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This book gives an overview of the usage of UAVs in environmental sciences covering technical basics, data acquisition with different sensors, data processing schemes and illustrating various examples of application.

Drone --- UAV --- unmanned aerial vehicle --- AUS --- unmanned aerial system --- RPAS --- photogrammetry --- remote sensing --- flight operations --- georeferencing --- point cloud --- Bildmessung --- Fernerkundung --- Cloudpoint --- Bildklassifikation --- Drone aircraft. --- Drones (Aircraft) --- Pilotless aircraft --- Remotely piloted aircraft --- UAVs (Unmanned aerial vehicles) --- Unmanned aerial vehicles --- Flying-machines --- Vehicles, Remotely piloted --- Airplanes --- Radio 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.

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 design of novel UAV systems and the use of UAV platforms integrated with robotic sensing and imaging techniques, as well as the development of processing workflows and the capacity of ultra-high temporal and spatial resolution data, have enabled a rapid uptake of UAVs and drones across several industries and application domains.This book provides a forum for high-quality peer-reviewed papers that broaden awareness and understanding of single- and multiple-UAV developments for remote sensing applications, and associated developments in sensor technology, data processing and communications, and UAV system design and sensing capabilities in GPS-enabled and, more broadly, Global Navigation Satellite System (GNSS)-enabled and GPS/GNSS-denied environments.Contributions include:UAV-based photogrammetry, laser scanning, multispectral imaging, hyperspectral imaging, and thermal imaging;UAV sensor applications; spatial ecology; pest detection; reef; forestry; volcanology; precision agriculture wildlife species tracking; search and rescue; target tracking; atmosphere monitoring; chemical, biological, and natural disaster phenomena; fire prevention, flood prevention; volcanic monitoring; pollution monitoring; microclimates; and land use;Wildlife and target detection and recognition from UAV imagery using deep learning and machine learning techniques;UAV-based change detection.

Technology: general issues --- UAV --- landing --- optical flow --- video navigation --- Kalman filter --- coastal mapping --- coastal monitoring --- Digital Elevation Models (DEMs) --- geomorphological evolution --- photogrammetry --- Structure-from-Motion (SfM) --- Unmanned Aerial Vehicles (UAVs) --- snow mapping --- UAS --- remote sensing --- direct georeferencing --- snow field --- snow-covered area --- snow depth --- water level changes --- UAV photogrammetry --- tidal phase --- GNSS --- Kilim River --- unmanned aerial vehicles --- UAV swarms --- visual detection --- visual tracking --- machine vision --- deep learning --- YOLO --- laser guidance --- emergency landing --- particle filter --- change detection --- convolutional neural networks --- moving camera --- image alignment --- multirotor --- ground effect --- sensor faults --- UAV imagery --- bundle block adjustment --- digital surface model --- orthomosaic --- data collection --- accuracy --- technical guidelines --- DSM assessment --- backpack mobile mapping --- underground cellars --- unmanned aerial vehicle --- unmanned aerial system --- vision-based navigation --- search and rescue --- vision and action --- OODA --- inspection --- target detection --- autonomous localization --- 3D registration --- GPS-denied environment --- real-time --- multi-robot --- bioinspired map --- topologic mapping --- map exploration --- onboard GNSS RTK --- UAS traffic management --- multiple UAV navigation --- navigation in GPS/GNSS-denied environments --- distributed state estimation --- consensus theory --- computer architecture --- decision making --- navigation --- semantics --- aerial systems --- applications, inspection robotics, bridge inspection with UAS --- POMDP --- Deep Reinforcement-Learning --- multi-agent --- search --- UAV --- landing --- optical flow --- video navigation --- Kalman filter --- coastal mapping --- coastal monitoring --- Digital Elevation Models (DEMs) --- geomorphological evolution --- photogrammetry --- Structure-from-Motion (SfM) --- Unmanned Aerial Vehicles (UAVs) --- snow mapping --- UAS --- remote sensing --- direct georeferencing --- snow field --- snow-covered area --- snow depth --- water level changes --- UAV photogrammetry --- tidal phase --- GNSS --- Kilim River --- unmanned aerial vehicles --- UAV swarms --- visual detection --- visual tracking --- machine vision --- deep learning --- YOLO --- laser guidance --- emergency landing --- particle filter --- change detection --- convolutional neural networks --- moving camera --- image alignment --- multirotor --- ground effect --- sensor faults --- UAV imagery --- bundle block adjustment --- digital surface model --- orthomosaic --- data collection --- accuracy --- technical guidelines --- DSM assessment --- backpack mobile mapping --- underground cellars --- unmanned aerial vehicle --- unmanned aerial system --- vision-based navigation --- search and rescue --- vision and action --- OODA --- inspection --- target detection --- autonomous localization --- 3D registration --- GPS-denied environment --- real-time --- multi-robot --- bioinspired map --- topologic mapping --- map exploration --- onboard GNSS RTK --- UAS traffic management --- multiple UAV navigation --- navigation in GPS/GNSS-denied environments --- distributed state estimation --- consensus theory --- computer architecture --- decision making --- navigation --- semantics --- aerial systems --- applications, inspection robotics, bridge inspection with UAS --- POMDP --- Deep Reinforcement-Learning --- multi-agent --- search

