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Auteur Brian Calder |
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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)
[article]
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 pointsRésumé : (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
in International journal of geographical information science IJGIS > vol 35 n° 8 (August 2021) . - pp 1592 - 1610[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)
[article]
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 pointsRésumé : (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
in Marine geodesy > vol 44 n° 4 (July 2021) . - pp 259 - 286[article]Horizontal calibration of vessels with UASs / Casey O'Heran in Marine geodesy, vol 44 n° 2 (March 2021)
[article]
Titre : Horizontal calibration of vessels with UASs Type de document : Article/Communication Auteurs : Casey O'Heran, Auteur ; Brian Calder, Auteur Année de publication : 2021 Article en page(s) : pp 91 - 107 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] balayage laser
[Termes IGN] bathymétrie laser
[Termes IGN] carte bathymétrique
[Termes IGN] centrale inertielle
[Termes IGN] données localisées des bénévoles
[Termes IGN] étalonnage d'instrument
[Termes IGN] modèle numérique de surface
[Termes IGN] navire
[Termes IGN] point d'appui
[Termes IGN] réalité de terrain
[Termes IGN] structure-from-motionRésumé : (auteur) Knowledge of offset vectors from vessel mounted sonars, to systems such as Inertial Measurement Units and Global Navigation Satellite Systems is crucial for accurate ocean mapping applications. Traditional survey methods, such as employing laser scanners or total stations, are used to determine professional vessel offset distances reliably. However, for vessels of opportunity that are collecting volunteer bathymetric data, it is beneficial to consider survey methods that may be less time consuming, less expensive, or which do not involve bringing the vessel into a dry dock. Thus, this article explores two alternative methods that meet this criterion for horizontally calibrating vessels. Unmanned Aircraft Systems (UASs) were used to horizontally calibrate a vessel with both Structure from Motion photogrammetry and aerial lidar while the vessel was moored to a floating dock. Estimates of the horizontal deviations from ground truth, were obtained by comparing the horizontal distances between targets on a vessel, acquired by the UAS methods, to multiple ground truth sources: a survey-grade terrestrial laser scan and fiberglass tape measurements. The investigated methods were able to achieve horizontal deviations on the order of centimeters with the use of Ground Control Points. Numéro de notice : A2021-266 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1080/01490419.2021.187933 Date de publication en ligne : 04/03/2021 En ligne : https://doi.org/10.1080/01490419.2021.1879330 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97320
in Marine geodesy > vol 44 n° 2 (March 2021) . - pp 91 - 107[article]