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3D change detection using adaptive thresholds based on local point cloud density / Dan Liu in ISPRS International journal of geo-information, vol 10 n° 3 (March 2021)
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[article]
Titre : 3D change detection using adaptive thresholds based on local point cloud density Type de document : Article/Communication Auteurs : Dan Liu, Auteur ; Dajun Li, Auteur ; Meizhen Wang, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 127 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] classification barycentrique
[Termes IGN] densité des points
[Termes IGN] détection de changement
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] MNS lidar
[Termes IGN] scène urbaine
[Termes IGN] semis de points
[Termes IGN] seuillage de pointsRésumé : (auteur) In recent years, because of highly developed LiDAR (Light Detection and Ranging) technologies, there has been increasing demand for 3D change detection in urban monitoring, urban model updating, and disaster assessment. In order to improve the effectiveness of 3D change detection based on point clouds, an approach for 3D change detection using point-based comparison is presented in this paper. To avoid density variation in point clouds, adaptive thresholds are calculated through the k-neighboring average distance and the local point cloud density. A series of experiments for quantitative evaluation is performed. In the experiments, the influencing factors including threshold, registration error, and neighboring number of 3D change detection are discussed and analyzed. The results of the experiments demonstrate that the approach using adaptive thresholds based on local point cloud density are effective and suitable. Numéro de notice : A2021-231 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi10030127 Date de publication en ligne : 02/03/2021 En ligne : https://doi.org/10.3390/ijgi10030127 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97222
in ISPRS International journal of geo-information > vol 10 n° 3 (March 2021) . - n° 127[article]Enhanced trajectory estimation of mobile laser scanners using aerial images / Zille Hussnain in ISPRS Journal of photogrammetry and remote sensing, vol 173 (March 2021)
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Titre : Enhanced trajectory estimation of mobile laser scanners using aerial images Type de document : Article/Communication Auteurs : Zille Hussnain, Auteur ; Sander J. Oude Elberink, Auteur ; M. George Vosselman, Auteur Année de publication : 2021 Article en page(s) : pp 66 - 78 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] appariement de points
[Termes IGN] atténuation du signal
[Termes IGN] balayage laser
[Termes IGN] canyon urbain
[Termes IGN] centrale inertielle
[Termes IGN] données lidar
[Termes IGN] erreur
[Termes IGN] image captée par drone
[Termes IGN] mesurage par GNSS
[Termes IGN] semis de points
[Termes IGN] trajectoire (véhicule non spatial)
[Termes IGN] trajet multipleRésumé : (auteur) Multipath effects and signal obstruction by buildings in urban canyons can lead to inaccurate GNSS measurements and therefore errors in the estimated trajectory of Mobile Laser Scanning (MLS) systems; consequently, derived point clouds are distorted and lose spatial consistency. We obtain decimetre-level trajectory accuracy making use of corresponding points between the MLS data and aerial images with accurate exterior orientations instead of using ground control points. The MLS trajectory is estimated based on observation equations resulting from these corresponding points, the original IMU observations, and soft constraints on the pitch and yaw rotations of the vehicle. We analyse the quality of the trajectory enhancement under several conditions where the experiments were designed to test the influence of the number and quality of corresponding points and to test different settings for a B-spline representation of the vehicle trajectory. The method was tested on two independently acquired MLS datasets in Rotterdam by enhancing the trajectories and evaluating them using checkpoints. The RMSE values of the original GNSS/IMU based Kalman filter results at the checkpoints were 0.26 m, 0.30 m, and 0.47 m for the X-, Y- and Z-coordinates in the first dataset and 1.10 m, 1.51 m, and 1.81 m in the second dataset. The latter dataset was recorded with a lower quality IMU in an area with taller buildings. After trajectory adjustment these RMSE values were reduced to 0.09 m, 0.11 m, and 0.16 m for the first dataset and 0.12 m, 0.14 m, and 0.18 m for the second dataset. The results confirmed that, if sufficient tie points between the point cloud and aerial imagery are available, the method supports geo-referencing of MLS point clouds in urban canyons with a near-decimetre accuracy. Numéro de notice : A2021-102 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2021.01.005 Date de publication en ligne : 17/01/2021 En ligne : https://doi.org/10.1016/j.isprsjprs.2021.01.005 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96877
