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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]

A hybrid data model for dynamic GIS: application to marine geomorphological dynamics / Younes Hamdani in International journal of geographical information science IJGIS, vol 35 n° 8 (August 2021)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 35 n° 8 (August 2021) . - pp 1475 - 1499 Titre : A hybrid data model for dynamic GIS: application to marine geomorphological dynamics Type de document : Article/Communication Auteurs : Younes Hamdani, Auteur ; Rémy Thibaud, Auteur ; Christophe Claramunt, Auteur Année de publication : 2021 Article en page(s) : pp 1475 - 1499 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications SIG [Termes IGN] base de données spatiotemporelles [Termes IGN] érosion [Termes IGN] interface utilisateur [Termes IGN] modèle conceptuel de données localisées [Termes IGN] modèle dynamique [Termes IGN] PostGIS [Termes IGN] PostgreSQL [Termes IGN] relief sous-marin [Termes IGN] SIG dynamique Résumé : (auteur) The search for the most appropriate GIS data model to integrate, manipulate and analyse spatio-temporal data raises several research questions about the conceptualisation of geographic spaces. Although there is now a general consensus that many environmental phenomena require field and object conceptualisations to provide a comprehensive GIS representation, there is still a need for better integration of these dual representations of space within a formal spatio-temporal database. The research presented in this paper introduces a hybrid and formal dual data model for the representation of spatio-temporal data. The whole approach has been fully implemented in PostgreSQL and its spatial extension PostGIS, where the SQL language is extended by a series of data type constructions and manipulation functions to support hybrid queries. The potential of the approach is illustrated by an application to underwater geomorphological dynamics oriented towards the monitoring of the evolution of seabed changes. A series of performance and scalability experiments are also reported to demonstrate the computational performance of the model. Numéro de notice : A2021-547 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2020.1829628 date de publication en ligne : 12/10/2020 En ligne : https://doi.org/10.1080/13658816.2020.1829628 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98059 [article]

Measuring shallow-water bathymetric signal strength in lidar point attribute data using machine learning / Kim Lowell in International journal of geographical information science IJGIS, vol 35 n° 8 (August 2021)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 35 n° 8 (August 2021) . - pp 1592 - 1610 Titre : Measuring shallow-water bathymetric signal strength in lidar point attribute data using machine learning Type de document : Article/Communication Auteurs : Kim Lowell, Auteur ; Brian Calder, Auteur ; Anthony Lyons, Auteur Année de publication : 2021 Article en page(s) : pp 1592 - 1610 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] apprentissage automatique [Termes IGN] bathymétrie laser [Termes IGN] données lidar [Termes IGN] Extreme Gradient Machine [Termes IGN] Floride (Etats-Unis) [Termes IGN] hydrographie [Termes IGN] lever bathymétrique [Termes IGN] semis de points Résumé : (auteur) The goal of this work was to evaluate if routinely collected but seldom used airborne lidar metadata – ‘point attribute data’ (PAD) – analyzed using machine learning/artificial intelligence can improve extraction of shallow-water (less than 20 m) bathymetry from lidar point clouds. Extreme gradient boosting (XGB) models relating PAD to an existing bathymetry/not bathymetry classification were fitted and evaluated for four areas near the Florida Keys. The PAD examined include ‘pulse specific’ information such as the return intensity and PAD describing flight path consistency. The R2 values for the XGB models were between 0.34 and 0.74. Global classification accuracies were above 80% although this reflected a sometimes extreme Bathy/NotBathy imbalance that inflated global accuracy. This imbalance was mitigated by employing a probability decision threshold (PDT) that equalizes the true positive (Bathy) and true negative (NotBathy) rates. It was concluded that 1) the strength of the bathymetric signal in the PAD should be sufficient to increase accuracy of density-based lidar point cloud bathymetry extraction methods and 2) ML can successfully model the relationship between the PAD and the Bathy/NotBathy classification. A method is also presented to examine the spatial and feature-space distribution of errors that will facilitate quality assurance and continuous improvement. Numéro de notice : A2021-548 Affiliation des auteurs : non IGN Thématique : IMAGERIE/POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2020.1867147 date de publication en ligne : 30/12/2020 En ligne : https://doi.org/10.1080/13658816.2020.1867147 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98061 [article]

Evaluating the suitability of multi-scale terrain attribute calculation approaches for seabed mapping applications / Benjamin Misiuk in Marine geodesy, vol 44 n° 4 (July 2021)  

