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Combining photogrammetric and bathymetric data to build a 3D model of a canal tunnel / Emmanuel Moisan in Photogrammetric record, Vol 36 n° 175 (September 2021)  

Public [article]  inPhotogrammetric record > Vol 36 n° 175 (September 2021) . - pp 202 - 223 Titre : Combining photogrammetric and bathymetric data to build a 3D model of a canal tunnel Type de document : Article/Communication Auteurs : Emmanuel Moisan, Auteur ; Christophe Heinkelé, Auteur ; Philippe Foucher, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 202 - 223 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes IGN] bathymétrie [Termes IGN] canalisation [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] données TLS (télémétrie) [Termes IGN] étalonnage géométrique [Termes IGN] instrument embarqué [Termes IGN] modélisation 3D [Termes IGN] Moselle (57) [Termes IGN] reconstruction 3D [Termes IGN] semis de points [Termes IGN] sonar [Termes IGN] sondeur multifaisceaux [Termes IGN] système de numérisation mobile [Termes IGN] tunnel Résumé : (auteur) This paper introduces an original method for modelling in 3D the full tube (both vault and canal) of navigable tunnels using data acquired dynamically from a boat. The recording system is composed of cameras that provide images of the vault and a multibeam echo sounder that acquires 3D profiles underwater. Reconstructing partially submerged structures, in a confined environment where no global positioning system signal is available, is challenging. The method exploits the capabilities of photogrammetry, not only to reconstruct the tunnel vault, but also to estimate the trajectory of the vessel, which is necessary to rearrange sonar profiles and form the 3D model of the canal. The comparison of a model reconstructed from in situ dynamic acquisitions with a reference one, obtained from static laser and sonar acquisitions, shows that the accuracy is of the order of a centimetre for the vault, while it is decimetric for underwater features. Numéro de notice : A2021-690 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1111/phor.12379 date de publication en ligne : 02/09/2021 En ligne : https://doi.org/10.1111/phor.12379 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98483 [article]

Utilisation de l'apprentissage profond dans la modélisation 3D urbaine [Partie 1] / Hamza Ben Addou in Géomatique expert, n° 135 (septembre 2021)

Public [article]  inGéomatique expert > n° 135 (septembre 2021) . - pp 11 - 20 Titre : Utilisation de l'apprentissage profond dans la modélisation 3D urbaine [Partie 1] Type de document : Article/Communication Auteurs : Hamza Ben Addou, Auteur Année de publication : 2021 Article en page(s) : pp 11 - 20 Langues : Français (fre) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] apprentissage profond [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] détection du bâti [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] emprise au sol [Termes IGN] fusion de données multisource [Termes IGN] image aérienne [Termes IGN] information sémantique [Termes IGN] modèle 3D de l'espace urbain [Termes IGN] segmentation d'image [Termes IGN] semis de points Résumé : (Auteur) Partie I : Mise en place d’un processus de détection automatique des emprises de bâtiments par apprentissage profond Numéro de notice : A2021-660 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE/URBANISME Nature : Article nature-HAL : ArtSansCL DOI : sans date de publication en ligne : 01/09/2021 Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98414 [article]

Three-dimensional building change detection using object-based image analysis (case study: Tehran) / Fatemeh Tabib Mahmoudi in Applied geomatics, vol 13 n° 3 (September 2021)  

