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Journal articles on the topic 'Synthetic LiDAR data generation'

Author: Grafiati

Published: 5 June 2025

Last updated: 2 August 2025

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1

Kim, Kana, Sangjun Lee, Vijay Kakani, Xingyou Li, and Hakil Kim. "Point Cloud Wall Projection for Realistic Road Data Augmentation." Sensors 24, no. 24 (2024): 8144. https://doi.org/10.3390/s24248144.

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Several approaches have been developed to generate synthetic object points using real LiDAR point cloud data for advanced driver-assistance system (ADAS) applications. The synthetic object points generated from a scene (both the near and distant objects) are essential for several ADAS tasks. However, generating points from distant objects using sparse LiDAR data with precision is still a challenging task. Although there are a few state-of-the-art techniques to generate points from synthetic objects using LiDAR point clouds, limitations such as the need for intense compute power still persist i

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Wang, Fei, Yan Zhuang, Hong Gu, and Huosheng Hu. "Automatic Generation of Synthetic LiDAR Point Clouds for 3-D Data Analysis." IEEE Transactions on Instrumentation and Measurement 68, no. 7 (2019): 2671–73. http://dx.doi.org/10.1109/tim.2019.2906416.

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Toro, Javier Villena, Lars Bolin, Jacob Eriksson, and Anton Wiberg. "Towards digital representations for brownfield factories using synthetic data generation and 3D object detection." Proceedings of the Design Society 4 (May 2024): 2297–306. http://dx.doi.org/10.1017/pds.2024.232.

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AbstractThis study emphasizes the importance of automatic synthetic data generation in data-driven applications, especially in the development of a 3D computer vision system for engineering contexts such as brownfield factory projects, where no data is readily available. Key points: (1) A successful integration of a synthetic data generator with the S3DIS dataset, leading to a significant enhancement in object detection of previous classes and enabling recognition of new ones; (2) A proposal for a CAD-based configurator for efficient and customizable scene reconstruction from LiDAR scanner poi

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Gusmão, Guilherme Ferreira, Carlos Roberto Hall Barbosa, and Alberto Barbosa Raposo. "Development and Validation of LiDAR Sensor Simulators Based on Parallel Raycasting." Sensors 20, no. 24 (2020): 7186. http://dx.doi.org/10.3390/s20247186.

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Three-dimensional (3D) imaging technologies have been increasingly explored in academia and the industrial sector, especially the ones yielding point clouds. However, obtaining these data can still be expensive and time-consuming, reducing the efficiency of procedures dependent on large datasets, such as the generation of data for machine learning training, forest canopy calculation, and subsea survey. A trending solution is developing simulators for imaging systems, performing the virtual scanning of the digital world, and generating synthetic point clouds from the targets. This work presents

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Schuster, Alexander, Raphael Hagmanns, Iman Sonji, et al. "Synthetic data generation for the continuous development and testing of autonomous construction machinery." at - Automatisierungstechnik 71, no. 11 (2023): 953–68. http://dx.doi.org/10.1515/auto-2023-0026.

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Abstract The development and testing of autonomous systems require sufficient meaningful data. However, generating suitable scenario data is a challenging task. In particular, it raises the question of how to narrow down what kind of data should be considered meaningful. Autonomous systems are characterized by their ability to cope with uncertain situations, i.e. complex and unknown environmental conditions. Due to this openness, the definition of training and test scenarios cannot be easily specified. Not all relevant influences can be sufficiently specified with requirements in advance, espe

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Soto Sagredo, Esperanza, Ásta Hannesdóttir, Jennifer M. Rinker, and Michael Courtney. "Reconstructing turbulent wind-fields using inverse-distance-weighting interpolation and measurements from a pulsed mounted-hub lidar." Journal of Physics: Conference Series 2745, no. 1 (2024): 012017. http://dx.doi.org/10.1088/1742-6596/2745/1/012017.

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Abstract This study evaluates a numerical multi-beam pulsed lidar mounted on the hub of the NREL 5MW reference wind turbine using the HAWC2 v13.1 numerical sensor for synthetic lidar measurement generation. While initially designed for single-beam operations, it facilitates multi-beam configuration simulations. We conducted an analysis of full-rotor longitudinal wind speed reconstruction by combining inverse-distance-weighting with synthetic sensor data from HAWC2. Utilizing a Mann-generated turbulence box for wind input at U = 11.4 m/s, we examined three lidar configurations for efficacy. The

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Zhang, Junge, Feihu Zhang, Shaochen Kuang, and Li Zhang. "NeRF-LiDAR: Generating Realistic LiDAR Point Clouds with Neural Radiance Fields." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 7 (2024): 7178–86. http://dx.doi.org/10.1609/aaai.v38i7.28546.