Public [article]  inMarine geodesy > vol 44 n° 4 (July 2021) . - pp 327 - 385 Titre : Evaluating the suitability of multi-scale terrain attribute calculation approaches for seabed mapping applications Type de document : Article/Communication Auteurs : Benjamin Misiuk, Auteur ; Vincent Lecours, Auteur ; M.F.J. Dolan, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 327 - 385 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Cartographie thématique [Termes IGN] analyse multiéchelle [Termes IGN] artefact [Termes IGN] attribut géomètrique [Termes IGN] carte bathymétrique [Termes IGN] cartographie hydrographique [Termes IGN] fond marin [Termes IGN] géomorphométrie [Termes IGN] habitat animal [Termes IGN] pente [Termes IGN] réalité de terrain [Termes IGN] rugosité [Termes IGN] sondeur multifaisceaux [Termes IGN] Terre-Neuve, île de (Terre-Neuve-et-Labrador) Résumé : (auteur) The scale dependence of benthic terrain attributes is well-accepted, and multi-scale methods are increasingly applied for benthic habitat mapping. There are, however, multiple ways to calculate terrain attributes at multiple scales, and the suitability of these approaches depends on the purpose of the analysis and data characteristics. There are currently few guidelines establishing the appropriateness of multi-scale raster calculation approaches for specific benthic habitat mapping applications. First, we identify three common purposes for calculating terrain attributes at multiple scales for benthic habitat mapping: (i) characterizing scale-specific terrain features, (ii) reducing data artefacts and errors, and (iii) reducing the mischaracterization of ground-truth data due to inaccurate sample positioning. We then define criteria that calculation approaches should fulfill to address these purposes. At two study sites, five raster terrain attributes, including measures of orientation, relative position, terrain variability, slope, and rugosity were calculated at multiple scales using four approaches to compare the suitability of the approaches for these three purposes. Results suggested that specific calculation approaches were better suited to certain tasks. A transferable parameter, termed the ‘analysis distance’, was necessary to compare attributes calculated using different approaches, and we emphasize the utility of such a parameter for facilitating the generalized comparison of terrain attributes across methods, sites, and scales. Numéro de notice : A2021-526 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/01490419.2021.1925789 date de publication en ligne : 04/06/2021 En ligne : https://doi.org/10.1080/01490419.2021.1925789 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97967 [article]

Extracting Shallow-Water Bathymetry from Lidar point clouds using pulse attribute data: Merging density-based and machine learning approaches / Kim Lowell in Marine geodesy, vol 44 n° 4 (July 2021)  

Public [article]  inMarine geodesy > vol 44 n° 4 (July 2021) . - pp 259 - 286 Titre : Extracting Shallow-Water Bathymetry from Lidar point clouds using pulse attribute data: Merging density-based and machine learning approaches Type de document : Article/Communication Auteurs : Kim Lowell, Auteur ; Brian Calder, Auteur Année de publication : 2021 Article en page(s) : pp 259 - 286 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] angle d'incidence [Termes IGN] apprentissage automatique [Termes IGN] bathymétrie laser [Termes IGN] classification barycentrique [Termes IGN] données lidar [Termes IGN] Extreme Gradient Machine [Termes IGN] Floride (Etats-Unis) [Termes IGN] lever bathymétrique [Termes IGN] profondeur [Termes IGN] semis de points Résumé : (auteur) To automate extraction of bathymetric soundings from lidar point clouds, two machine learning (ML1) techniques were combined with a more conventional density-based algorithm. The study area was four data “tiles” near the Florida Keys. The density-based algorithm determined the most likely depth (MLD) for a grid of “estimation nodes” (ENs). Unsupervised k-means clustering determined which EN’s MLD depth and associated soundings represented ocean depth rather than ocean surface or noise to produce a preliminary classification. An extreme gradient boosting (XGB) model was fitted to pulse return metadata – e.g. return intensity, incidence angle – to produce a final Bathy/NotBathy classification. Compared to an operationally produced reference classification, the XGB model increased global accuracy and decreased the false negative rate (FNR) – i.e. undetected bathymetry – that are most important for nautical navigation for all but one tile. Agreement between the final XGB and operational reference classifications ranged from 0.84 to 0.999. Imbalance between Bathy and NotBathy was addressed using a probability decision threshold that equalizes the FNR and the true positive rate (TPR). Two methods are presented for visually evaluating differences between the two classifications spatially and in feature-space. Numéro de notice : A2021-525 Affiliation des auteurs : non IGN Thématique : IMAGERIE/POSITIONNEMENT Nature : Article DOI : 10.1080/01490419.2021.1925790 date de publication en ligne : 25/05/2021 En ligne : https://doi.org/10.1080/01490419.2021.1925790 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97964 [article]

Coral habitat mapping: a comparison between maximum likelihood, Bayesian and Dempster–Shafer classifiers / Mohammad Shawkat Hossain in Geocarto international, vol 36 n° 11 ([15/06/2021])  

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Comparison and evaluation of high-resolution marine gravity recovery via sea surface heights or sea surface slopes / Shengjun Zhang in Journal of geodesy, vol 95 n° 6 (June 2021)  

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Flood risk mapping using uncertainty propagation analysis on a peak discharge: case study of the Mille Iles River in Quebec / Jean-Marie Zokagoa in Natural Hazards, vol 107 n° 1 (May 2021)  

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Mapping and quantification of the dwarf eelgrass Zostera noltii using a random forest algorithm on a SPOT 7 satellite image / Salma Benmokhtar in ISPRS International journal of geo-information, vol 10 n° 5 (May 2021)  

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Study on offshore seabed sediment classification based on particle size parameters using XGBoost algorithm / Fengfan Wang in Computers & geosciences, vol 149 (April 2021)  

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Horizontal calibration of vessels with UASs / Casey O'Heran in Marine geodesy, vol 44 n° 2 (March 2021)  

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Coastal water remote sensing from sentinel-2 satellite data using physical, statistical, and neural network retrieval approach / Frank S. Marzano in IEEE Transactions on geoscience and remote sensing, vol 59 n° 2 (February 2021)  

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Using Sentinel-2 images to estimate topography, tidal-stage lags and exposure periods over large intertidal areas / José P. Granadeiro in Remote sensing, Vol 13 n° 2 (January-2 2021)  

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Determination of the under water position of objects by reflectorless total stations / Štefan Rákay in Survey review, vol 53 n°376 (January 2021)  

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The Influence of camera calibration on nearshore bathymetry estimation from UAV Vvdeos / Gonzalo Simarro in Remote sensing, vol 13 n° 1 (January 2021)  

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