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Using multi-scale and hierarchical deep convolutional features for 3D semantic classification of TLS point clouds / Zhou Guo in International journal of geographical information science IJGIS, vol 34 n° 4 (April 2020)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 34 n° 4 (April 2020) . - pp 661 - 680 Titre : Using multi-scale and hierarchical deep convolutional features for 3D semantic classification of TLS point clouds Type de document : Article/Communication Auteurs : Zhou Guo, Auteur ; Chen-Chieh Feng, Auteur Année de publication : 2020 Article en page(s) : pp 661 - 680 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes descripteurs IGN] analyse multiéchelle [Termes descripteurs IGN] apprentissage profond [Termes descripteurs IGN] approche hiérarchique [Termes descripteurs IGN] classification par réseau neuronal convolutif [Termes descripteurs IGN] données lidar [Termes descripteurs IGN] modélisation 3D [Termes descripteurs IGN] Oakland (Californie) [Termes descripteurs IGN] régression [Termes descripteurs IGN] semis de points Résumé : (auteur) Point cloud classification, which provides meaningful semantic labels to the points in a point cloud, is essential for generating three-dimensional (3D) models. Its automation, however, remains challenging due to varying point densities and irregular point distributions. Adapting existing deep-learning approaches for two-dimensional (2D) image classification to point cloud classification is inefficient and results in the loss of information valuable for point cloud classification. In this article, a new approach that classifies point cloud directly in 3D is proposed. The approach uses multi-scale features generated by deep learning. It comprises three steps: (1) extract single-scale deep features using 3D convolutional neural network (CNN); (2) subsample the input point cloud at multiple scales, with the point cloud at each scale being an input to the 3D CNN, and combine deep features at multiple scales to form multi-scale and hierarchical features; and (3) retrieve the probabilities that each point belongs to the intended semantic category using a softmax regression classifier. The proposed approach was tested against two publicly available point cloud datasets to demonstrate its performance and compared to the results produced by other existing approaches. The experiment results achieved 96.89% overall accuracy on the Oakland dataset and 91.89% overall accuracy on the Europe dataset, which are the highest among the considered methods. Numéro de notice : A2020-109 Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2018.1552790 date de publication en ligne : 10/12/2018 En ligne : https://doi.org/10.1080/13658816.2018.1552790 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94711 [article]

Comparison and analysis of results of 3D modelling of complex cultural and historical objects using different types of terrestrial laser scanner / Admir Mulahusic in Survey review, vol 52 n° 371 (March 2020)  

Public [article]  inSurvey review > vol 52 n° 371 (March 2020) . - pp 107-114 Titre : Comparison and analysis of results of 3D modelling of complex cultural and historical objects using different types of terrestrial laser scanner Type de document : Article/Communication Auteurs : Admir Mulahusic, Auteur ; Nedim Tuno, Auteur ; Dubravko Gajski, Auteur ; Jusuf Topoljak, Auteur Année de publication : 2020 Article en page(s) : pp 107-114 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes descripteurs IGN] balayage laser [Termes descripteurs IGN] Croatie [Termes descripteurs IGN] impulsion laser [Termes descripteurs IGN] logiciel de reconstruction 3D [Termes descripteurs IGN] modélisation 3D [Termes descripteurs IGN] monument historique [Termes descripteurs IGN] patrimoine culturel [Termes descripteurs IGN] précision géométrique (imagerie) [Termes descripteurs IGN] télémétrie laser terrestre [Termes descripteurs IGN] traitement de données Résumé : (auteur) Laser scanning does not provide unlimited geometrical accuracy and integrity when scanning complex objects. Scanning systems have a minimum and maximum range in which they operate, depending on the technical characteristics. Scanning below or above these limits results in gross errors and registering of incorrect data. Laser scanners can have difficulties with certain materials such as marble and reflective surfaces. This paper presents the results of laser scanning of a complex monument of cultural and historical heritage using two different types of terrestrial laser scanners. Afterwards, the comparison and analysis of the results are shown. The scanners used were terrestrial laser scanners Faro Focus 3D (phase mode distance measurements) and STONEX X300 (pulse mode distance measurements). Numéro de notice : A2020-073 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/00396265.2018.1528758 date de publication en ligne : 09/10/2018 En ligne : https://doi.org/10.1080/00396265.2018.1528758 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94638 [article]