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Labelling LiDAR point clouds for training autonomous driving is extremely expensive and difficult. LiDAR simulation aims at generating realistic LiDAR data with labels for training and verifying self-driving algorithms more efficiently. Recently, Neural Radiance Fields (NeRF) have been proposed for novel view synthesis using implicit reconstruction of 3D scenes. Inspired by this, we present NeRF-LIDAR, a novel LiDAR simulation method that leverages real-world information to generate realistic LIDAR point clouds. Different from existing LiDAR simulators, we use real images and point cloud data

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Chitnis, S. A., Z. Huang, and K. Khoshelham. "GENERATING SYNTHETIC 3D POINT SEGMENTS FOR IMPROVED CLASSIFICATION OF MOBILE LIDAR POINT CLOUDS." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B2-2021 (June 28, 2021): 139–44. http://dx.doi.org/10.5194/isprs-archives-xliii-b2-2021-139-2021.

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Abstract. Mobile lidar point clouds are commonly used for 3d mapping of road environments as they provide a rich, highly detailed geometric representation of objects on and around the road. However, raw lidar point clouds lack semantic information about the type of objects, which is necessary for various applications. Existing methods for the classification of objects in mobile lidar data, including state of the art deep learning methods, achieve relatively low accuracies, and a primary reason for this under-performance is the inadequacy of available 3d training samples to sufficiently train d

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Donovan, David P., Pavlos Kollias, Almudena Velázquez Blázquez, and Gerd-Jan van Zadelhoff. "The generation of EarthCARE L1 test data sets using atmospheric model data sets." Atmospheric Measurement Techniques 16, no. 21 (2023): 5327–56. http://dx.doi.org/10.5194/amt-16-5327-2023.

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Abstract. The Earth Cloud, Aerosol and Radiation Explorer mission (EarthCARE) is a multi-instrument cloud–aerosol–radiation process study mission embarking a high spectral resolution lidar, a cloud profiling radar, a multi-spectral imager, and a three-view broadband radiometer. An important aspect of the EarthCARE mission is its focus on instrument synergy. Many L2 products are the result of L1 inputs from one or more instruments. Since no existing complete observational proxy data sets comprised of co-located and co-temporal “EarthCARE-like” data exists, it has been necessary to create synthe

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Goo, June Moh, Zichao Zeng, and Jan Boehm. "Zero-Shot Detection of Buildings in Mobile LiDAR using Language Vision Model." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-2-2024 (June 11, 2024): 107–13. http://dx.doi.org/10.5194/isprs-archives-xlviii-2-2024-107-2024.

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Abstract. Recent advances have demonstrated that Language Vision Models (LVMs) surpass the existing State-of-the-Art (SOTA) in two-dimensional (2D) computer vision tasks, motivating attempts to apply LVMs to three-dimensional (3D) data. While LVMs are efficient and effective in addressing various downstream 2D vision tasks without training, they face significant challenges when it comes to point clouds, a representative format for representing 3D data. It is more difficult to extract features from 3D data and there are challenges due to large data sizes and the cost of the collection and label

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Artigao, Estefania, Antonio Vigueras-Rodríguez, Andrés Honrubia-Escribano, Sergio Martín-Martínez, and Emilio Gómez-Lázaro. "Wind Resource and Wind Power Generation Assessment for Education in Engineering." Sustainability 13, no. 5 (2021): 2444. http://dx.doi.org/10.3390/su13052444.

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This paper proposes a practical approach to assess wind energy resource and calculate annual energy production for use on university courses in engineering. To this end, two practical exercises were designed in the open-source software GNU Octave (compatible with MATLAB) using both synthetic and field data. The script used to generate the synthetic data as well as those created to develop the practical exercises are included for the benefit of other educational bodies. With the first exercise the students learn how to characterize the wind resource at the wind turbine hub height and adjust it

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Brown, Kyle M., Crispin H. Hambidge, and Jonathan M. Brownett. "Progress in operational flood mapping using satellite synthetic aperture radar (SAR) and airborne light detection and ranging (LiDAR) data." Progress in Physical Geography: Earth and Environment 40, no. 2 (2016): 196–214. http://dx.doi.org/10.1177/0309133316633570.

