Centre dedocumentation
scientifique

Terrain representation using orientation / Gene Trantham in Cartography and Geographic Information Science, vol 49 n° 6 (November 2022)  

Public [article]  inCartography and Geographic Information Science > vol 49 n° 6 (November 2022) . - pp 479 - 491 Titre : Terrain representation using orientation Type de document : Article/Communication Auteurs : Gene Trantham, Auteur ; Patrick Kennelly, Auteur Année de publication : 2022 Article en page(s) : pp 479 - 491 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Rédaction cartographique [Termes IGN] données matricielles [Termes IGN] estompage [Termes IGN] modèle numérique de surface [Termes IGN] ombre [Termes IGN] orientation [Termes IGN] représentation du relief [Termes IGN] teinte hypsométrique Résumé : (auteur) A terrain data model using orientation rather than elevation permits more efficient analysis and stores its data in a multi-band raster. Representation techniques from the computer graphics industry are readily adopted with this data model. A common data model for terrain surfaces–the raster digital elevation model (DEM)–carries with it limitations by emphasizing height. Derived products such as relief shading require additional processing to determine orientation, even though they are used more frequently than those relying on elevation (e.g. hypsometric tinting). We show some of the benefits of encoding and analyzing terrain based on surface orientation, an approach that uses normal vectors stored as multi-band raster, the data storage convention in the computer graphics industry. A change in the data model and the conceptualization of the surface’s defining characteristics allows relief shading methods to run faster than conventional tools. Processing efficiencies are especially useful for more advanced shading models that may employ hundreds of relief shading calculations. In addition, an orientation-focused approach to terrain permits cartographic techniques to parallel common computer graphics methods. This project explores one such method, normal-mapping, an effect that adds texture to conventional relief shading by perturbing surface normal vectors. Numéro de notice : A2022-844 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/15230406.2022.2035256 Date de publication en ligne : 03/03/2022 En ligne : https://doi.org/10.1080/15230406.2022.2035256 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102072 [article]

A deep 2D/3D Feature-Level fusion for classification of UAV multispectral imagery in urban areas / Hossein Pourazar in Geocarto international, vol 37 n° 23 ([15/10/2022])  

Public [article]  inGeocarto international > vol 37 n° 23 [15/10/2022] . - pp 6695 - 6712 Titre : A deep 2D/3D Feature-Level fusion for classification of UAV multispectral imagery in urban areas Type de document : Article/Communication Auteurs : Hossein Pourazar, Auteur ; Farhad Samadzadegan, Auteur ; Farzaneh Dadrass Javan, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 6695 - 6712 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] alignement des données [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] image captée par drone [Termes IGN] image multibande [Termes IGN] image proche infrarouge [Termes IGN] image RVB [Termes IGN] modèle numérique de surface [Termes IGN] orthophotoplan numérique [Termes IGN] zone urbaine Résumé : (auteur) In this paper, a deep convolutional neural network (CNN) is developed to classify the Unmanned Aerial Vehicle (UAV) derived multispectral imagery and normalized digital surface model (DSM) data in urban areas. For this purpose, a multi-input deep CNN (MIDCNN) architecture is designed using 11 parallel CNNs; 10 deep CNNs to extract the features from all possible triple combinations of spectral bands as well as one deep CNN dedicated to the normalized DSM data. The proposed method is compared with the traditional single-input (SI) and double-input (DI) deep CNN designations and random forest (RF) classifier, and evaluated using two independent test datasets. The results indicate that increasing the CNN layers parallelly augmented the classifier’s generalization and reduced overfitting risk. The overall accuracy and kappa value of the proposed method are 95% and 0.93, respectively, for the first test dataset, and 96% and 0.94, respectively, for the second test data set. Numéro de notice : A2022-749 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2021.1959655 Date de publication en ligne : 04/08/2021 En ligne : https://doi.org/10.1080/10106049.2021.1959655 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101741 [article]

