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The expansion of deployed traffic monitoring systems and information transmission is a crucial step towards increasing the efficiency, reliability and safety of vehicular transportation. Under the current context in terrestrial transportation, the implementation of real-time traffic analysis mechanisms can provide more insight into the network, leading to better informed decisions on how to direct traffic and plan roadways. In the future, as we move towards the integration of fully autonomous vehicles to a transportation network with human drivers, the extraction and processing of real time data will become even more crucial to ensure safe transition. In this paper we present a real time data analysis system for in-flight vehicle detection as an option for the expansion of traffic monitoring. The presented solution is able to perform typical post-flight processing in real time, with minimal computational and power requirements, which allows its implementation on light-weight UAS. It utilizes adaptive segmentation and 3D convolutions that take advantage of the structure of the LiDAR point cloud, to identify vehicles and their respective positions within 3D point cloud segments that may include background clutter. All the necessary positioning information required to run the algorithm are introduced along with a detailed description for the computational steps extracting the desired features from the raw data. We provide the timing constraints for the system and evaluate its performance while considering different optimization variables and computation capabilities.
LiDAR (or airborne laser scanning) systems became a dominant player in high-precision spatial data acquisition to efficiently create DEM/DSM in the late 90's. With increasing point density, new systems are now able to support object extraction, such as extracting building and roads, from LiDAR data. The novel concept of this project was to use LiDAR data for traffic flow estimates. In a sense, extracting vehicles over transportation corridors represents the next step in complexity by adding the temporal component to the LiDAR data feature extraction process. The facts are that vehicles are moving at highway speeds and the scanning acquisition mode of the LiDAR certainly poses a serious challenge for the data extraction process. The OSU developed method and its implementation, the I FLOW program, have demonstrated that LiDAR data contain valuable information to support vehicle extraction, including vehicle grouping and localizations. The classification performance showed strong evidence that the major vehicle categories can be efficiently separated. The I FLOW program is ready for deployment.
Light detection and ranging, or LiDAR, is an advanced active remote sensing technology developed in the last 30 years to measure variable distances to the Earth. This book explains the fundamental concepts of LiDAR technology and its extended spaceborne, airborne, terrestrial, mobile, and unmanned aerial vehicle (UAV) platforms. It addresses the challenges of massive LiDAR data intelligent processing, LiDAR software engineering, and in-depth applications. The theory and algorithms are integrated with multiple applications in a systematic way and with step-by-step instructions. Written for undergraduate and graduate students and practitioners in the field of LiDAR remote sensing, this book is a much-needed comprehensive resource. FEATURES Explains the fundamentals of LiDAR remote sensing, including theory, techniques, methods, and applications Highlights the dissemination and popularization of LiDAR remote sensing technology in the last decade Includes new advances in LiDAR data processing and applications Introduces new technologies such as spaceborne LiDAR and photon-counting LiDAR Provides multiple LiDAR application cases regarding topography mapping, forest investigation, power line inspection, building modeling, automatic driving, crop monitoring, indoor navigation, cultural heritage conservation, and underwater mapping This book is written for graduate and upper-level undergraduate students taking courses in remote sensing, geography, photogrammetric engineering, laser techniques, surveying and mapping, geographic information systems (GIS), forestry, and resources and environmental protection. It is also a comprehensive resource for researchers and scientists interested in learning techniques for collecting LiDAR remote sensing data and processing, analyzing, and managing LiDAR data for applications in forestry, surveying and mapping, cultural relic protection, and digital products. Chapters 1 and 2 of this book are freely available as a downloadable Open Access PDF at http://www.taylorfrancis.com under a Creative Commons Attribution-Non Commercial-No Derivatives (CC-BY-NC-ND) 4.0 license.
This book collects the papers in the special issue "Airborne Laser Scanning" in Remote Sensing (Nov. 2016) and several other selected papers published in the same journal in the past few years. Our intention is to reflect recent technological developments and innovative techniques in this field. The book consists of 23 papers in six subject areas: 1) Single photon and Geiger-mode Lidar, 2) Multispectral lidar, 3) Waveform lidar, 4) Registration of point clouds, 5) Trees and terrain, and 6) Building extraction. The book is a valuable resource for scientists, engineers, developers, instructors, and graduate students interested in lidar systems and data processing.