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During flooding, operational tools for mapping flood extent and depth of water in flood-prone areas are required by those planning emergency response, including UK statutory agencies such as the Environment Agency. Satellite data have become a source of information to map and monitor floods, but many of the methods developed to process the data are unsuitable for accurate, near real-time production of flood information products. This paper describes a new semi-automated methodology developed to provide operational mapping of flood extent and flood depth using satellite synthetic aperture radar

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Ren, Peng, and Qunli Xia. "Classification method for imbalanced LiDAR point cloud based on stack autoencoder." Electronic Research Archive 31, no. 6 (2023): 3453–70. http://dx.doi.org/10.3934/era.2023175.

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<abstract><p>The existing classification methods of LiDAR point cloud are almost based on the assumption that each class is balanced, without considering the imbalanced class problem. Moreover, from the perspective of data volume, the LiDAR point cloud classification should be a typical big data classification problem. Therefore, by studying the existing deep network structure and imbalanced sampling methods, this paper proposes an oversampling method based on stack autoencoder. The method realizes automatic generation of synthetic samples by learning the distribution characteristi

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Ma'arif, Syamsul, Irwanuddin H. I. Kulla, and Lia Yunita. "Enhancing Renewable Energy Power Generation through the Exploration of Laser Technology." Jurnal Offshore: Oil, Production Facilities and Renewable Energy 7, no. 2 (2023): 59–67. https://doi.org/10.30588/jo.v7i2.1739.

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This research aims to investigate the utilization of laser technology in improving the performance of power generation systems from renewable energy sources, focusing on solar, wind, hydropower, and biomass energy. A literature review and comprehensive analysis were conducted using a descriptive and synthetic data analysis method. The discussion results indicate that laser technology can significantly contribute to the utilization of renewable energy. In solar power generation systems, laser technology enhances solar radiation absorption in solar thermophotovoltaics applications and flexible s

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Sánchez, Manuel, Jesús Morales, and Jorge L. Martínez. "Waypoint Generation in Satellite Images Based on a CNN for Outdoor UGV Navigation." Machines 11, no. 8 (2023): 807. http://dx.doi.org/10.3390/machines11080807.

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Moving on paths or trails present in natural environments makes autonomous navigation of unmanned ground vehicles (UGV) simpler and safer. In this sense, aerial photographs provide a lot of information of wide areas that can be employed to detect paths for UGV usage. This paper proposes the extraction of paths from a geo-referenced satellite image centered at the current UGV position. Its pixels are individually classified as being part of a path or not using a convolutional neural network (CNN) which has been trained using synthetic data. Then, successive distant waypoints inside the detected

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Sagar, Anwar, Kalle Kärhä, Kalervo Järvelin, and Reza Ghabcheloo. "Evaluation of Simulation Framework for Detecting the Quality of Forest Tree Stems." Forests 16, no. 6 (2025): 1023. https://doi.org/10.3390/f16061023.

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The advancement of harvester technology increasingly relies on automated forest analysis within machine operational ranges. However, real-world testing remains costly and time-consuming. To address this, we introduced the Tree Classification Framework (TCF), a simulation platform for the cost-effective testing of harvester technologies. TCF accelerates technology development by simulating forest environments and machine operations, leveraging machine-learning and computer vision models. TCF has four components: Synthetic Forest Creation, which generates diverse virtual forests; Point Cloud Gen

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Palama, R., O. Monserrat, B. Crippa, et al. "Radargrammetry DEM Generation Using High-Resolution SAR Imagery Over La Palma During the 2021 Cumbre Vieja Volcanic Eruption." IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 20 (January 1, 2023): 4000705. https://doi.org/10.1109/LGRS.2023.3238182.