Public [article]  inApplied geomatics > vol 13 n° 3 (September 2021) . - pp 325 - 332 Titre : Three-dimensional building change detection using object-based image analysis (case study: Tehran) Type de document : Article/Communication Auteurs : Fatemeh Tabib Mahmoudi, Auteur ; Sharareh Hosseini, Auteur Année de publication : 2021 Article en page(s) : pp 325 - 332 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Photogrammétrie numérique [Termes IGN] analyse d'image orientée objet [Termes IGN] analyse diachronique [Termes IGN] Bâti-3D [Termes IGN] détection de changement [Termes IGN] détection du bâti [Termes IGN] extraction de traits caractéristiques [Termes IGN] gestion urbaine [Termes IGN] hauteur du bâti [Termes IGN] Matlab [Termes IGN] modèle 3D de l'espace urbain [Termes IGN] modèle numérique de surface [Termes IGN] segmentation d'image [Termes IGN] Téhéran Résumé : (auteur) Natural disasters such as earthquakes and floods together with the urban sprawl conducted by increasing the population make multi-temporal changes in building areas. Destruction, buildings’ renovation, and constructing new buildings are the main changes of the urban areas that should be detected to update three-dimensional city models. The results of performing three-dimensional changes detecting of high altitude objects such as buildings are more close to reality than the two-dimensional methods. In this study, a three-dimensional changes detection method is proposed based on digital elevation models (DEMs). In the first step of this proposed method, the normalized digital surface model (nDSM) is generated for timely datasets. Then, object-based image analysis is utilized by performing segmentation followed by the structural classification of DEMs. Differencing and comparing the multi-temporal classification maps as the third step of the proposed algorithm led to analyzing the occurred changes. The obtained results are evaluated in an urban area in Tehran, Iran, in a 9-year time interval. These results represent −9.7% decreasing rate in low-rise buildings and also −1.37% decreases in the ground. Moreover, the class of high-rise buildings increased for +16.4% which conforms to making new constructions in addition to the renovation of low-rise buildings. According to the area analyzing of the changes, 4.8% of the investigated study area has new constructions, 3.05% has buildings’ renovation, and 3.89% has destruction in that 9-year period. Numéro de notice : A2021-623 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1007/s12518-020-00349-w date de publication en ligne : 07/01/2021 En ligne : https://doi.org/10.1007/s12518-020-00349-w Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98247 [article]

Research on 3D model reconstruction based on a sequence of cross-sectional images / Zhiguo Dong in Machine Vision and Applications, vol 32 n°4 (July 2021)  

Public [article]  inMachine Vision and Applications > vol 32 n°4 (July 2021) . - n° 92 Titre : Research on 3D model reconstruction based on a sequence of cross-sectional images Type de document : Article/Communication Auteurs : Zhiguo Dong, Auteur ; Xiaobo Wu, Auteur ; Zhipeng Ma, Auteur Année de publication : 2021 Article en page(s) : n° 92 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Photogrammétrie numérique [Termes IGN] analyse infrapixellaire [Termes IGN] B-Spline [Termes IGN] détection de contours [Termes IGN] modélisation 3D [Termes IGN] reconstruction 3D [Termes IGN] reconstruction d'objet [Termes IGN] semis de points Résumé : (auteur) It is often difficult to obtain the high-precision inner cavity contour size and 3D model of parts and components in reverse engineering. This paper proposes a method that uses a sequence of section images of a part to reconstruct their 3D models. This method cuts the part layer by layer to obtain the sectional images and extracts the 3D information of the sectional image contours to generate point clouds. These point clouds are then used to reconstruct a 3D model of the part. High contrast material is used to embed the target part for pre-processing. A machining centre was used to mill the part layer by layer vertically to acquire high precision section profile images. The improved Canny edge detection operator was combined with the spatial moment sub-pixel subdivision algorithm to improve the edge detection accuracy. The camera imaging model algorithm transforms the coordinates of the image edge position to obtain a high-precision 3D point cloud of the part. The 3D solid model of the target part was obtained using NURBS surface reconstruction. The results show that the 3D model reconstruction method using the profile sequence of the cross-sectional images is independent of the complexity of the part’s structure and the complete internal structure of the part can be obtained. The proposed edge detection algorithm significantly refines the edge position of the contours in the cross-sectional image and the measurement accuracy was improved. This method improves the minimum deviation to 50 μm. The shape accuracy of roundness, cylindricity and perpendicularity of the structure is high. The proposed method can meet the reverse precision requirements in general precision machining. Numéro de notice : A2021-635 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s00138-021-01220-7 date de publication en ligne : 11/06/2021 En ligne : https://doi.org/10.1007/s00138-021-01220-7 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98299 [article]