Determination of local geometric geoid model for Kuwait / Ahmed Zaki in Journal of applied geodesy, vol 16 n° 4 (October 2022)  

Public [article]  inJournal of applied geodesy > vol 16 n° 4 (October 2022) . - pp 393 - 400 Titre : Determination of local geometric geoid model for Kuwait Type de document : Article/Communication Auteurs : Ahmed Zaki, Auteur ; Yasmeen Elberry, Auteur ; Hamad Al-Ajami, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 393 - 400 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie physique [Termes IGN] altitude orthométrique [Termes IGN] conversion altimétrique [Termes IGN] données GNSS [Termes IGN] géoïde local [Termes IGN] Koweit [Termes IGN] modèle de géopotentiel [Termes IGN] modèle numérique de surface Résumé : (auteur) Determining a precise local geoid is particularly important for converting the Global Navigation Satellite System (GNSS) heights to orthometric heights. The geometric method for computing the geoid has been extensively used for a comparatively small region, which, in some points, interpolates geoid heights based on GNSS-derived heights and levelling heights. Several considerations should be considered when using the geometric method to increase the accuracy of a local geoid. Kuwait is used as a test area in this paper to investigate several features of the geometric method. The achievable precision is one of these aspects, the role of the interpolation method, global geopotential models, and the influence of the topographic effect. The accuracy of the local geoid can be substantially enhanced by integrating a geopotential model with a digital terrain model of the research region. It is possible to get a precision of 2–3 cm. Numéro de notice : A2022-743 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article DOI : 10.1515/jag-2022-0017 Date de publication en ligne : 23/07/2022 En ligne : https://doi.org/10.1515/jag-2022-0017 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101725 [article]

Analytical method for high-precision seabed surface modelling combining B-spline functions and Fourier series / Tyler Susa in Marine geodesy, vol 45 n° 5 (September 2022)  

Public [article]  inMarine geodesy > vol 45 n° 5 (September 2022) . - pp 435 - 461 Titre : Analytical method for high-precision seabed surface modelling combining B-spline functions and Fourier series Type de document : Article/Communication Auteurs : Tyler Susa, Auteur Année de publication : 2022 Article en page(s) : pp 435 - 461 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] bathymétrie [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] données d'entrainement (apprentissage automatique) [Termes IGN] Extreme Gradient Machine [Termes IGN] fond marin [Termes IGN] image Sentinel-MSI [Termes IGN] littoral [Termes IGN] modèle numérique de surface [Termes IGN] modélisation [Termes IGN] Porto Rico [Termes IGN] profondeur [Termes IGN] réflectance Résumé : (auteur) Accurate charting of nearshore bathymetry is critical to the safe and dependable use of coastal waterways frequented by the trading, fishing, tourism, and ocean energy industries. The accessibility of satellite imagery and the availability of various satellite-derived bathymetry (SDB) techniques have provided a cost-effective alternative to traditional in-situ bathymetric surveys. Furthermore, improved algorithms and the advancement of machine learning models have provided opportunity for higher quality bathymetric derivations. However, to date the relative accuracy and performance between traditional physics-based techniques, improved physics-based methods, and machine learning ensemble models have not been adequately quantified. In this study, nearshore bathymetry is derived from Sentinel-2 satellite imagery near La Parguera, Puerto Rico utilizing a traditional band-ratio algorithm, a band-ratio switching method, a random forest machine learning model, and the XGBoost machine learning model. The machine learning models returned comparable results and were markedly more accurate relative to other techniques; however, both machine learning models required an extensive training dataset. All models were constrained by environmental influences and image spatial resolution, which were assessed to be the limiting factors for routine use of satellite-derived bathymetry as a reliable method for hydrographic surveying. Numéro de notice : A2022-609 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/01490419.2022.2064572 Date de publication en ligne : 04/05/2022 En ligne : https://doi.org/10.1080/01490419.2022.2064572 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101392 [article]

