tree crown shapes in discrete-return LiDAR point clouds. The proposed methodology comprises of five main components, including definition of a local coordinate system, learning salient points, generation of simulated LiDAR point clouds with geometrical shapes, shape signature generation (from simulated LiDAR points as reference shape signature and actual LiDAR point clouds as evaluated shape signature), and finally, similarity assessment of shape signatures in order to extract the shape of a real tree. The first component represents a proposed strategy to define a local coordinate system relating to each tree to normalize 3D point clouds. In the second component, a learning approach is used to categorize all 3D point clouds into two ranks to identify interesting or salient points on each tree. The third component discusses generation of simulated LiDAR point clouds for two geometrical shapes, including a hemisphere and a half-ellipsoid. Then, the operator extracts 3D LiDAR point clouds of actual trees, either deciduous or evergreen. In the fourth component, a longitude-latitude transformation is applied to simulated and actual LiDAR point clouds to generate 3D shape signatures of tree crowns. A critical step is transformation of LiDAR points from their exact positions to their longitude and latitude positions using the longitude-latitude transformation, which is different from the geographic longitude and latitude coordinates, and labeled by their pre-assigned ranks. Then, natural neighbor interpolation converts the point maps to raster datasets. The generated shape signatures from simulated and actual LiDAR points are called reference and evaluated shape signatures, respectively. Lastly, the fifth component determines the similarity between evaluated and reference shape signatures to extract the shape of each examined tree. The entire process is automated by ArcGIS toolboxes through Python programming for further evaluation using more tree crowns in different study areas. Results from LiDAR points captured for 43 trees in the City of Surrey, British Columbia (Canada) suggest that the modified shape descriptor is a promising method for separating different shapes of tree crowns using LiDAR point cloud data. Experimental results also indicate that the modified longitude-latitude shape descriptor fulfills all desired properties of a suitable shape descriptor proposed in computer science along with leaf-off, leaf-on invariance, which makes this process autonomous from the acquisition date of LiDAR data. In summary, the modified longitude-latitude shape descriptor is a promising method for discriminating different shapes of tree crowns using LiDAR point cloud data.
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Hilker, Thomas. "Estimation of photosynthetic light-use efficience from automated multi-angular spectroradiometer measurements of coastal Douglas-fir." Thesis, University of British Columbia, 2008. http://hdl.handle.net/2429/2685.

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Global modeling of gross primary production (GPP) is a critical component of climate change research. On local scales, GPP can be assessed from measuring CO₂ exchange above the plant canopy using tower-based eddy covariance (EC) systems. The limited footprint inherent to this method however, restricts observations to relatively few discrete areas making continuous predictions of global CO₂ fluxes difficult. Recently, the advent of high resolution optical remote sensing devices has offered new possibilities to address some of the scaling issues related to GPP using remote sensing. One key component for inferring GPP spectrally is the efficiency (ε) with which plants can use absorbed photosynthetically active radiation to produce biomass. While recent years have seen progress in measuring ε using the photochemical reflectance index (PRI), little is known about the temporal and spatial requirements for up-scaling these findings continuously throughout the landscape. Satellite observations of canopy reflectance are subject to view and illumination effects induced by the bi-directional reflectance distribution function(BRDF) which can confound the desired PRI signal. Further uncertainties include dependencies of PRI on canopy structure, understorey, species composition and leaf pigment concentration. The objective of this research was to investigate the effects of these factors on PRI to facilitate the modeling of GPP in a continuous fashion. Canopy spectra were sampled over a one-year period using an automated tower-based, multi-angular spectroradiometer platform (AMSPEC), designed to sample high spectral resolution data. The wide range of illumination and viewing geometries seen by the instrument permitted comprehensive modeling of the BRDF. Isolation of physiologically induced changes in PRI yielded a high correlation (r²=0.82, p<0.05) to EC-measured ε, thereby demonstrating the capability of PRI to model ε throughout the year. The results were extrapolated to the landscape scale using airborne laser-scanning (light detection and ranging, LiDAR) and high correlations were found between remotely-sensed and EC-measured GPP (r²>0.79, p<0.05). Permanently established tower-based canopy reflectance measurements are helpful for ongoing research aimed at up-scaling ε to landscape and global scales and facilitate a better understanding of physiological cycles of vegetation and serve as a calibration tool for broader band satellite observations.
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Hungar, Constanze [Verfasser], Frank [Akademischer Betreuer] Köster, and Stephan [Akademischer Betreuer] Schmidt. "Map-based Localization for Automated Vehicles using LiDAR Features / Constanze Hungar ; Frank Köster, Stephan Schmidt." Oldenburg : BIS der Universität Oldenburg, 2021. http://nbn-resolving.de/urn:nbn:de:gbv:715-oops-51937.

