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Image processing, computer-assisted --- Vehicle detection --- Vehicular traffic control --- Image processing, computer-assisted --- Vehicle detection --- Vehicular traffic control

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Mobile mapping is applied widely in society, for example, in asset management, fleet management, construction planning, road safety, and maintenance optimization. Yet, further advances in these technologies are called for. Advances can be radical, such as changes to the prevailing paradigms in mobile mapping, or incremental, such as the state-of-the-art mobile mapping methods. With current multi-sensor systems in mobile mapping, laser-scanned data are often registered in point clouds with the aid of global navigation satellite system (GNSS) positioning or simultaneous localization and mapping (SLAM) techniques and then labeled and colored with the aid of machine learning methods and digital camera data. These multi-sensor platforms are beginning to undergo further advancements via the addition of multi-spectral and other sensors and via the development of machine learning techniques used in processing this multi-modal data. Embedded systems and minimalistic system designs are also attracting attention, from both academic and commercial perspectives.This book contains the accepted publications of the Special Issue 'Advances in Mobile Mapping Technologies' of the Remote Sensing journal. It consists of works introducing a new mobile mapping dataset (‘Paris CARLA 3D’), system calibration studies, SLAM topics, and multiple deep learning works for asset detection. We, the Guest Editors, Ville Lehtola from University of Twente, Netherlands, Andreas Nüchter from University of Würzburg, Germany, and François Goulette from Mines Paris- PSL University, France, wish to thank all the authors who contributed to this collection.

Technology: general issues --- History of engineering & technology --- LiDAR --- RetinaNet --- inception --- Mobile Laser Scanning --- point clouds --- data fusion --- Lidar --- point cloud density --- point cloud coverage --- mobile mapping systems --- 3D simulation --- Pandar64 --- Ouster OS-1-64 --- mobile laser scanning --- lever arm --- boresight angles --- plane-based calibration field --- configuration analysis --- accuracy --- controllability --- evaluation --- control points --- TLS reference point clouds --- visual–inertial odometry --- Helmert variance component estimation --- line feature matching method --- correlation coefficient --- point and line features --- mobile mapping --- manhole cover --- point cloud --- F-CNN --- transfer learning --- CAM localization --- loop closure detection --- visual SLAM --- semantic topology graph --- graph matching --- CNN features --- deep learning --- view planning --- imaging network design --- building 3D modelling --- path planning --- V-SLAM --- real-time --- guidance --- embedded-systems --- 3D surveying --- exposure control --- photogrammetry --- parking statistics --- vehicle detection --- robot operating system --- 3D camera --- RGB-D --- performance evaluation --- convolutional neural networks --- smart city --- georeferencing --- MSS --- IEKF --- DSIEKF --- geometrical constraints --- 6-DoF --- DTM --- 3D city model --- dataset --- laser scanning --- 3D mapping --- synthetic --- outdoor --- semantic --- scene completion

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Mobile mapping is applied widely in society, for example, in asset management, fleet management, construction planning, road safety, and maintenance optimization. Yet, further advances in these technologies are called for. Advances can be radical, such as changes to the prevailing paradigms in mobile mapping, or incremental, such as the state-of-the-art mobile mapping methods. With current multi-sensor systems in mobile mapping, laser-scanned data are often registered in point clouds with the aid of global navigation satellite system (GNSS) positioning or simultaneous localization and mapping (SLAM) techniques and then labeled and colored with the aid of machine learning methods and digital camera data. These multi-sensor platforms are beginning to undergo further advancements via the addition of multi-spectral and other sensors and via the development of machine learning techniques used in processing this multi-modal data. Embedded systems and minimalistic system designs are also attracting attention, from both academic and commercial perspectives.This book contains the accepted publications of the Special Issue 'Advances in Mobile Mapping Technologies' of the Remote Sensing journal. It consists of works introducing a new mobile mapping dataset (‘Paris CARLA 3D’), system calibration studies, SLAM topics, and multiple deep learning works for asset detection. We, the Guest Editors, Ville Lehtola from University of Twente, Netherlands, Andreas Nüchter from University of Würzburg, Germany, and François Goulette from Mines Paris- PSL University, France, wish to thank all the authors who contributed to this collection.

