Choose an application

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

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

Choose an application

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

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

Choose an application

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

The Special Issue collects papers showing the progress made in key areas of photogrammetry and remote sensing such as modern and/or forthcoming sensors, improvements in data processing strategies and assessment of their reliability, application of innovations as proof of the contribution offered in the observation of the natural and built environment with better understanding of phenomena at required spatial scale.

VHR tri-stereo satellite imagery --- digital elevation model --- isolated objects --- dense image matching --- change detection --- natural disasters --- deep learning --- threshold selection --- optical flow estimation --- Structure from Motion (SfM) --- 3D reconstruction --- noise estimation --- point clouds --- roughness --- surface reconstruction --- mesh model --- visibility constraints --- volumetric methods --- dense point cloud --- multiple view stereo (MVS) --- dense image matching (DIM) --- photogrammetry --- computer vision --- Copernicus --- Sentinel-1 --- Sentinel-2 --- InSAR --- damage proxy map --- Beirut --- Lebanon --- explosion --- radiometric calibration --- modeling --- geometric error --- high-precision calibration --- n/a --- preprocessing --- enhancement --- point cloud --- image processing --- image histogram --- UAV --- camera calibration --- GNSS-assisted block orientation --- dome effect --- Monte Carlo simulation --- soil moisture content --- artificial neural network --- sample optimization --- synthetic aperture radar --- optical remote sensing image

Choose an application

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

The Special Issue collects papers showing the progress made in key areas of photogrammetry and remote sensing such as modern and/or forthcoming sensors, improvements in data processing strategies and assessment of their reliability, application of innovations as proof of the contribution offered in the observation of the natural and built environment with better understanding of phenomena at required spatial scale.

Technology: general issues --- History of engineering & technology --- VHR tri-stereo satellite imagery --- digital elevation model --- isolated objects --- dense image matching --- change detection --- natural disasters --- deep learning --- threshold selection --- optical flow estimation --- Structure from Motion (SfM) --- 3D reconstruction --- noise estimation --- point clouds --- roughness --- surface reconstruction --- mesh model --- visibility constraints --- volumetric methods --- dense point cloud --- multiple view stereo (MVS) --- dense image matching (DIM) --- photogrammetry --- computer vision --- Copernicus --- Sentinel-1 --- Sentinel-2 --- InSAR --- damage proxy map --- Beirut --- Lebanon --- explosion --- radiometric calibration --- modeling --- geometric error --- high-precision calibration --- preprocessing --- enhancement --- point cloud --- image processing --- image histogram --- UAV --- camera calibration --- GNSS-assisted block orientation --- dome effect --- Monte Carlo simulation --- soil moisture content --- artificial neural network --- sample optimization --- synthetic aperture radar --- optical remote sensing image

Choose an application

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

Remote sensing is being actively researched in the fields of environment, military and urban planning through technologies such as monitoring of natural climate phenomena on the earth, land cover classification, and object detection. Recently, satellites equipped with observation cameras of various resolutions were launched, and remote sensing images are acquired by various observation methods including cluster satellites. However, the atmospheric and environmental conditions present in the observed scene degrade the quality of images or interrupt the capture of the Earth's surface information. One method to overcome this is by generating synthetic images through image simulation. Synthetic images can be generated by using statistical or knowledge-based models or by using spectral and optic-based models to create a simulated image in place of the unobtained image at a required time. Various proposed methodologies will provide economical utility in the generation of image learning materials and time series data through image simulation. The 6 published articles cover various topics and applications central to Remote sensing image simulation. Although submission to this Special Issue is now closed, the need for further in-depth research and development related to image simulation of High-spatial and spectral resolution, sensor fusion and colorization remains.I would like to take this opportunity to express my most profound appreciation to the MDPI Book staff, the editorial team of Applied Sciences journal, especially Ms. Nimo Lang, the assistant editor of this Special Issue, talented authors, and professional reviewers.

Technology: general issues --- History of engineering & technology --- image fusion --- random forest regression --- SAR image --- panchromatic image --- high-resolution --- multi-beam LiDAR --- in situ self-calibration --- mobile mapping system --- 3D point cloud --- backpack-based mapping --- aerial orthoimage --- Sentinel-2 --- super-resolution --- image simulation --- residual U-Net --- interferometry --- remote sensing --- computational simulation --- denoising --- detection --- SAR imagery --- fusing region proposals --- KOMPSAT-3A --- strip --- sensor modeling --- RPCs --- mosaic --- matching --- discrepancy