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Dinoev, Todor. "Automated Raman lidar for day and night operational observation of tropospheric water vapor for meterorological applications /." [S.l.] : [s.n.], 2009. http://library.epfl.ch/theses/?nr=4501.

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Deshpande, Sagar Shriram. "Semi-automated Methods to Create a Hydro-flattened DEM using Single Photon and Linear Mode LiDAR Points." The Ohio State University, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=osu1491300120665946.

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Manhed, Joar. "Investigating Simultaneous Localization and Mapping for an Automated Guided Vehicle." Thesis, Linköpings universitet, Reglerteknik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-163075.

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The aim of the thesis is to apply simultaneous localization and mapping (SLAM) to automated guided vehicles (AGVs) in a Robot Operating System (ROS) environment. Different sensor setups are used and evaluated. The SLAM applications used is the open-source solution Cartographer as well as Intel's own commercial SLAM in their T265 tracking camera. The different sensor setups are evaluated based on how well the localization will give the exact pose of the AGV in comparison to another positioning system acting as ground truth.
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Book chapters on the topic "Automated lidar"

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Rosenberger, Philipp, Martin Holder, Marc René Zofka, et al. "Functional Decomposition of Lidar Sensor Systems for Model Development." In Validation and Verification of Automated Systems. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-14628-3_12.

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Devagiri, Rohini, Nalini C. Iyer, and Shruti Maralappanavar. "Real-time RADAR and LIDAR Sensor Fusion for Automated Driving." In Machine Intelligence and Signal Processing. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-1366-4_11.

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Husain, Arshad, and Rakesh Chandra Vaishya. "An Automated Method for Power Line Points Detection from Terrestrial LiDAR Data." In Advances in Intelligent Systems and Computing. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1498-8_41.

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Zhang, Keqi, Jianhua Yan, and Shu-Ching Chen. "A Framework for Automated Construction of Building Models from Airborne LiDAR Measurements." In Topographic Laser Ranging and Scanning. CRC Press, 2018. http://dx.doi.org/10.1201/9781315154381-18.

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Zou, Yakun, Limei Chen, Ting Deng, and Yi Tan. "Automated Intelligent Detection of Truss Geometric Quality Based on BIM and LiDAR." In Lecture Notes in Operations Research. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-1949-5_21.

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Maryem, Bouali, Sammuneh Muhammad Ali, El Mehouche Rani, Ababsa Fakhreddine, Salavati Bahar, and Viguier Flavien. "An Automated Technique for Detecting Sinkholes from Lidar DEMs in Railway Environments." In Communications in Computer and Information Science. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-82150-9_25.

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Hamid-Lakzaeian, Fatemeh, and Debra F. Laefer. "An Integrated Octree-RANSAC Technique for Automated LiDAR Building Data Segmentation for Decorative Buildings." In Advances in Visual Computing. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-50832-0_44.

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Di Benedetto, Alessandro, and Margherita Fiani. "Integration of LiDAR Data into a Regional Topographic Database for the Generation of a 3D City Model." In Geomatics for Green and Digital Transition. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-17439-1_14.