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This letter aims at investigating the potential of high-resolution (up to 0.7 x 0.5 m(2)) synthetic aperture radar (SAR) images in generating digital elevation models (DEMs) using the radargrammetry technique. In this work, we process two SAR images recorded by the Capella Space X-band satellite borne radar sensor on two consecutive days, October 2 and 3, 2021, over La Palma (Canary Islands, Spain) during the Cumbre Vieja volcanic eruption. We adopt an iterative point aggregation algorithm to identify matching pixels between the two images; then, the height estimation is performed using a dist

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Tian, Weiming, Zheng Zhao, Cheng Hu, Jingyang Wang, and Tao Zeng. "GB-InSAR-Based DEM Generation Method and Precision Analysis." Remote Sensing 11, no. 9 (2019): 997. http://dx.doi.org/10.3390/rs11090997.

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Ground-based interferometric technology plays an important role in the terrain mapping sphere because it is characterized by short observation intervals, a flexible operation environment, and high data precision. Ground-based interferometric synthetic aperture radar (GB-InSAR) has a wide beam, a scene breadth comparative to the slant range, and a large downwards-looking angle. The observation scenes always show the type of slope terrain with various gradients and slope orientations. These particularities cause the invalidation of the typical terrain generation method and produce poor precision

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Rodríguez-Martínez, Eder A., Wendy Flores-Fuentes, Farouk Achakir, Oleg Sergiyenko, and Fabian N. Murrieta-Rico. "Vision-Based Navigation and Perception for Autonomous Robots: Sensors, SLAM, Control Strategies, and Cross-Domain Applications—A Review." Eng 6, no. 7 (2025): 153. https://doi.org/10.3390/eng6070153.

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Camera-centric perception has matured into a cornerstone of modern autonomy, from self-driving cars and factory cobots to underwater and planetary exploration. This review synthesizes more than a decade of progress in vision-based robotic navigation through an engineering lens, charting the full pipeline from sensing to deployment. We first examine the expanding sensor palette—monocular and multi-camera rigs, stereo and RGB-D devices, LiDAR–camera hybrids, event cameras, and infrared systems—highlighting the complementary operating envelopes and the rise of learning-based depth inference. The

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Radke, David, Daniel Radke, and John Radke. "Beyond Measurement: Extracting Vegetation Height from High Resolution Imagery with Deep Learning." Remote Sensing 12, no. 22 (2020): 3797. http://dx.doi.org/10.3390/rs12223797.

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Measuring and monitoring the height of vegetation provides important insights into forest age and habitat quality. These are essential for the accuracy of applications that are highly reliant on up-to-date and accurate vegetation data. Current vegetation sensing practices involve ground survey, photogrammetry, synthetic aperture radar (SAR), and airborne light detection and ranging sensors (LiDAR). While these methods provide high resolution and accuracy, their hardware and collection effort prohibits highly recurrent and widespread collection. In response to the limitations of current methods

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Doyog, Nova D., and Chinsu Lin. "Generating Wall-to-Wall Canopy Height Information from Discrete Data Provided by Spaceborne LiDAR System." Forests 15, no. 3 (2024): 482. http://dx.doi.org/10.3390/f15030482.

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Provision of multi-temporal wall-to-wall canopy height information is one of the initiatives to combat deforestation and is necessary in strategizing forest conversion and reforestation initiatives. This study generated wall-to-wall canopy height information of the subtropical forest of Lishan, Taiwan, using discrete data provided by spaceborne LiDARs, wall-to-wall passive and active remote sensing imageries, topographic data, and machine learning (ML) regression models such as gradient boosting (GB), k-nearest neighbor (k-NN), and random forest (RF). ICESat-2- and GEDI-based canopy height dat

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Bagheri, Hossein, Michael Schmitt, and Xiaoxiang Zhu. "Fusion of Multi-Sensor-Derived Heights and OSM-Derived Building Footprints for Urban 3D Reconstruction." ISPRS International Journal of Geo-Information 8, no. 4 (2019): 193. http://dx.doi.org/10.3390/ijgi8040193.

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So-called prismatic 3D building models, following the level-of-detail (LOD) 1 of the OGC City Geography Markup Language (CityGML) standard, are usually generated automatically by combining building footprints with height values. Typically, high-resolution digital elevation models (DEMs) or dense LiDAR point clouds are used to generate these building models. However, high-resolution LiDAR data are usually not available with extensive coverage, whereas globally available DEM data are often not detailed and accurate enough to provide sufficient input to the modeling of individual buildings. There

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Zhou, Liwei, Jiaying Tan, Jing Fu, and Guiwei Shao. "Fast 3D Transmission Tower Detection Based on Virtual Views." Applied Sciences 15, no. 2 (2025): 947. https://doi.org/10.3390/app15020947.