in ISPRS Journal of photogrammetry and remote sensing > vol 173 (March 2021) . - pp 66 - 78[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2021031 SL Revue Centre de documentation Revues en salle Disponible 081-2021033 DEP-RECP Revue LASTIG Dépôt en unité Exclu du prêt 081-2021032 DEP-RECF Revue Nancy Dépôt en unité Exclu du prêt Graph convolutional autoencoder model for the shape coding and cognition of buildings in maps / Xiongfeng Yan in International journal of geographical information science IJGIS, vol 35 n° 3 (March 2021)
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Titre : Graph convolutional autoencoder model for the shape coding and cognition of buildings in maps Type de document : Article/Communication Auteurs : Xiongfeng Yan, Auteur ; Tinghua Ai, Auteur ; Min Yang, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 490 - 512 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] apprentissage non-dirigé
[Termes IGN] apprentissage profond
[Termes IGN] codage
[Termes IGN] données vectorielles
[Termes IGN] graphe
[Termes IGN] mesure géométrique
[Termes IGN] modélisation du bâti
[Termes IGN] représentation cognitive
[Termes IGN] représentation spatialeRésumé : (auteur) The shape of a geospatial object is an important characteristic and a significant factor in spatial cognition. Existing shape representation methods for vector-structured objects in the map space are mainly based on geometric and statistical measures. Considering that shape is complicated and cognitively related, this study develops a learning strategy to combine multiple features extracted from its boundary and obtain a reasonable shape representation. Taking building data as example, this study first models the shape of a building using a graph structure and extracts multiple features for each vertex based on the local and regional structures. A graph convolutional autoencoder (GCAE) model comprising graph convolution and autoencoder architecture is proposed to analyze the modeled graph and realize shape coding through unsupervised learning. Experiments show that the GCAE model can produce a cognitively compliant shape coding, with the ability to distinguish different shapes. It outperforms existing methods in terms of similarity measurements. Furthermore, the shape coding is experimentally proven to be effective in representing the local and global characteristics of building shape in application scenarios such as shape retrieval and matching. Numéro de notice : A2021-166 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2020.1768260 Date de publication en ligne : 25/05/2020 En ligne : https://doi.org/10.1080/13658816.2020.1768260 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97100
in International journal of geographical information science IJGIS > vol 35 n° 3 (March 2021) . - pp 490 - 512[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 079-2021031 SL Revue Centre de documentation Revues en salle Disponible Improving the unsupervised mapping of riparian bugweed in commercial forest plantations using hyperspectral data and LiDAR / Kabir Peerbhay in Geocarto international, vol 36 n° 4 ([01/03/2021])
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Titre : Improving the unsupervised mapping of riparian bugweed in commercial forest plantations using hyperspectral data and LiDAR Type de document : Article/Communication Auteurs : Kabir Peerbhay, Auteur ; Onisimo Mutanga, Auteur ; Romano Lottering, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 465 - 480 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] carte de la végétation
[Termes IGN] classification non dirigée
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] détection d'objet
[Termes IGN] données lidar
[Termes IGN] espèce exotique envahissante
[Termes IGN] forêt ripicole
[Termes IGN] image AISA+
[Termes IGN] image hyperspectrale
[Termes IGN] précision cartographique
[Termes IGN] semis de pointsRésumé : (auteur) Accurate spatial information on the location of invasive alien plants (IAPs) in riparian environments is critical to fulfilling a comprehensive weed management regime. This study aimed to automatically map the occurrence of riparian bugweed (Solanum mauritianum) using airborne AISA Eagle hyperspectral data (393 nm–994 nm) in conjunction with LiDAR derived height. Utilising an unsupervised random forest (RF) classification approach and Anselin local Moran’s I clustering, results indicate that the integration of LiDAR with minimum noise fraction (MNF) produce the best detection rate (DR) of 88%, the lowest false positive rate (FPR) of 7.14% and an overall mapping accuracy of 83% for riparian bugweed. In comparison, utilising the original hyperspectral wavebands with and without LiDAR produced lower DRs and higher FPRs with overall accuracies of 79% and 68% respectively. This research demonstrates the potential of combining spectral information with LiDAR to accurately map IAPs using an automated unsupervised RF anomaly detection framework. Numéro de notice : A2021-163 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2019.1614101 Date de publication en ligne : 10/06/2019 En ligne : https://doi.org/10.1080/10106049.2019.1614101 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97084
