Centre dedocumentation
scientifique

Pyramid scene parsing network in 3D: Improving semantic segmentation of point clouds with multi-scale contextual information / Hao Fang in ISPRS Journal of photogrammetry and remote sensing, vol 154 (August 2019)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 154 (August 2019) . - pp 246 - 258 Titre : Pyramid scene parsing network in 3D: Improving semantic segmentation of point clouds with multi-scale contextual information Type de document : Article/Communication Auteurs : Hao Fang, Auteur ; Florent Lafarge, Auteur Année de publication : 2019 Article en page(s) : pp 246 - 258 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes descripteurs IGN] apprentissage profond [Termes descripteurs IGN] compréhension de l'image [Termes descripteurs IGN] données localisées 3D [Termes descripteurs IGN] prise en compte du contexte [Termes descripteurs IGN] représentation multiple [Termes descripteurs IGN] scène [Termes descripteurs IGN] scène intérieure [Termes descripteurs IGN] segmentation sémantique [Termes descripteurs IGN] semis de points Résumé : (Auteur) Analyzing and extracting geometric features from 3D data is a fundamental step in 3D scene understanding. Recent works demonstrated that deep learning architectures can operate directly on raw point clouds, i.e. without the use of intermediate grid-like structures. These architectures are however not designed to encode contextual information in-between objects efficiently. Inspired by a global feature aggregation algorithm designed for images (Zhao et al., 2017), we propose a 3D pyramid module to enrich pointwise features with multi-scale contextual information. Our module can be easily coupled with 3D semantic segmentation methods operating on 3D point clouds. We evaluated our method on three large scale datasets with four baseline models. Experimental results show that the use of enriched features brings significant improvements to the semantic segmentation of indoor and outdoor scenes. Numéro de notice : A2019-271 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2019.06.010 date de publication en ligne : 01/07/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.06.010 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93089 [article]

Réservation

Réserver ce document

Exemplaires (3)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
081-2019081	RAB	Revue	Centre de documentation	En réserve 3L	Disponible
081-2019083	DEP-RECP	Revue	MATIS	Dépôt en unité	Exclu du prêt
081-2019082	DEP-RECF	Revue	Nancy	Dépôt en unité	Exclu du prêt

Semantic segmentation of road furniture in mobile laser scanning data / Fashuai Li in ISPRS Journal of photogrammetry and remote sensing, vol 154 (August 2019)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 154 (August 2019) . - pp 98 - 113 Titre : Semantic segmentation of road furniture in mobile laser scanning data Type de document : Article/Communication Auteurs : Fashuai Li, Auteur ; Matti Lehtomäki, Auteur ; Sander J. Oude Elberink, Auteur ; et al., Auteur Année de publication : 2019 Article en page(s) : pp 98 - 113 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes descripteurs IGN] classification bayesienne [Termes descripteurs IGN] classification par forêts aléatoires [Termes descripteurs IGN] classification par séparateurs à vaste marge [Termes descripteurs IGN] données lidar [Termes descripteurs IGN] données localisées 3D [Termes descripteurs IGN] mobilier urbain [Termes descripteurs IGN] segmentation sémantique Résumé : (Auteur) Road furniture recognition has become a prevalent issue in the past few years because of its great importance in smart cities and autonomous driving. Previous research has especially focussed on pole-like road furniture, such as traffic signs and lamp posts. Published methods have mainly classified road furniture as individual objects. However, most road furniture consists of a combination of classes, such as a traffic sign mounted on a street light pole. To tackle this problem, we propose a framework to interpret road furniture at a more detailed level. Instead of being interpreted as single objects, mobile laser scanning data of road furniture is decomposed in elements individually labelled as poles, and objects attached to them, such as, street lights, traffic signs and traffic lights. In our framework, we first detect road furniture from unorganised mobile laser scanning point clouds. Then detected road furniture is decomposed into poles and attachments (e.g. traffic signs). In the interpretation stage, we extract a set of features to classify the attachments by utilising a knowledge-driven method and four representative types of machine learning classifiers, which are random forest, support vector machine, Gaussian mixture model and naïve Bayes, to explore the optimal method. The designed features are the unary features of attachments and the spatial relations between poles and their attachments. Two experimental test sites in Enschede dataset and Saunalahti dataset were applied, and Saunalahti dataset was collected in two different epochs. In the experimental results, the random forest classifier outperforms the other methods, and the overall accuracy acquired is higher than 80% in Enschede test site and higher than 90% in both Saunalahti epochs. The designed features play an important role in the interpretation of road furniture. The results of two epochs in the same area prove the high reliability of our framework and demonstrate that our method achieves good transferability with an accuracy over 90% through employing the training data of one epoch to test the data in another epoch. Numéro de notice : A2019-266 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2019.06.001 date de publication en ligne : 08/06/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.06.001 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93081 [article]

Réservation

Réserver ce document

Exemplaires (3)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
081-2019081	RAB	Revue	Centre de documentation	En réserve 3L	Disponible
081-2019083	DEP-RECP	Revue	MATIS	Dépôt en unité	Exclu du prêt
081-2019082	DEP-RECF	Revue	Nancy	Dépôt en unité	Exclu du prêt