Technology: general issues --- History of engineering & technology --- LiDAR --- RetinaNet --- inception --- Mobile Laser Scanning --- point clouds --- data fusion --- Lidar --- point cloud density --- point cloud coverage --- mobile mapping systems --- 3D simulation --- Pandar64 --- Ouster OS-1-64 --- mobile laser scanning --- lever arm --- boresight angles --- plane-based calibration field --- configuration analysis --- accuracy --- controllability --- evaluation --- control points --- TLS reference point clouds --- visual–inertial odometry --- Helmert variance component estimation --- line feature matching method --- correlation coefficient --- point and line features --- mobile mapping --- manhole cover --- point cloud --- F-CNN --- transfer learning --- CAM localization --- loop closure detection --- visual SLAM --- semantic topology graph --- graph matching --- CNN features --- deep learning --- view planning --- imaging network design --- building 3D modelling --- path planning --- V-SLAM --- real-time --- guidance --- embedded-systems --- 3D surveying --- exposure control --- photogrammetry --- parking statistics --- vehicle detection --- robot operating system --- 3D camera --- RGB-D --- performance evaluation --- convolutional neural networks --- smart city --- georeferencing --- MSS --- IEKF --- DSIEKF --- geometrical constraints --- 6-DoF --- DTM --- 3D city model --- dataset --- laser scanning --- 3D mapping --- synthetic --- outdoor --- semantic --- scene completion --- LiDAR --- RetinaNet --- inception --- Mobile Laser Scanning --- point clouds --- data fusion --- Lidar --- point cloud density --- point cloud coverage --- mobile mapping systems --- 3D simulation --- Pandar64 --- Ouster OS-1-64 --- mobile laser scanning --- lever arm --- boresight angles --- plane-based calibration field --- configuration analysis --- accuracy --- controllability --- evaluation --- control points --- TLS reference point clouds --- visual–inertial odometry --- Helmert variance component estimation --- line feature matching method --- correlation coefficient --- point and line features --- mobile mapping --- manhole cover --- point cloud --- F-CNN --- transfer learning --- CAM localization --- loop closure detection --- visual SLAM --- semantic topology graph --- graph matching --- CNN features --- deep learning --- view planning --- imaging network design --- building 3D modelling --- path planning --- V-SLAM --- real-time --- guidance --- embedded-systems --- 3D surveying --- exposure control --- photogrammetry --- parking statistics --- vehicle detection --- robot operating system --- 3D camera --- RGB-D --- performance evaluation --- convolutional neural networks --- smart city --- georeferencing --- MSS --- IEKF --- DSIEKF --- geometrical constraints --- 6-DoF --- DTM --- 3D city model --- dataset --- laser scanning --- 3D mapping --- synthetic --- outdoor --- semantic --- scene completion

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The recent years have witnessed tremendous growth in connected vehicles due to major interest in vehicular ad hoc networks (VANET) technology from both the research and industrial communities. VANET involves the generation of data from onboard sensors and its dissemination in other vehicles via vehicle-to-everything (V2X) communication, thus resulting in numerous applications such as steep-curve warnings. However, to increase the scope of applications, VANET has to integrate various technologies including sensor networks, which results in a new paradigm commonly referred to as vehicular sensor networks (VSN). Unlike traditional sensor networks, every node (vehicle) in VSN is equipped with various sensing (distance sensors, GPS, and cameras), storage, and communication capabilities, which can provide a wide range of applications including environmental surveillance and traffic monitoring. VSN has the potential to improve transportation technology and the transportation environment due to its unlimited power supply and resulting minimum energy constraints. However, VSN faces numerous challenges in terms of its design, implementation, network scalability, reliability, and deployment over large-scale networks, which need to be addressed before it is realized. This book comprises 12 outstanding research works related to vehicular sensor networks, addressing various aspects such as security, routing, SDN, and NDN.