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Advanced remote sensing technologies leverage extensive synthetic aperture radar (SAR) satellite data and high-resolution airborne light detection and ranging (LiDAR) data to swiftly capture comprehensive 3D information about electrical grid assets and their surrounding environments. This facilitates in-depth scene analysis for target detection and classification, allowing for the early recognition of potential hazards near transmission towers (TTs). These innovations present a groundbreaking strategy for the automated inspection of electrical grid assets. However, traditional 3D target detect

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Whitehead, Ken, and Chris H. Hugenholtz. "Remote sensing of the environment with small unmanned aircraft systems (UASs), part 1: a review of progress and challenges." Journal of Unmanned Vehicle Systems 02, no. 03 (2014): 69–85. http://dx.doi.org/10.1139/juvs-2014-0006.

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The recent development and proliferation of unmanned aircraft systems (UASs) has made it possible to examine environmental processes and changes occurring at spatial and temporal scales that would be difficult or impossible to detect using conventional remote sensing platforms. This review article highlights new developments in UAS-based remote sensing, focusing mainly on small UASs (<25 kg). Because this class is generally less expensive and more versatile than larger systems the use of small UASs for civil, commercial, and scientific applications is expected to expand considerably in the

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Tran, H., and K. Khoshelham. "A STOCHASTIC APPROACH TO AUTOMATED RECONSTRUCTION OF 3D MODELS OF INTERIOR SPACES FROM POINT CLOUDS." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences IV-2/W5 (May 29, 2019): 299–306. http://dx.doi.org/10.5194/isprs-annals-iv-2-w5-299-2019.

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<p><strong>Abstract.</strong> Automated reconstruction of 3D interior models has recently been a topic of intensive research due to its wide range of applications in Architecture, Engineering, and Construction. However, generation of the 3D models from LiDAR data and/or RGB-D data is challenged by not only the complexity of building geometries, but also the presence of clutters and the inevitable defects of the input data. In this paper, we propose a stochastic approach for automatic reconstruction of 3D models of interior spaces from point clouds, which is applicable to both

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Bugliaro, Luca, Dennis Piontek, Stephan Kox, et al. "VADUGS: a neural network for the remote sensing of volcanic ash with MSG/SEVIRI trained with synthetic thermal satellite observations simulated with a radiative transfer model." Natural Hazards and Earth System Sciences 22, no. 3 (2022): 1029–54. http://dx.doi.org/10.5194/nhess-22-1029-2022.

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Abstract. After the eruption of volcanoes around the world, monitoring of the dispersion of ash in the atmosphere is an important task for satellite remote sensing since ash represents a threat to air traffic. In this work we present a novel method, tailored for Eyjafjallajökull ash but applicable to other eruptions as well, that uses thermal observations of the SEVIRI imager aboard the geostationary Meteosat Second Generation satellite to detect ash clouds and determine their mass column concentration and top height during the day and night. This approach requires the compilation of an extens

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Mirbod, Omeed, Daeun Choi, and John K. Schueller. "From Simulation to Field Validation: A Digital Twin-Driven Sim2real Transfer Approach for Strawberry Fruit Detection and Sizing." AgriEngineering 7, no. 3 (2025): 81. https://doi.org/10.3390/agriengineering7030081.

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Typically, developing new digital agriculture technologies requires substantial on-site resources and data. However, the crop’s growth cycle provides only limited time windows for experiments and equipment validation. This study presents a photorealistic digital twin of a commercial-scale strawberry farm, coupled with a simulated ground vehicle, to address these constraints by generating high-fidelity synthetic RGB and LiDAR data. These data enable the rapid development and evaluation of a deep learning-based machine vision pipeline for fruit detection and sizing without continuously relying o

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Mercy Muroyiwa. "Revolutionizing Resilience: A Comprehensive Review of Technological, AI-Driven Innovations in Anchorage Zone Design and Maintenance." Journal of Innovation and Development 11, no. 2 (2025): 118–23. https://doi.org/10.54097/b3g5x678.