Automated extraction of lane markings from mobile LiDAR point clouds based on fuzzy inference / Heidar Rastiveis in ISPRS Journal of photogrammetry and remote sensing, vol 160 (February 2020)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 160 (February 2020) . - pp 149 - 166 Titre : Automated extraction of lane markings from mobile LiDAR point clouds based on fuzzy inference Type de document : Article/Communication Auteurs : Heidar Rastiveis, Auteur ; Alireza Shams, Auteur ; Wayne A. Sarasua, Auteur ; Jonathan Li, Auteur Année de publication : 2020 Article en page(s) : pp 149 - 166 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes descripteurs IGN] autoroute [Termes descripteurs IGN] données lidar [Termes descripteurs IGN] données localisées 3D [Termes descripteurs IGN] extraction automatique [Termes descripteurs IGN] extraction de points [Termes descripteurs IGN] extraction du réseau routier [Termes descripteurs IGN] Inférence floue [Termes descripteurs IGN] lidar mobile [Termes descripteurs IGN] modélisation 3D [Termes descripteurs IGN] semis de points [Termes descripteurs IGN] transformation de Hough Résumé : (Auteur) Mobile LiDAR systems (MLS) are rapid and accurate technologies for acquiring three-dimensional (3D) point clouds that can be used to generate 3D models of road environments. Because manual extraction of desirable features such as road traffic signs, trees, and pavement markings from these point clouds is tedious and time-consuming, automatic information extraction of these objects is desirable. This paper proposes a novel automatic method to extract pavement lane markings (LMs) using point attributes associated with the MLS point cloud based on fuzzy inference. The proposed method begins with dividing the MLS point cloud into a number of small sections (e.g. tiles) along the route. After initial filtering of non-ground points, each section is vertically aligned. Next, a number of candidate LM areas are detected using a Hough Transform (HT) algorithm and considering a buffer area around each line. The points inside each area are divided into “probable-LM” and “non-LM” clusters. After extracting geometric and radiometric descriptors for the “probable-LM” clusters and analyzing them in a fuzzy inference system, true-LM clusters are eventually detected. Finally, the extracted points are enhanced and transformed back to their original position. The efficiency of the method was tested on two different point cloud datasets along 15.6 km and 9.5 km roadway corridors. Comparing the LMs extracted using the algorithm with the manually extracted LMs, 88% of the LM lines were successfully extracted in both datasets. Numéro de notice : A2020-047 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2019.12.009 date de publication en ligne : 20/12/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.12.009 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94558 [article]

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Comparative usability of an augmented reality sandtable and 3D GIS for education / Antoni B. Moore in International journal of geographical information science IJGIS, vol 34 n° 2 (February 2020)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 34 n° 2 (February 2020) . - pp 229 - 250 Titre : Comparative usability of an augmented reality sandtable and 3D GIS for education Type de document : Article/Communication Auteurs : Antoni B. Moore, Auteur ; Benjamin Daniel, Auteur ; greg Leonard, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 229 - 250 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Systèmes d'information géographique [Termes descripteurs IGN] enseignement supérieur [Termes descripteurs IGN] hydrologie [Termes descripteurs IGN] modèle numérique de terrain [Termes descripteurs IGN] modélisation 3D [Termes descripteurs IGN] Nouvelle-Zélande [Termes descripteurs IGN] réalité augmentée [Termes descripteurs IGN] réalité de terrain [Termes descripteurs IGN] réalité virtuelle [Termes descripteurs IGN] sable [Termes descripteurs IGN] test de performance Résumé : (auteur) Augmented Reality (AR) sandtables facilitate the shaping of sand to form a surface that is transformed into a digital terrain map which is projected back onto the sand. Although a mature technology, there are still few instances of sandtables being used in surface analysis. Fundamentally there has not been any reported formal assessment of how well sandtables perform in an educational context compared to other conventional learning environments. We compared learning outcomes from using an AR sandtable versus a conventional 3D GIS to convey key concepts in terrain and hydrological analyses via usability and knowledge testing. Overall results from students at a research-intensive New Zealand university reveal a faster task performance and more learning satisfaction when using the sandtable to undertake experimental tasks. Effectiveness and knowledge quiz results revealed no significant difference between the technologies though there was a trend for more accurate answers with 3D GIS tasks. Student learning wise, the sandtable integrated core concepts (especially morphometry) more effectively though both technologies were otherwise similar. We conclude that sandtables have high potential in geospatial teaching, fostering accessible and engaging means of introducing terrain and hydrological concepts, prior to undertaking a more accurate and precise surface analysis with 3D GIS. Numéro de notice : A2020-028 Thématique : GEOMATIQUE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2019.1656810 date de publication en ligne : 27/08/2019 En ligne : https://doi.org/10.1080/13658816.2019.1656810 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94481 [article]

