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Reconstitution de l'atmosphère dans la Tombe de Sennefer (TT96) avant la condamnation de l’hypèthre en créant une maquette virtuelle grâce à différents logiciels (Context Capture, Point Tools Sketchup, V-Ray, Blender...),à partir des données collectées sur place (photos, Laser scanning, dessins...). Reconstitution of the atmosphere in the Tomb of Sennefer (TT96) before the condemnation of the hypethrum(transom) by creating a virtual model with different softwares (Context Capture, Point Tools Sketchup, V-Ray, Blender ...), from the data collected on site (photos, Laser scanning, scketches...).

Tombe égyptienne Sennefer --- TT 96 --- nuage de points --- maillage --- reconstitution atmosphère --- lumière --- hypèthre --- hypogée --- modélisation 3D --- Laser scanning --- Tomb of Sennefer --- TT96 --- Cloud points --- mesh --- atmposhere reconstitution --- Laser scanning --- light --- transom --- hypogeum --- 3D modeling --- Ingénierie, informatique & technologie > Architecture

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The book presents a method to create a land cover map from lidar data (elevation, intensity and standard deviation of height). The intensity of the observed object depends on many factors and is therefore not easy to interpret. With careful study of the problem and implementing various improvements we managed to distinctively classify categories of grass, agricultural fields and asphalt. We used standard deviation of height to differentiate trees and buildings, because this was not possible using intensity data alone. On the basis of independent lidar data a high quality three dimensional land cover map of local area has been successfully generated. It distinguishes five basic categories, although more detailed sub-categories could be introduced if necessary. Once the methodology is determined, products can be generated fully automatically, with minimal effort and costs. The resulting maps are of very high positional and thematic accuracy with numerous advantages for local studies. Knjiga opisuje postopek izdelave karte pokrovnosti iz podatkov lidarskega snemanja, kot so višina, intenziteta in standardni odklon višine. Intenziteta opazovanega objekta je odvisna od vrste dejavnikov in zato težavna za interpretacijo. Po njeni preučitvi in nekaterih izboljšavah smo zelo dobro razločili kategorije pokrovnosti trava, njive in asfalt, za razločitev dreves in stavb pa smo morali uporabiti še standardni odklon višin. Na osnovi samostojnih lidarskih podatkov smo uspeli izdelati kakovostno karto pokrovnosti krajevnega območja, ki loči pet osnovnih kategorij, po potrebi pa jih lahko ločimo tudi več. Ko je postopek izdelave karte znan, lahko karto izdelamo povsem samodejno, z minimalnim naporom in stroški. Pri tem dobimo karto zelo dobre položajne in tematske natančnosti, s številnimi prednostmi za krajevne študije.

Geographical information systems (GIS) & remote sensing --- aerial laser scanning --- coverage --- intensity --- land cover --- lidar --- remote sensing --- Slovenia --- daljinsko zaznavanje --- intenziteta --- pokrovnost --- pokrovnost tal --- Slovenija --- zračno lasersko skeniranje

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Le mobile laser scanning (MLS) est une méthode d'acquisition de nuages de points 3D qui permet une acquisition rapide et précise. Ce travail vise à développer une méthode de segmentation et de classification pour les nuages de points 3D relevés par MLS en environnement ferroviaire. La méthode se décompose en trois parties. La première est l'extraction du sol, des rails et des panneaux sur base des attributs du nuages. La seconde est le calcul de caractéristiques qui décrivent la structure du nuage. La dernière partie est la classification des éléments restants de l'infrastructure ferroviaire en utilisant le modèle de classification supervisée Random Forest. La méthode proposée obtient une exactitude globale de 99~\% avec un temps de traitement relativement rapide. Mobile laser scanning (MLS) is a 3D point cloud acquisition method that allows fast and accurate acquisition. This work aims to develop segmentation and classification method for 3D point clouds collected by MLS in a railway environment. The method consists of three parts. The first is the extraction of ground, rails and traffic signs based on cloud attributes. The second is the computation of features that describe the structure of the cloud. The last part is the classification of the remaining elements of the railway infrastructure using the Random Forest supervised classification model. The proposed method achieves an overall accuracy of 99~\% with a relatively fast processing time.

point cloud --- lidar --- mobile laser scanning --- segmentation --- classification --- Physique, chimie, mathématiques & sciences de la terre > Sciences de la terre & géographie physique

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Non-Intrusive Methodologies for Large Area Urban Research brings together contributions from a conference held in 2021 in association with the ERC-funded 'Rome Transformed' research project. The papers address a major challenge in archaeology. Non-intrusive research in pursuit of a deeper understanding of urban areas can be both richly informative and cost-effective. Multiple successes in the field have led to an impressive array of innovative methodologies, methodologies that are frequently combined for still greater insight and impact. Geophysical surveys, the use of UAVs, the study of exposed historic structures and the exhaustive examination of archival records can all play a vital role, and the development of these data capture methodologies is of the utmost importance for the future of research. As well as advances in data capture methodologies, however, the papers also present case studies in the management of the big data generated and in the integration of different methodologies. A further strength of this collection lies in the range of site types considered. While many projects have historically pursued non-intrusive research in areas relatively clear of modern buildings, a growing number of research initiatives, such as 'Rome Transformed' are seeking to advance them in areas which remain densely occupied. Accordingly the material presented here will also be of interest to non-archaeologists working in such diverse fields as civil engineering, urban planning and physical geography.

Social sciences --- Ground Penetrating Radar --- Archival Research --- Geophysics --- Electrical Resistance Tomography --- Big Data --- Metadata --- Laser-scanning --- Urban Archaeology --- Photogammetry --- Social Science / Archaeology --- Computers / Data Science / Data Analytics --- Science / Physics / Geophysics --- Archaeology. --- Physics. --- Geophysics. --- Computers. --- Analytics. --- Social sciences. --- Visual analytics.

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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