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Anchorage zones, the critical junctures transferring immense prestressing forces in concrete and steel structures, remain persistent vulnerability hot spots. Susceptible to stress concentrations, corrosion propagation, and fatigue-induced degradation, their premature failure jeopardizes structural integrity despite conservative design codes and labor-intensive inspections. Traditional approaches often fail to capture the dynamic interplay of environmental stressors (chloride ingress, humidity fluctuations, thermal cycling) and evolving operational loads (increasing traffic volumes, extreme wea

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Shin, Alvin Lau Meng, Wenxi Chen, Toshinori Okuda, Hui Lin Sim, T. H. Tam, and W. C. Chew. "Performance Analysis of Forest Canopy Height Model Generated from UAV and InSAR." Journal of Advanced Geospatial Science & Technology 4, no. 1 (2024): 86–105. http://dx.doi.org/10.11113/jagst.v4n1.88.

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Forest canopy height is a crucial parameter in ecosystem process modelling, yet research on generating canopy height models (CHMs) using photogrammetry method from UAV data remains limited compared to methods such as light detection and ranging (LiDAR). This study investigates the performance of accuracy and effectiveness of CHM generated from low-cost Unmanned Aerial Vehicle (UAV) via the photogrammetry method together with the well-known long-established Interferometric Synthetic Aperture Radar (InSAR). Leveraging advancements in UAV technology for three-dimensional land surface modelling, t

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Morse, Corinne S., Robert K. Goodrich, and Larry B. Cornman. "The NIMA Method for Improved Moment Estimation from Doppler Spectra." Journal of Atmospheric and Oceanic Technology 19, no. 3 (2002): 274–95. http://dx.doi.org/10.1175/1520-0426-19.3.274.

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Abstract The NCAR Improved Moments Algorithm (NIMA) for estimating moments from wind measurement devices that measure Doppler spectra as a function of range is described in some detail. Although NIMA's main application has been for real-time processing of wind profiler data, it has also been successfully applied to Doppler lidar and weather radar data. Profiler spectra are often contaminated by a variety of sources including aircraft, birds, velocities exceeding the Nyquist velocity, radio frequency interference, and ground clutter. The NIMA method uses mathematical analysis, fuzzy logic synth

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Calvo, Roberto Crespo, Mª Ángeles Varo Martínez, Francisco Ruiz Gómez, Antonio Jesús Ariza Salamanca, and Rafael M. Navarro-Cerrillo. "Improvements of Fire Fuels Attributes Maps by Integrating Field Inventories, Low Density ALS, and Satellite Data in Complex Mediterranean Forests." Remote Sensing 15, no. 8 (2023): 2023. http://dx.doi.org/10.3390/rs15082023.

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One of the most determining factors in forest fire behaviour is to characterize forest fuel attributes. We investigated a complex Mediterranean forest type—mountainous Abies pinsapo–Pinus–Quercus–Juniperus with distinct structures, such as broadleaf and needleleaf forests—to integrate field data, low density Airborne Laser Scanning (ALS), and multispectral satellite data for estimating forest fuel attributes. The three-step procedure consisted of: (i) estimating three key forest fuel attributes (biomass, structural complexity and hygroscopicity), (ii) proposing a synthetic index that encompass

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Zhao, H., M. Tomko, and K. Khoshelham. "ENTROPY-BASED INDOOR CHANGE DETECTION USING LIDAR DATA AND A 3D MODEL." ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences X-1/W1-2023 (December 5, 2023): 287–93. http://dx.doi.org/10.5194/isprs-annals-x-1-w1-2023-287-2023.

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Abstract. Indoor change detection is important for building monitoring, building management and model-based localization and navigation systems because the real building environment may not always be the same as the design model. This paper presents a novel indoor building change detection method based on entropy. A sequence of real LiDAR scans is acquired with a static LiDAR scanner and the pose of the LiDAR scanner for each scan is then estimated. Synthetic LiDAR scans are generated with the pose of the LiDAR scanner using the 3D model. The real LiDAR scans and synthetic LiDAR scans are slic

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Xiaoye Liu. "Airborne LiDAR for DEM generation: some critical issues." Progress in Physical Geography: Earth and Environment 32, no. 1 (2008): 31–49. http://dx.doi.org/10.1177/0309133308089496.