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AbstractTo analyze the resilience of road infrastructures to natural and anthropic hazards, the spatial and descriptive data provided by the Italian National Topographic Data Base (NTDB) and the 3D data coming from the LiDAR data of the “Ministero dell'Ambiente e della Tutela del Territorio e del Mare” (MATTM) can be used. The two datasets, having different nature, need to be properly joined. The aim of the work is the integration of the two datasets in a GIS environment for the 3D modelling of the anthropized territory and the optimization of the cartographic bases. On a test area, crossed by a network of linear infrastructures of great strategic importance and subjected to hydrogeological risk, an automated process has been implemented and tested in ArcGIS Desktop environment, to homogenize the data into the National Reference System. The planimetric component comes from the NTDB whereas the LiDAR data have been used to attribute the elevation to the extracted elements, to create the breaklines for a proper interpolation of the heights to build the Digital Terrain Model (DTM), to extract the height of the pitches of the buildings identified in the NTDB polygons, and finally to generate, filter and optimize the contour lines. The proposed workflow and the methodologies implemented also allowed the reconstruction of the volumes of each element involved (infrastructures and buildings) and to correct the altimetric aberrations present in the NTDB polygons.
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Chen, Yuzhe, Yi Tan, and Shenghan Li. "Automated LiDAR Scan Planning of 3D Indoor Space Based on BIM and an Improved GA." In Proceedings of the 27th International Symposium on Advancement of Construction Management and Real Estate. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-3626-7_93.

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Beierlein, Georg, Jinhan Kong, and Steffen Kutter. "A Robust Localisation System for an Highly Automated People Mover Based on GNSS- and LiDAR-Data." In Proceedings. Springer Fachmedien Wiesbaden, 2021. http://dx.doi.org/10.1007/978-3-658-33466-6_29.

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Conference papers on the topic "Automated lidar"

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Barnwal, Surbhi, and Salil Goel. "Automated and Targetless Multi-LiDAR Calibration using RANSAC-ICP." In 2024 IEEE India Geoscience and Remote Sensing Symposium (InGARSS). IEEE, 2024. https://doi.org/10.1109/ingarss61818.2024.10983978.

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Wang, Xinyu, Muhammad Ibrahim, Atif Mansoor, Hasnein Tareque, and Ajmal Mian. "Automated Road Extraction and Centreline Fitting in LiDAR Point Clouds." In 2024 International Conference on Digital Image Computing: Techniques and Applications (DICTA). IEEE, 2024. https://doi.org/10.1109/dicta63115.2024.00092.

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Wulff, Florian, Bernd Schäufele, Julian Pfeifer, and Ilja Radusch. "Railway LiDAR semantic segmentation based on intelligent semi-automated data annotation." In 2024 IEEE 100th Vehicular Technology Conference (VTC2024-Fall). IEEE, 2024. https://doi.org/10.1109/vtc2024-fall63153.2024.10758029.

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Massicotte, Philippe, Louis-Alexandre Leclaire, Mohamed Gaha, Guillaume Houle, and Christian Buteau. "Automated Inventory of Electrical Distribution Assets Based on Image Recognition and Ground LiDAR." In 2024 32nd European Signal Processing Conference (EUSIPCO). IEEE, 2024. http://dx.doi.org/10.23919/eusipco63174.2024.10715006.

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Xu, Qingpo, Haitao Liu, Yugeng Huang, Yabin Ding, Juliang Xiao, and Yijin Wang. "An Automated Extrinsic Calibration Method of a multi-layer LiDAR and a Camera." In 2024 IEEE International Conference on Signal, Information and Data Processing (ICSIDP). IEEE, 2024. https://doi.org/10.1109/icsidp62679.2024.10868761.

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Ito, Toshio. "LiDAR for Automated Driving Era." In 2021 IEEE CPMT Symposium Japan (ICSJ). IEEE, 2021. http://dx.doi.org/10.1109/icsj52620.2021.9648873.

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Zhu, Dongdong, Qin Qin, Jingwei Wang, Jianfang Dou, Sujuan Wang, and Zimei Tu. "Research on automated disassembly technology for waste LCD." In LIDAR Imaging Detection and Target Recognition 2017, edited by Yueguang Lv, Jianzhong Su, Wei Gong, et al. SPIE, 2017. http://dx.doi.org/10.1117/12.2287638.