Comparison of three algorithms to estimate tree stem diameter from terrestrial laser scanner data / Joris Ravaglia in Forests, vol 10 n° 7 (July 2019)  

Public [article]  inForests > vol 10 n° 7 (July 2019) . - 19 p. Titre : Comparison of three algorithms to estimate tree stem diameter from terrestrial laser scanner data Type de document : Article/Communication Auteurs : Joris Ravaglia, Auteur ; Richard A. Fournier, Auteur ; Alexandra Bac, Auteur ; Cédric Vega , Auteur ; Jean-François Côté, Auteur ; Alexandre Piboule, Auteur ; Ulysse Rémillard, Auteur Année de publication : 2019 Projets : ARBRE / AgroParisTech Article en page(s) : 19 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes descripteurs IGN] analyse comparative [Termes descripteurs IGN] Canada [Termes descripteurs IGN] diamètre à hauteur de poitrine [Termes descripteurs IGN] diamètre des arbres [Termes descripteurs IGN] données lidar [Termes descripteurs IGN] données localisées 3D [Termes descripteurs IGN] feuillu [Termes descripteurs IGN] France (administrative) [Termes descripteurs IGN] inventaire forestier (techniques et méthodes) [Termes descripteurs IGN] pinophyta [Termes descripteurs IGN] semis de points [Termes descripteurs IGN] transformation de Hough [Termes descripteurs IGN] volume en bois Résumé : (auteur) Terrestrial laser scanners provide accurate and detailed point clouds of forest plots, which can be used as an alternative to destructive measurements during forest inventories. Various specialized algorithms have been developed to provide automatic and objective estimates of forest attributes from point clouds. The STEP (Snakes for Tuboid Extraction from Point cloud) algorithm was developed to estimate both stem diameter at breast height and stem diameters along the bole length. Here, we evaluate the accuracy of this algorithm and compare its performance with two other state-of-the-art algorithms that were designed for the same purpose (i.e., the CompuTree and SimpleTree algorithms). We tested each algorithm against point clouds that incorporated various degrees of noise and occlusion. We applied these algorithms to three contrasting test sites: (1) simulated scenes of coniferous stands in Newfoundland (Canada), (2) test sites of deciduous stands in Phalsbourg (France), and (3) coniferous plantations in Quebec, Canada. In most cases, the STEP algorithm predicted diameter at breast height with higher R2 and lower RMSE than the other two algorithms. The STEP algorithm also achieved greater accuracy when estimating stem diameter in occluded and noisy point clouds, with mean errors in the range of 1.1 cm to 2.28 cm. The CompuTree and SimpleTree algorithms respectively produced errors in the range of 2.62 cm to 6.1 cm and 1.03 cm to 3.34 cm, respectively. Unlike CompuTree or SimpleTree, the STEP algorithm was not able to estimate trunk diameter in the uppermost portions of the trees. Our results show that the STEP algorithm is more adapted to extract DBH and stem diameter automatically from occluded and noisy point clouds. Our study also highlights that SimpleTree and CompuTree require data filtering and results corrections. Conversely, none of these procedures were applied for the implementation of the STEP algorithm. Numéro de notice : A2019-337 Affiliation des auteurs : LIF+Ext (2012-2019) Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/f10070599 date de publication en ligne : 18/07/2019 En ligne : https://doi.org/10.3390/f10070599 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93367 [article]

Empirical stochastic model of detected target centroids: Influence on registration and calibration of terrestrial laser scanners / Tomislav Medic in Journal of applied geodesy, vol 13 n° 3 (July 2019)  

Public [article]  inJournal of applied geodesy > vol 13 n° 3 (July 2019) . - pp 179 – 197 Titre : Empirical stochastic model of detected target centroids: Influence on registration and calibration of terrestrial laser scanners Type de document : Article/Communication Auteurs : Tomislav Medic, Auteur ; Christoph Holst, Auteur ; Jannik Janssen, Auteur ; Heiner Kuhlmann, Auteur Année de publication : 2019 Article en page(s) : pp 179 – 197 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes descripteurs IGN] centroïde [Termes descripteurs IGN] compensation par moindres carrés [Termes descripteurs IGN] détection de cible [Termes descripteurs IGN] données lidar [Termes descripteurs IGN] données localisées 3D [Termes descripteurs IGN] étalonnage d'instrument [Termes descripteurs IGN] incertitude de mesurage [Termes descripteurs IGN] métrologie dimensionelle [Termes descripteurs IGN] modèle stochastique [Termes descripteurs IGN] télémètre laser terrestre Résumé : (auteur) The target-based point cloud registration and calibration of terrestrial laser scanners (TLSs) are mathematically modeled and solved by the least-squares adjustment. However, usual stochastic models are simplified to a large amount: They generally employ a single point measurement uncertainty based on the manufacturers’ specifications. This definition does not hold true for the target-based calibration and registration due to the fact that the target centroid is derived from multiple measurements and its uncertainty depends on the detection procedure as well. In this study, we empirically investigate the precision of the target centroid detection and define an empirical stochastic model in the form of look-up tables. Furthermore, we compare the usual stochastic model with the empirical stochastic model on several point cloud registration and TLS calibration experiments. There, we prove that the values of usual stochastic models are underestimated and incorrect, which can lead to multiple adverse effects such as biased results of the estimation procedures, a false a posteriori variance component analysis, false statistical testing, and false network design conclusions. In the end, we prove that some of the adverse effects can be mitigated by employing the a priori knowledge about the target centroid uncertainty behavior. Numéro de notice : A2019-284 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1515/jag-2018-0032 date de publication en ligne : 22/03/2019 En ligne : https://doi.org/10.1515/jag-2018-0032 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93119 [article]