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

The design of novel UAV systems and the use of UAV platforms integrated with robotic sensing and imaging techniques, as well as the development of processing workflows and the capacity of ultra-high temporal and spatial resolution data, have enabled a rapid uptake of UAVs and drones across several industries and application domains.This book provides a forum for high-quality peer-reviewed papers that broaden awareness and understanding of single- and multiple-UAV developments for remote sensing applications, and associated developments in sensor technology, data processing and communications, and UAV system design and sensing capabilities in GPS-enabled and, more broadly, Global Navigation Satellite System (GNSS)-enabled and GPS/GNSS-denied environments.Contributions include:UAV-based photogrammetry, laser scanning, multispectral imaging, hyperspectral imaging, and thermal imaging;UAV sensor applications; spatial ecology; pest detection; reef; forestry; volcanology; precision agriculture wildlife species tracking; search and rescue; target tracking; atmosphere monitoring; chemical, biological, and natural disaster phenomena; fire prevention, flood prevention; volcanic monitoring; pollution monitoring; microclimates; and land use;Wildlife and target detection and recognition from UAV imagery using deep learning and machine learning techniques;UAV-based change detection.

UAV --- landing --- optical flow --- video navigation --- Kalman filter --- coastal mapping --- coastal monitoring --- Digital Elevation Models (DEMs) --- geomorphological evolution --- photogrammetry --- Structure-from-Motion (SfM) --- Unmanned Aerial Vehicles (UAVs) --- snow mapping --- UAS --- remote sensing --- direct georeferencing --- snow field --- snow-covered area --- snow depth --- water level changes --- UAV photogrammetry --- tidal phase --- GNSS --- Kilim River --- unmanned aerial vehicles --- UAV swarms --- visual detection --- visual tracking --- machine vision --- deep learning --- YOLO --- laser guidance --- emergency landing --- particle filter --- change detection --- convolutional neural networks --- moving camera --- image alignment --- multirotor --- ground effect --- sensor faults --- UAV imagery --- bundle block adjustment --- digital surface model --- orthomosaic --- data collection --- accuracy --- technical guidelines --- DSM assessment --- backpack mobile mapping --- underground cellars --- unmanned aerial vehicle --- unmanned aerial system --- vision-based navigation --- search and rescue --- vision and action --- OODA --- inspection --- target detection --- autonomous localization --- 3D registration --- GPS-denied environment --- real-time --- multi-robot --- bioinspired map --- topologic mapping --- map exploration --- onboard GNSS RTK --- UAS traffic management --- multiple UAV navigation --- navigation in GPS/GNSS-denied environments --- distributed state estimation --- consensus theory --- computer architecture --- decision making --- navigation --- semantics --- aerial systems --- applications, inspection robotics, bridge inspection with UAS --- POMDP --- Deep Reinforcement-Learning --- multi-agent --- search