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Qin, Xiaofei, Xiao Zhang, Rongfu Zhang, and Feng Li. "Design of an adaptive regulator for an automated microscope stage." In LIDAR Imaging Detection and Target Recognition 2017, edited by Yueguang Lv, Jianzhong Su, Wei Gong, et al. SPIE, 2017. http://dx.doi.org/10.1117/12.2296365.

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Matthey, Renaud, Valentin Mitev, Gaetano Mileti, et al. "Miniature aerosol lidar for automated airborne application." In AeroSense 2000, edited by Gary W. Kamerman, Upendra N. Singh, Christian Werner, and Vasyl V. Molebny. SPIE, 2000. http://dx.doi.org/10.1117/12.397821.

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Visser, Edgar E., Arnoud Apituley, J. B. Bergwerff, et al. "RIVM's automated lidar systems for climate research." In European Symposium on Optics for Environmental and Public Safety, edited by Richard J. Becherer. SPIE, 1995. http://dx.doi.org/10.1117/12.219640.

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Reports on the topic "Automated lidar"

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VISEUR, Sophie. Automated operator- and statistic-based geological interpretations of numerical LIDAR outcrops. Cogeo@oeaw-giscience, 2011. http://dx.doi.org/10.5242/iamg.2011.0162.

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Berney, Ernest, Andrew Ward, and Naveen Ganesh. First generation automated assessment of airfield damage using LiDAR point clouds. Engineer Research and Development Center (U.S.), 2021. http://dx.doi.org/10.21079/11681/40042.

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This research developed an automated software technique for identifying type, size, and location of man-made airfield damage including craters, spalls, and camouflets from a digitized three-dimensional point cloud of the airfield surface. Point clouds were initially generated from Light Detection and Ranging (LiDAR) sensors mounted on elevated lifts to simulate aerial data collection and, later, an actual unmanned aerial system. LiDAR data provided a high-resolution, globally positioned, and dimensionally scaled point cloud exported in a LAS file format that was automatically retrieved and processed using volumetric detection algorithms developed in the MATLAB software environment. Developed MATLAB algorithms used a three-stage filling technique to identify the boundaries of craters first, then spalls, then camouflets, and scaled their sizes based on the greatest pointwise extents. All pavement damages and their locations were saved as shapefiles and uploaded into the GeoExPT processing environment for visualization and quality control. This technique requires no user input between data collection and GeoExPT visualization, allowing for a completely automated software analysis with all filters and data processing hidden from the user.
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Porcel Magnusson, Cristina. Unsettled Topics Concerning Coating Detection by LiDAR in Autonomous Vehicles. SAE International, 2021. http://dx.doi.org/10.4271/epr2021002.

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Autonomous vehicles (AVs) utilize multiple devices, like high-resolution cameras and radar sensors, to interpret the driving environment and achieve full autonomy. One of these instruments—the light detection and ranging (LiDAR) sensor—utilizes pulsed infrared (IR) light, typically at wavelengths of 905 nm or 1,550 nm, to calculate object distance and position. Exterior automotive paint covers an area larger than any other exterior material. Therefore, understanding how LiDAR wavelengths interact with vehicle coatings is extremely important for the safety of future automated driving technologies. Sensing technologies and materials are two different industries that have not directly interacted in the perception and system sense. With the new applications in the AV industry, multidisciplinary approaches need to be taken to ensure reliability and safety in the future. Unsettled Topics Concerning Coating Detection by LiDAR in Autonomous Vehicles provides a transversal view of different industry segments, from pigment and coating manufacturers to LiDAR components and vehicle system development and integration. The report includes a structured decomposition of the different variables and technologies involved.
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Papasodoro, C., D. Bélanger, G. Légaré-Couture, P. Tardif, and M. Turgeon-Pelchat. Mise à jour de la Stratégie nationale sur les données d'élévation, automne 2021. Natural Resources Canada/CMSS/Information Management, 2021. http://dx.doi.org/10.4095/329336.