Landslide monitoring analysis of single-frequency BDS/GPS combined positioning with constraints on deformation characteristics / Dongwei Qiu in Survey review, vol 51 n° 367 (July 2019)  

Public [article]  inSurvey review > vol 51 n° 367 (July 2019) . - pp 364 - 372 Titre : Landslide monitoring analysis of single-frequency BDS/GPS combined positioning with constraints on deformation characteristics Type de document : Article/Communication Auteurs : Dongwei Qiu, Auteur ; Laiyang Wang, Auteur ; Dean Luo, Auteur ; et al., Auteur Année de publication : 2019 Article en page(s) : pp 364 - 372 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de géodésie spatiale [Termes descripteurs IGN] déformation de la croute terrestre [Termes descripteurs IGN] données lidar [Termes descripteurs IGN] données localisées 3D [Termes descripteurs IGN] effondrement de terrain [Termes descripteurs IGN] modèle de simulation [Termes descripteurs IGN] positionnement par BeiDou [Termes descripteurs IGN] positionnement par GPS [Termes descripteurs IGN] précipitation [Termes descripteurs IGN] récepteur monofréquence [Termes descripteurs IGN] simulation numérique [Termes descripteurs IGN] surveillance géologique Résumé : (Auteur) In order to optimise the selection of landslide monitoring points and save the cost of monitoring, a geological constitutive model was constructed by using 3D laser scanning and geological borehole data to simulate the relationship between rainfall and deformation. Thus, the main occurrence area and maximum deformation of the landslide were determined. Aiming at the deficiency of the single-epoch redundancy of the single-frequency GNSS receiver and the poor accuracy, this paper proposes a single-frequency BDS/GPS combined positioning and monitoring scheme with constraint of deformation features to restrict the search range of single-frequency ambiguity, obviously increase the ambiguity fixed success rate and then improve the BDS/GPS positioning accuracy. By contrast experiments, the landslide area obtained by numerical simulation basically matches with the on-site landslide area. The BDS/GPS combined positioning with constraint is consistent with the deep displacement changes, which can well reflect the displacement of the landslide body and make an early warning of disasters. Numéro de notice : A2019-364 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/00396265.2018.1467075 date de publication en ligne : 05/05/2018 En ligne : https://doi.org/10.1080/00396265.2018.1467075 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93449 [article]

Shadow detection and correction using a combined 3D GIS and image processing approach / Safa Ridene in Revue internationale de géomatique, vol 29 n° 3 - 4 (juillet - décembre 2019)  

Permalink

Structural segmentation and classification of mobile laser scanning point clouds with large variations in point density / Yuan Li in ISPRS Journal of photogrammetry and remote sensing, vol 153 (July 2019)  

Permalink

Using LiDAR-modified topographic wetness index, terrain attributes with leaf area index to improve a single-tree growth model in south-eastern Finland / Cheikh Mohamedou in Forestry, an international journal of forest research, vol 92 n° 3 (July 2019)  

Permalink

Demonstrating the transferability of forest inventory attribute models derived using airborne laser scanning data / Piotr Tompalski in Remote sensing of environment, vol 227 (15 June 2019)  

Permalink

Automatisation du traitement de données "mobile mapping" : extraction d'éléments linéaires et ponctuels / Loïc Elsholz in XYZ, n° 159 (juin 2019)  

Permalink

Combining low-density LiDAR and satellite images to discriminate species in mixed Mediterranean forest / Angela Blázquez-Casado in Annals of Forest Science [en ligne], vol 76 n° 2 (June 2019)  

Permalink

Estimating forest stand density and structure using Bayesian individual tree detection, stochastic geometry, and distribution matching / Kasper Kansanen in ISPRS Journal of photogrammetry and remote sensing, vol 152 (June 2019)  

Permalink

RegisTree: a registration algorithm to enhance forest inventory plot georeferencing / Maryem Fadili in Annals of Forest Science [en ligne], vol 76 n° 2 (June 2019)  

Permalink

Piecewise-planar approximation of large 3D data as graph-structured optimization / Stéphane Guinard in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, IV-2/W5 (May 2019)  

Permalink

Detecting and characterizing downed dead wood using terrestrial laser scanning / Tuomas Yrttimaa in ISPRS Journal of photogrammetry and remote sensing, vol 151 (May 2019)  

Permalink