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Airborne LiDAR is one of the most effective and reliable means of terrain data collection. Using LiDAR data for digital elevation model (DEM) generation is becoming a standard practice in spatial related areas. However, the effective processing of the raw LiDAR data and the generation of an efficient and high-quality DEM remain big challenges. This paper reviews the recent advances of airborne LiDAR systems and the use of LiDAR data for DEM generation, with special focus on LiDAR data filters, interpolation methods, DEM resolution, and LiDAR data reduction. Separating LiDAR points into ground

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Abtew, Wossenu, Rafael G. Moras, and K. L. Campbell. "Synthetic precipitation data generation." Computers & Industrial Engineering 19, no. 1-4 (1990): 582–86. http://dx.doi.org/10.1016/0360-8352(90)90185-o.

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Shan, Jie, and Sampath Aparajithan. "Urban DEM Generation from Raw Lidar Data." Photogrammetric Engineering & Remote Sensing 71, no. 2 (2005): 217–26. http://dx.doi.org/10.14358/pers.71.2.217.

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Zhao, H., D. Acharya, M. Tomko, and K. Khoshelham. "INDOOR LIDAR RELOCALIZATION BASED ON DEEP LEARNING USING A 3D MODEL." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B1-2020 (August 6, 2020): 541–47. http://dx.doi.org/10.5194/isprs-archives-xliii-b1-2020-541-2020.

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Abstract. Indoor localization, navigation and mapping systems highly rely on the initial sensor pose information to achieve a high accuracy. Most existing indoor mapping and navigation systems cannot initialize the sensor poses automatically and consequently these systems cannot perform relocalization and recover from a pose estimation failure. For most indoor environments, a map or a 3D model is often available, and can provide useful information for relocalization. This paper presents a novel relocalization method for lidar sensors in indoor environments to estimate the initial lidar pose us

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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

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Lakshmanan, Kayal, Matt Roach, Cinzia Giannetti, et al. "A Robust Vehicle Detection Model for LiDAR Sensor Using Simulation Data and Transfer Learning Methods." AI 4, no. 2 (2023): 461–81. http://dx.doi.org/10.3390/ai4020025.

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Vehicle detection in parking areas provides the spatial and temporal utilisation of parking spaces. Parking observations are typically performed manually, limiting the temporal resolution due to the high labour cost. This paper uses simulated data and transfer learning to build a robust real-world model for vehicle detection and classification from single-beam LiDAR of a roadside parking scenario. The paper presents a synthetically augmented transfer learning approach for LiDAR-based vehicle detection and the implementation of synthetic LiDAR data. A synthetic augmented transfer learning metho

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Li, Xiaozhen, Liang Guo, and Minyi Shen. "Inverse synthetic aperture lidar imaging of indoor real data." Optik 181 (March 2019): 28–35. http://dx.doi.org/10.1016/j.ijleo.2018.11.074.

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Sagduyu, Yalin E., Alexander Grushin, and Yi Shi. "Synthetic Social Media Data Generation." IEEE Transactions on Computational Social Systems 5, no. 3 (2018): 605–20. http://dx.doi.org/10.1109/tcss.2018.2854668.

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Mohapatra, Shubhankar, Jianqiao Zong, Florian Kerschbaum, and Xi He. "Differentially Private Data Generation with Missing Data." Proceedings of the VLDB Endowment 17, no. 8 (2024): 2022–35. http://dx.doi.org/10.14778/3659437.3659455.

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Despite several works that succeed in generating synthetic data with differential privacy (DP) guarantees, they are inadequate for generating high-quality synthetic data when the input data has missing values. In this work, we formalize the problems of DP synthetic data with missing values and propose three effective adaptive strategies that significantly improve the utility of the synthetic data on four real-world datasets with different types and levels of missing data and privacy requirements. We also identify the relationship between privacy impact for the complete ground truth data and in

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Zhao, Sicheng, Yezhen Wang, Bo Li, et al. "ePointDA: An End-to-End Simulation-to-Real Domain Adaptation Framework for LiDAR Point Cloud Segmentation." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 4 (2021): 3500–3509. http://dx.doi.org/10.1609/aaai.v35i4.16464.

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Due to its robust and precise distance measurements, LiDAR plays an important role in scene understanding for autonomous driving. Training deep neural networks (DNNs) on LiDAR data requires large-scale point-wise annotations, which are time-consuming and expensive to obtain. Instead, simulation-to-real domain adaptation (SRDA) trains a DNN using unlimited synthetic data with automatically generated labels and transfers the learned model to real scenarios. Existing SRDA methods for LiDAR point cloud segmentation mainly employ a multi-stage pipeline and focus on feature-level alignment. They req

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Rzonca, Antoni, and Mariusz Twardowski. "Lidargrammetric co-matching and co-adjustment – a new method of photogrammetric and LiDAR data integration." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-1/W4-2025 (June 16, 2025): 123–30. https://doi.org/10.5194/isprs-archives-xlviii-1-w4-2025-123-2025.