Information technology industries --- barrier control --- sensors platform --- vehicle detection --- license plate recognition --- raspberry-pi --- features extraction --- machine learning algorithms --- connected vehicles, internet of vehicles --- security --- IoT --- blockchain --- vehicular ad-hoc network --- wireless sensor networks --- wake-up radio --- medium access control protocol --- receiver-initiated MAC protocol --- traffic adaptation --- software-defined vehicular network --- vehicle-to-everything (V2X) --- modeling and implementation --- software defined network --- information-centric networking (ICN) --- client-cache (CC) --- video on demand (VoD) --- vehicular sensor network (VSN) --- smart city --- delay tolerant network --- infrastructure offloading --- opportunistic network --- vehicular mobility --- energy consumption --- carbon emission --- V2V communication --- message contents plausibility --- power control --- vehicle edge computing --- 5G cellular networks --- multi-receiver signcryption --- privacy --- PSO --- genetic algorithm --- ITS --- UAV --- simulation --- dynamic positioning --- 3D placement --- vehicular communications --- cross-validation --- anti-collaborative attack --- resource-saving --- trust computing --- Caching --- Named Data Networking --- Information Centric Networking --- Vehicular Ad Hoc Networks --- 5G --- D2D communication --- vehicle-to-vehicle communication --- mode selection --- vehicular social network --- vehicular sensor networks (VSN) --- vehicular ad-hoc networks (VANET) --- privacy and trust --- cyber security --- multimedia and cellular communication --- emerging IoT applications in VANET and VSN --- blockchain within VANET and VSN --- barrier control --- sensors platform --- vehicle detection --- license plate recognition --- raspberry-pi --- features extraction --- machine learning algorithms --- connected vehicles, internet of vehicles --- security --- IoT --- blockchain --- vehicular ad-hoc network --- wireless sensor networks --- wake-up radio --- medium access control protocol --- receiver-initiated MAC protocol --- traffic adaptation --- software-defined vehicular network --- vehicle-to-everything (V2X) --- modeling and implementation --- software defined network --- information-centric networking (ICN) --- client-cache (CC) --- video on demand (VoD) --- vehicular sensor network (VSN) --- smart city --- delay tolerant network --- infrastructure offloading --- opportunistic network --- vehicular mobility --- energy consumption --- carbon emission --- V2V communication --- message contents plausibility --- power control --- vehicle edge computing --- 5G cellular networks --- multi-receiver signcryption --- privacy --- PSO --- genetic algorithm --- ITS --- UAV --- simulation --- dynamic positioning --- 3D placement --- vehicular communications --- cross-validation --- anti-collaborative attack --- resource-saving --- trust computing --- Caching --- Named Data Networking --- Information Centric Networking --- Vehicular Ad Hoc Networks --- 5G --- D2D communication --- vehicle-to-vehicle communication --- mode selection --- vehicular social network --- vehicular sensor networks (VSN) --- vehicular ad-hoc networks (VANET) --- privacy and trust --- cyber security --- multimedia and cellular communication --- emerging IoT applications in VANET and VSN --- blockchain within VANET and VSN

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Recent years have seen a vast development in various methodologies for object detection and feature extraction and recognition, both in theory and in practice. When processing images, videos, or other types of multimedia, one needs efficient solutions to perform fast and reliable processing. Computational intelligence is used for medical screening where the detection of disease symptoms is carried out, in prevention monitoring to detect suspicious behavior, in agriculture systems to help with growing plants and animal breeding, in transportation systems for the control of incoming and outgoing transportation, for unmanned vehicles to detect obstacles and avoid collisions, in optics and materials for the detection of surface damage, etc. In many cases, we use developed techniques which help us to recognize some special features. In the context of this innovative research on computational intelligence, the Special Issue “Advanced Computational Intelligence for Object Detection, Feature Extraction and Recognition in Smart Sensor Environments” present an excellent opportunity for the dissemination of recent results and achievements for further innovations and development. It is my pleasure to present this collection of excellent contributions to the research community. - Prof. Marcin Woźniak, Silesian University of Technology, Poland –

Information technology industries --- Traffic sign detection and tracking (TSDR) --- advanced driver assistance system (ADAS) --- computer vision --- 3D convolutional neural networks --- machine learning --- CT brain --- brain hemorrhage --- visual inspection --- one-class classifier --- grow-when-required neural network --- evolving connectionist systems --- automatic design --- bio-inspired techniques --- artificial bee colony --- image analysis --- feature extraction --- ship classification --- marine systems --- citrus --- pests and diseases identification --- convolutional neural network --- parameter efficiency --- vehicle detection --- YOLOv2 --- focal loss --- anchor box --- multi-scale --- deep learning --- neural network --- generative adversarial network --- synthetic images --- tool wear monitoring --- superalloy tool --- image recognition --- object detection --- UAV imagery --- vehicular traffic flow detection --- vehicular traffic flow classification --- vehicular traffic congestion --- video classification --- benchmark --- semantic segmentation --- atrous convolution --- spatial pooling --- ship radiated noise --- underwater acoustics --- surface electromyography (sEMG) --- convolution neural networks (CNNs) --- hand gesture recognition --- fabric defect --- mixed kernels --- cross-scale --- cascaded center-ness --- deformable localization --- continuous casting --- surface defects --- 3D imaging --- defect detection --- object detector --- object tracking --- activity measure --- Yolo --- deep sort --- Hungarian algorithm --- optical flows --- spatiotemporal interest points --- sports scene --- CT images --- convolutional neural networks --- hepatic cancer --- visual question answering --- three-dimensional (3D) vision --- reinforcement learning --- human–robot interaction --- few shot learning --- SVM --- CNN --- cascade classifier --- video surveillance --- RFI --- artefacts --- InSAR --- image processing --- pixel convolution --- thresholding --- nearest neighbor filtering --- data acquisition --- augmented reality --- pose estimation --- industrial environments --- information retriever sensor --- multi-hop reasoning --- evidence chains --- complex search request --- high-speed trains --- hunting --- non-stationary --- feature fusion --- multi-sensor fusion --- unmanned aerial vehicles --- drone detection --- UAV detection --- visual detection --- n/a --- human-robot interaction