Tree annotations in LiDAR data using point densities and convolutional neural networks / Ananya Gupta in IEEE Transactions on geoscience and remote sensing, vol 58 n° 2 (February 2020)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 58 n° 2 (February 2020) . - pp 971 - 981 Titre : Tree annotations in LiDAR data using point densities and convolutional neural networks Type de document : Article/Communication Auteurs : Ananya Gupta, Auteur ; Jonathan Byrne, Auteur ; David Moloney, Auteur Année de publication : 2020 Article en page(s) : pp 971 - 981 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes descripteurs IGN] classification par réseau neuronal convolutif [Termes descripteurs IGN] données lidar [Termes descripteurs IGN] Dublin (Irlande ; ville) [Termes descripteurs IGN] étiquette [Termes descripteurs IGN] extraction d'arbres [Termes descripteurs IGN] image spectrale [Termes descripteurs IGN] Montréal (Québec) [Termes descripteurs IGN] segmentation [Termes descripteurs IGN] semis de points [Termes descripteurs IGN] voxel [Termes descripteurs IGN] zone urbaine Résumé : (auteur) LiDAR provides highly accurate 3-D point clouds. However, data need to be manually labeled in order to provide subsequent useful information. Manual annotation of such data is time-consuming, tedious, and error prone, and hence, in this article, we present three automatic methods for annotating trees in LiDAR data. The first method requires high-density point clouds and uses certain LiDAR data attributes for the purpose of tree identification, achieving almost 90% accuracy. The second method uses a voxel-based 3-D convolutional neural network on low-density LiDAR data sets and is able to identify most large trees accurately but struggles with smaller ones due to the voxelization process. The third method is a scaled version of the PointNet++ method and works directly on outdoor point clouds and achieves an F score of 82.1% on the ISPRS benchmark data set, comparable to the state-of-the-art methods but with increased efficiency. Numéro de notice : A2020-095 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2019.2942201 date de publication en ligne : 11/10/2019 En ligne : https://doi.org/10.1109/TGRS.2019.2942201 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94658 [article]

An indoor navigation model and its network extraction / Filippo Mortari in Applied geomatics, Vol 11 n° 4 (December 2019)  

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Application of photogrammetry to generate quantitative geobody data in ephemeral fluvial systems / Charlotte L. Priddy in Photogrammetric record, vol 34 n° 168 (December 2019)  

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Combining thermal imaging with photogrammetry of an active volcano using UAV: an example from Stromboli, Italy / Zoë E. Wakeford in Photogrammetric record, vol 34 n° 168 (December 2019)  

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Innovative techniques of photogrammetry for 3D modeling / Vicenzo Barrile in Applied geomatics, Vol 11 n° 4 (December 2019)  

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A learning approach to evaluate the quality of 3D city models / Oussama Ennafii in Photogrammetric Engineering & Remote Sensing, PERS, vol 85 n° 12 (December 2019)  

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A low‐cost open‐source workflow to generate georeferenced 3D SfM photogrammetric models of rocky outcrops / Laurent Froideval in Photogrammetric record, vol 34 n° 168 (December 2019)  

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Pré-localisation des données pour la modélisation 3D de tunnels : développements et évaluations / Christophe Heinkelé in Revue Française de Photogrammétrie et de Télédétection, n° 221 (novembre 2019)

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Multiple-view geospatial comparison using web-based virtual globes / Liangfeng Zhu in ISPRS Journal of photogrammetry and remote sensing, vol 156 (October 2019)  

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Enhanced 3D mapping with an RGB-D sensor via integration of depth measurements and image sequences / Bo Wu in Photogrammetric Engineering & Remote Sensing, PERS, vol 85 n° 9 (September 2019)  

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Analysis of free image-based modelling systems applied to support topographic measurements / José Miguel Caldera-Cordero in Survey review, vol 51 n° 367 (July 2019)  

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