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Abstract. The accuracy of spatial raw data is contingent on a number of factors, including the accuracy of the sensors, their calibration, and direct and indirect referencing. The efficacy of the captured data can be enhanced through the implementation of effective data processing methodologies. The present research is dedicated to the enhancement of combined photogrammetric and LiDAR data.In the contemporary context, the trajectory of the vehicle can be affected by GPS signal jamming, a phenomenon that is attributable to international circumstances. This necessitates a combined adjustment of

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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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Ma, Ruijin. "DEM Generation and Building Detection from Lidar Data." Photogrammetric Engineering & Remote Sensing 71, no. 7 (2005): 847–54. http://dx.doi.org/10.14358/pers.71.7.847.

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Pargieła, K., A. Rzonca, and M. Twardowski. "THE UTILIZATION OF SYNTHETIC AND SEMISYNTHETIC POINT CLOUDS AND IMAGES FOR TESTING NOVEL APPROACHES FOR CORRECTING LIDAR DATA." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-1/W3-2023 (October 19, 2023): 145–51. http://dx.doi.org/10.5194/isprs-archives-xlviii-1-w3-2023-145-2023.

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Abstract. The paper presents the application of lidar data and photo datasets, external orientation parameters (EOPs), ground control points (GCPs), and check points for testing new methods of geometric lidar data correction. These datasets are utilized to validate novel approaches such as altimetric deformation methods based on stereo models or lidargrammetric methods that utilize image matching and specialized lidar data formats. The paper presents specific use cases of these data as examples of two tested processes. After describing these processes, the methods of synthetic and semisyntheti

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Parida, G., and K. S. Rajan. "LOCALIZED SEGMENT BASED PROCESSING FOR AUTOMATIC BUILDING EXTRACTION FROM LiDAR DATA." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-1/W1 (May 31, 2017): 473–80. http://dx.doi.org/10.5194/isprs-archives-xlii-1-w1-473-2017.

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The current methods of object segmentation and extraction and classification of aerial LiDAR data is manual and tedious task. This work proposes a technique for object segmentation out of LiDAR data. A bottom-up geometric rule based approach was used initially to devise a way to segment buildings out of the LiDAR datasets. For curved wall surfaces, comparison of localized surface normals was done to segment buildings. The algorithm has been applied to both synthetic datasets as well as real world dataset of Vaihingen, Germany. Preliminary results show successful segmentation of the buildings o

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Lychev, Andrey V. "Synthetic Data Generation for Data Envelopment Analysis." Data 8, no. 10 (2023): 146. http://dx.doi.org/10.3390/data8100146.

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The paper is devoted to the problem of generating artificial datasets for data envelopment analysis (DEA), which can be used for testing DEA models and methods. In particular, the papers that applied DEA to big data often used synthetic data generation to obtain large-scale datasets because real datasets of large size, available in the public domain, are extremely rare. This paper proposes the algorithm which takes as input some real dataset and complements it by artificial efficient and inefficient units. The generation process extends the efficient part of the frontier by inserting artificia

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Bacher, U. "3D CONTENT GENERATION USING HYBRID AERIAL SENSOR DATA." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B2-2021 (June 28, 2021): 297–303. http://dx.doi.org/10.5194/isprs-archives-xliii-b2-2021-297-2021.

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Abstract. In aerial data acquisition a new era started with the introduction of the first real hybrid sensor systems, like the Leica CityMapper-2. Hybrid in this context means the combination of an (oblique) camera system with a topographic LiDAR into an integrated aerial mapping system. By combining these complimentary sub-systems into one system the weaknesses of the one system could be compensated by using the alternative data source. An example is the mapping of low-light urban canyons, where image-based systems mostly produce unreliable results. For an LiDAR sensor the geometrical reconst

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Lu Tianan, 鲁天安, and 李洪平 Li Hongping. "Phase Error Compensation in Airborne Synthetic Aperture Lidar Data Processing." Acta Optica Sinica 35, no. 8 (2015): 0801002. http://dx.doi.org/10.3788/aos201535.0801002.

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