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The National Elevation Data Strategy is being implemented. This newsletter of October 2021 provides an update on the following: 1. Status of the 2021 LiDAR acquisitions 2. New elevation products and data 3. Improvement of the automatic extraction of building footprints 4. Validation of LiDAR classification by artificial intelligence.
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Papasodoro, C., D. Bélanger, G. Légaré-Couture, P. Tardif, and M. Turgeon-Pelchat. National Elevation Data Strategy update, fall 2021. Natural Resources Canada/CMSS/Information Management, 2021. http://dx.doi.org/10.4095/329335.

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The National Elevation Data Strategy is being implemented. This newsletter of October 2021 provides an update on the following: 1. Status of the 2021 LiDAR acquisitions 2. New elevation products and data 3. Improvement of the automatic extraction of building footprints 4. Validation of LiDAR classification by artificial intelligence.
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Jackson, Samuel, Christina Saltus, Molly Reif, and Glenn Suir. During Nearshore Event Vegetation Gradation (DUNEVEG) : geospatial tools for automating remote vegetation extraction. Engineer Research and Development Center (U.S.), 2023. http://dx.doi.org/10.21079/11681/47649.

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Monitoring and modeling of coastal vegetation and ecosystems are major challenges, especially when considering environmental response to hazards, disturbances, and management activities. Remote sensing applications can provide alternatives and complementary approaches to the often costly and laborious field-based collection methods traditionally used for coastal ecosystem monitoring. New and improved sensors and data analysis techniques have become available, making remote sensing applications attractive for evaluation and potential use in monitoring coastal vegetation properties and ecosystem conditions and changes. This study involves the extraction of vegetation metrics from airborne lidar and hyperspectral imagery (HSI) collected by the US Army Corps of Engineers (USACE) National Coastal Mapping Program (NCMP) to quantify coastal dune vegetation characteristics. A custom geoprocessing toolbox and associated suite of tools were developed to allow inputs of common NCMP lidar and imagery products to help automate the workflow for extracting prioritized dune vegetation metrics in an efficient and repeatable way. This study advances existing coastal ecosystem knowledge and remote sensing techniques by developing new methodologies to classify, quantify, and estimate critical coastal vegetation metrics which will ultimately improve future estimates and predictions of nearshore dynamics and impacts from disturbance events.
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Mathew, Jijo K., Haydn Malackowski, Yerassyl Koshan, et al. Development of Latitude/Longitude (and Route/Milepost) Model for Positioning Traffic Management Cameras. Purdue University, 2024. http://dx.doi.org/10.5703/1288284317720.

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Traffic Incident Management (TIM) is a FHWA Every Day Counts initiative with the objective of reducing secondary crashes, improving travel reliability, and ensuring the safety of responders. Agency roadside cameras play a critical role in TIM by helping dispatchers quickly identify the precise location of incidents when receiving reports from motorists with varying levels of spatial accuracy. Reconciling position reports that are often mile-marker based with cameras that operate in a Pan-Tilt-Zoom (PTZ) coordinate system relies on dispatchers having detailed knowledge of hundreds of cameras and perhaps some presets. During real-time incident dispatching, reducing the time it takes to identify the most relevant cameras and view the incident improves incident management dispatch times. This research developed a camera-to-mile marker mapping technique that automatically sets the camera view to a specified mile marker within the field-of-view of the camera. A new performance metric on verification time (TEYE) that captures the time it takes for TMC operators to have the first visual on roadside cameras is proposed for integration into the FHWA TIM event sequence. Performance metrics that summarize spatial camera coverage and image quality for use in both dispatch and long-term statewide planning for camera deployments were also developed. Using mobile mapping and LiDAR geospatial data to automate the mapping of mile markers to camera PTZ settings, and the integration of connected vehicle trajectory data to detect incidents and set the nearest camera view on the incident are both discussed for future studies.
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Muldavin, Esteban, Yvonne Chauvin, Teri Neville, et al. A vegetation classi?cation and map: Guadalupe Mountains National Park. National Park Service, 2024. http://dx.doi.org/10.36967/2302855.