ODOT's Office of Aerial Engineering (OAE) has been using an Opetch 30/70 ALTM airborne LiDAR system for about four years. The introduction of LiDAR technology was a major development towards improving the mapping operations. The overall experiences are excellent as evidenced by numerous projects where highly accurate surface data were produced in an unprecedentedly short time. As is typically with new technology, OAE has identified areas for improvements in terms of achieving better accuracy and increasing data processing efficiency. In particular, the horizontal accuracy of the LiDAR product required further attention. The objectives of this research were to (1) introduce ground control to LiDAR by using road pavement makings that can be precisely surveyed by ODOT's system; (2) preform a strip adjustment for seamless integration of strips into the final product; (3) improve the horizontal accuracy in order to better characterize the final product; and (4) improve accuracy (both horizontal and vertical) to use ground control that is less labor-intense, requires no or limited surveying and imposes less restrictions in normal field operations.
This book aims to promote the core understanding of a proper modelling of road traffic accidents by deep learning methods using traffic information and road geometry delineated from laser scanning data. The first two chapters of the book introduce the reader to laser scanning technology with creative explanation and graphical illustrations, review and recent methods of extracting geometric road parameters. The next three chapters present different machine learning and statistical techniques applied to extract road geometry information from laser scanning data. Chapters 6 and 7 present methods for modelling roadside features and automatic road geometry identification in vector data. After that, this book goes on reviewing methods used for road traffic accident modelling including accident frequency and injury severity of the traffic accident (Chapter 8). Then, the next chapter explores the details of neural networks and their performance in predicting the traffic accidents along with a comparison with common data mining models. Chapter 10 presents a novel hybrid model combining extreme gradient boosting and deep neural networks for predicting injury severity of road traffic accidents. This chapter is followed by deep learning applications in modelling accident data using feed-forward, convolutional, recurrent neural network models (Chapter 11). The final chapter (Chapter 12) presents a procedure for modelling traffic accident with little data based on the concept of transfer learning. This book aims to help graduate students, professionals, decision makers, and road planners in developing better traffic accident prediction models using advanced neural networks.
LiDAR (or airborne laser scanning) systems became a dominant player in high-precision spatial data acquisition in the late 90's. This new technology quickly established itself as the main source of surface information in commercial mapping, delivering surface data at decimeter-level vertical accuracy in an almost totally automated way. With increasing point density, new systems are able to support object extraction, such as extracting building and roads from LiDAR data.
This edition is a reprint of the Special Issue published online in the open access journal Remote Sensing (ISSN 2072-4292) from 2016-2017 (available at: http://www.mdpi.com/journal/remotesensing/special issues/rsALS), complemented by selected articles published in Remote Sensing.
Three dimensional (3D) information about ground and above-ground features such as buildings and trees is important for many urban and environmental applications. Recent developments in Light Detection And Ranging (LiDAR) technology provide promising alternatives to conventional techniques for acquiring such information. The focus of this dissertation research is to effectively and efficiently filter massive airborne LiDAR point cloud data and to extract main above-ground features such as buildings and trees in the urban area. A novel segmentation algorithm for point cloud data, namely the 3D k mutual nearest neighborhood ( k MNN) segmentation algorithm, was developed based on the improvement to the k MNN clustering algorithm by employing distances in 3D space to define mutual nearest neighborhoods. A set of optimization strategies, including dividing dataset into multiple blocks and small size grids, and using distance thresholds in x and y, were implemented to improve the efficiency of the segmentation algorithm. A segmentation based filtering method was then employed to filter the generated segments, which first generates segment boundaries using Voronoi polygon and dissolving operations, and then labels the segments as ground and above-ground based on their size and relative heights to the surrounding segments. An object-based feature extraction approach was also devised to extract buildings and trees from the above-ground segments based on object-level statistics derived, which were subject to a rule based classification system developed by either human experts or an inductive machine-learning algorithm. Case studies were conducted with four different LiDAR datasets to evaluate the effectiveness and efficiency of the proposed approaches. The proposed segmentation algorithm proved to be not only effective in separating ground and above-ground measurements into different segments, but also efficient in processing large datasets. The segmentation based filtering and object based feature extraction approaches have also demonstrated effectiveness in labeling the segments into ground and above-ground and in extracting buildings and trees from the above-ground segments. When incorporating spectral information from remote sensing imagery with the LiDAR data, the accuracy for feature extraction was further increased.