Sensitivity of voxel-based estimations of leaf area density with terrestrial LiDAR to vegetation structure and sampling limitations: A simulation experiment / Maxime Soma in Remote sensing of environment, vol 257 (May 2021)  

Public [article]  inRemote sensing of environment > vol 257 (May 2021) . - n° 112354 Titre : Sensitivity of voxel-based estimations of leaf area density with terrestrial LiDAR to vegetation structure and sampling limitations: A simulation experiment Type de document : Article/Communication Auteurs : Maxime Soma, Auteur ; François Pimont, Auteur ; Jean-Luc Dupuy, Auteur Année de publication : 2021 Article en page(s) : n° 112354 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] analyse de sensibilité [Termes IGN] densité du feuillage [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] échantillonnage [Termes IGN] Leaf Area Index [Termes IGN] Leaf Mass per Area [Termes IGN] semis de points [Termes IGN] structure de la végétation [Termes IGN] voxel Résumé : (auteur) The need for fine scale description of vegetation structure is increasing as Leaf Area Density (LAD, m2/m3) becomes a critical parameter to understand ecosystem functioning and energy and mass fluxes in heterogeneous ecosystems. Terrestrial Laser Scanning (TLS) has shown great potential for retrieving the foliage area at stand, plant or voxel scales. Several sources of measurement errors have been identified and corrected over the past years. However, measurements remain sensitive to several factors, including, 1) voxel size and vegetation structure within voxels, 2) heterogeneity in sampling from TLS instrument (occlusion and shooting pattern), the consequences of which have been seldom analyzed at the scale of forest plots. In the present paper, we aimed at disentangling biases and errors in plot-scale measurements of LAD with TLS in a simulated vegetation scene. Two negative biases were formerly attributed to (i) the unsampled voxels and to (ii) the subgrid vegetation heterogeneity (i.e. clumping effect), and then quantified, thanks to a the simulation experiment providing known LAD references at voxel scale, vegetation manipulations and unbiased point estimators. We used confidence intervals to evaluate voxel-scale measurement accuracy. Numéro de notice : A2021-278 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1016/j.rse.2021.112354 date de publication en ligne : 18/02/2021 En ligne : https://doi.org/10.1016/j.rse.2021.112354 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97371 [article]

Structure-aware completion of photogrammetric meshes in urban road environment / Qing Zhu in ISPRS Journal of photogrammetry and remote sensing, vol 175 (May 2021)  

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A skyline-based approach for mobile augmented reality / Mehdi Ayadi in The Visual Computer, vol 37 n° 4 (April 2021)  

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Visual positioning in indoor environments using RGB-D images and improved vector of local aggregated descriptors / Longyu Zhang in ISPRS International journal of geo-information, vol 10 n° 4 (April 2021)  

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Automated registration of SfM‐MVS multitemporal datasets using terrestrial and oblique aerial images / Luigi Parente in Photogrammetric record, vol 36 n° 173 (March 2021)  

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Development of German-Ukrainian cooperations for education and research in photogrammetry and laser scanning / Thomas Luhmann in Geo-spatial Information Science, vol 24 n° 1 (March 2021)  

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Dynamic human body reconstruction and motion tracking with low-cost depth cameras / Kangkan Wang in The Visual Computer, vol 37 n° 3 (March 2021)  

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Progressive TIN densification with connection analysis for urban Lidar data / Tao Wang in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 3 (March 2021)  

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Spatial multi-criteria evaluation in 3D context: suitability analysis of urban vertical development / Kendra Munn in Cartography and Geographic Information Science, vol 48 n° 2 (March 2021)  

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Improving trajectory estimation using 3D city models and kinematic point clouds / Lucas Lucks in Transactions in GIS, Vol 25 n° 1 (February 2021)  

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Accurate assessment of protected area boundaries for land use planning using 3D GIS / Dilek Tezel in Geocarto international, vol 36 n° 1 ([01/01/2021])  

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