in Geocarto international > vol 36 n° 4 [01/03/2021] . - pp 465 - 480[article]Ontology-based semantic conceptualisation of historical built heritage to generate parametric structured models from point clouds / Elisabetta Colucci in Applied sciences, vol 11 n° 6 (March 2021)
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Titre : Ontology-based semantic conceptualisation of historical built heritage to generate parametric structured models from point clouds Type de document : Article/Communication Auteurs : Elisabetta Colucci, Auteur ; Xufeng Xing, Auteur ; Margarita Kokla, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 2813 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Bases de données localisées
[Termes IGN] CityGML
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] format Industry foudation classes IFC
[Termes IGN] information sémantique
[Termes IGN] modélisation du bâti
[Termes IGN] ontologie
[Termes IGN] patrimoine culturel
[Termes IGN] patrimoine immobilier
[Termes IGN] segmentation sémantiqueRésumé : (auteur) Nowadays, cultural and historical built heritage can be more effectively preserved, valorised and documented using advanced geospatial technologies. In such a context, there is a major issue concerning the automation of the process and the extraction of useful information from a huge amount of spatial information acquired by means of advanced survey techniques (i.e., highly detailed LiDAR point clouds). In particular, in the case of historical built heritage (HBH) there are very few effective efforts. Therefore, in this paper, the focus is on establishing the connections between semantic and geometrical information in order to generate a parametric, structured model from point clouds using ontology as an effective approach for the formal conceptualisation of application domains. Hence, in this paper, an ontological schema is proposed to structure HBH representations, starting with international standards, vocabularies, and ontologies (CityGML-Geography Markup Language, International Committee for Documentation conceptual reference model (CIDOC-CRM), Industry Foundation Classes (IFC), Getty Art and Architecture Thesaurus (AAT), as well as reasoning about morphology of historical centres by analysis of real case studies) to represent the built and architecture domain. The validation of such schema is carried out by means of its use to guide the segmentation of a LiDAR point cloud from a castle, which is later used to generate parametric geometries to be used in a historical building information model (HBIM). Numéro de notice : A2021-498 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.3390/app11062813 Date de publication en ligne : 22/03/2021 En ligne : https://doi.org/10.3390/app11062813 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97983
in Applied sciences > vol 11 n° 6 (March 2021) . - n° 2813[article]Progressive TIN densification with connection analysis for urban Lidar data / Tao Wang in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 3 (March 2021)
PermalinkWhat factors shape spatial distribution of biomass in riparian forests? Insights from a LiDAR survey over a large area / Leo Huylenbroeck in Forests, vol 12 n° 3 (March 2021)
PermalinkAn anchor-based graph method for detecting and classifying indoor objects from cluttered 3D point clouds / Fei Su in ISPRS Journal of photogrammetry and remote sensing, vol 172 (February 2021)
PermalinkAutomatic filtering and 2D modeling of airborne laser scanning building point cloud / Fayez Tarsha-Kurdi in Transactions in GIS, Vol 25 n° 1 (February 2021)
PermalinkCurved buildings reconstruction from airborne LiDAR data by matching and deforming geometric primitives / Jingwei Song in IEEE Transactions on geoscience and remote sensing, vol 59 n° 2 (February 2021)
PermalinkDeveloping a site index model for P. Pinaster stands in NW Spain by combining bi-temporal ALS data and environmental data / Juan Guerra-Hernández in Forest ecology and management, vol 481 (February 2021)
PermalinkA feature-preserving point cloud denoising algorithm for LiDAR-derived DEM construction / Chuanfa Chen in Survey review, Vol 53 n° 377 (February 2021)
PermalinkImproving trajectory estimation using 3D city models and kinematic point clouds / Lucas Lucks in Transactions in GIS, Vol 25 n° 1 (February 2021)
PermalinkMonitoring the coastal changes of the Po river delta (Northern Italy) since 1911 using archival cartography, multi-temporal aerial photogrammetry and LiDAR data: implications for coastline changes in 2100 A.D. / Massimo Fabris in Remote sensing, Vol 13 n° 3 (February 2021)
PermalinkTropical forest canopy height estimation from combined polarimetric SAR and LiDAR using machine-learning / Maryam Pourshamsi in ISPRS Journal of photogrammetry and remote sensing, vol 172 (February 2021)
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