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A vegetation classi?cation and map for Guadalupe Mountains National Park (NP) is presented as part of the National Park Service Inventory &amp; Monitoring - Vegetation Inventory Program to classify, describe, and map vegetation communities in more than 280 national park units across the United States. Guadalupe Mountains NP lies in far west Texas and contains the highest point in the state, Guadalupe Peak (8,751 ft; 2,667 m). The mountain escarpments descend some 5,000 ft (1,500 m) to the desert basins below forming a complex geologic landscape that supports vegetation communities ranging from montane coniferous forests down to desert grasslands and scrub. Following the US National Vegetation Classi?cation (USNVC) standard, we identi?ed 129 plant associations hierarchically tiered under 29 groups and 17 macrogroups, making it one of the most ecologically diverse National Park Service units in the southwestern United States. An aspect that adds to this diversity is that the park supports communities that extend southward from the Rocky Mountains (?ve macrogroups) and Great Plains (one macrogroup) and northward from the Chihuahuan Desert (two macrogroups) and Sierra Madre Orientale of Mexico (three macrogroups). The remaining six macrogroups are found in the Great Basin (one macrogroup), and throughout the southwestern United States (remaining ?ve macrogroups). Embedded in this matrix are gypsum dunelands and riparian zones and wetlands that add further complexity. We describe in detail this vegetation classi?cation, which is based on 540 vegetation plots collected between 2006 and 2010. Full descriptions and diagnostic keys to the plant associations along with an overall plant species list are provided as appendices. Based on the vegetation classi?cation and associated plot data, the vegetation map was developed using a combined strategy of automated digital object-oriented image classi?cation and direct-analog image interpretation of four-band National Agricultural Imagery Program (NAIP) aerial photography from 2004 and 2008 and Landsat Thematic Mapper satellite imagery. The map is designed to facilitate ecologically-based natural resource management at a 1:24,000 scale with 0.5-ha minimum map unit size. The map legend is hierarchically structured: the upper Level 1 consists of 16 map units corresponding in most cases to the USNVC group level, and an additional map unit describing built-up land and agriculture; Level 2 is composed of 48 nested map units re?ecting various combinations of plant associations. A ?eld-based accuracy assessment using 341 vegetation plots revealed a Level 1 overall accuracy of 79% with 90% CI of 74?84% and 68% with 90% CI of 59?76% at Level 2. An annotated legend with summary descriptions of the units, distribution maps, aerial photo examples of map unit polygons, and representative photos are provided in Appendix D. Large wall-size poster maps at 1:35,000 scale were also produced following NPS cartographic standards. The report, plot data, and spatial layers are available at National Park Service Vegetation Mapping Program https://www.nps.gov/im/vegetation-inventory.htm). Outcomes from this project provide the most detailed vegetation classi?cation and highest resolution mapping for Guadalupe Mountains NP to date to support many uses including ?re, recreation, vegetation, and wildlife management, among others. The upper Level 1 map is particularly suited to landscape-scale, park-wide planning and linkages to its sister park, Carlsbad Caverns NP. The Level 2 mapping provides added detail for use at a more localized project scale. The overall accuracy of the maps was good, but because Guadalupe Mountains NP is primarily wilderness park, there were logistical challenges to map development and testing in remote areas that should be considered in planning management actions. In this context, some map units would bene?t from further development and accuracy assessment. In particular, a higher resolution mapping of McKittrick Creek riparian habitat at 1:6,000 scale or ?ner is recommended for this important habitat in the park. In addition, developing a structural canopy height model from LiDAR imagery would be useful to more accurately quantify woody canopy density and height to support ?re management and other habitat management issues. With respect to understanding vegetation dynamics in this time of rapid environmental change, the 540 vegetation plots themselves are su?ciently georeferenced and have the data resolution to be useful in detecting change at the decadal scales across much of the park. To this end, an additional recommendation would be to install more plots to ?ll the gaps among the main vegetation units of the park, both spatially and thematically. Overall, the Vegetation and Classi?cation Map for Guadalupe Mountains NP will support the park?s management e?orts and enhance regional understanding of vegetation and ecology of ecosystems of the southwestern United States.
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