A high-resolution gravimetric geoid model for Kingdom of Saudi Arabia / Ahmed Zaki in Survey review, vol 54 n° 386 (September 2022)  

Public [article]  inSurvey review > vol 54 n° 386 (September 2022) . - pp 375 - 390 Titre : A high-resolution gravimetric geoid model for Kingdom of Saudi Arabia Type de document : Article/Communication Auteurs : Ahmed Zaki, Auteur ; Saad Mogren, Auteur Année de publication : 2022 Article en page(s) : pp 375 - 390 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie physique [Termes IGN] Arabie Saoudite [Termes IGN] géoïde altimétrique [Termes IGN] géoïde gravimétrique [Termes IGN] geoïde marin [Termes IGN] intégrale de Stokes [Termes IGN] modèle de géopotentiel [Termes IGN] modèle numérique de surface [Termes IGN] nivellement avec assistance GPS [Termes IGN] transformation rapide de Fourier Résumé : (auteur) A high-resolution gravimetric geoid model for the Kingdom of Saudi Arabia area was determined. A data set of 459,848 land gravity, 80,632 shipborne marine gravity data, DTU17 altimetry gravity model, and XGM2019e global geopotential model. The computation strategy followed for modelling of the gravimetric geoid is based on the Remove-Compute-Restore with Residual Terrain Model reduction and the 1D- Fast Fourier Transform approach technique. The geoid heights have been determined by using the Stokes integral with Wong–Gore modification. The accuracy of the resulting geoid models was evaluated by comparing them with 5385 GPS/levelling points. The geoid accuracy over all the kingdom is better than 11 cm in STD sense and the comparison in sub-areas obtained accuracy range from 2.8 to 11.9 cm according to the density of gravity observations. Numéro de notice : A2022-657 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/00396265.2021.1944544 Date de publication en ligne : 29/06/2021 En ligne : https://doi.org/10.1080/00396265.2021.1944544 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101508 [article]

Automated extraction and validation of Stone Pine (Pinus pinea L.) trees from UAV-based digital surface models / Asli Ozdarici-Ok in Geo-spatial Information Science, vol 25 n° inconnu ([01/08/2022])  

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Incorporation of Digital Elevation Model, Normalized Difference Vegetation Index, and Landsat-8 Data for Land Use Land Cover Mapping / Jwan Al-Doski in Photogrammetric Engineering & Remote Sensing, PERS, vol 88 n° 8 (August 2022)  

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Integrating post-processing kinematic (PPK) structure-from-motion (SfM) with unmanned aerial vehicle (UAV) photogrammetry and digital field mapping for structural geological analysis / Daniele Cirillo in ISPRS International journal of geo-information, vol 11 n° 8 (August 2022)  

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UAV-borne, LiDAR-based elevation modelling: a method for improving local-scale urban flood risk assessment / Katerina Trepekli in Natural Hazards, vol 113 n° 1 (August 2022)  

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Uncertainty interval estimates for computing slope and aspect from a gridded digital elevation model / Carlos López-Vázquez in International journal of geographical information science IJGIS, vol 36 n° 8 (August 2022)  

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Exploring the vertical dimension of street view image based on deep learning: a case study on lowest floor elevation estimation / Huan Ning in International journal of geographical information science IJGIS, vol 36 n° 7 (juillet 2022)  

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3D modeling method for dome structure using digital geological map and DEM / Xian-Yu Liu in ISPRS International journal of geo-information, vol 11 n° 6 (June 2022)  

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Physical modelling of Nanda Devi National Park, a natural world heritage site, from GIS data / Sanat Agrawal in Cartographica, vol 57 n° 2 (Summer 2022)  

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True orthophoto generation based on unmanned aerial vehicle images using reconstructed edge points / Mojdeh Ebrahimikia in Photogrammetric record, vol 37 n° 178 (June 2022)  

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Vegetation cover mapping from RGB webcam time series for land surface emissivity retrieval in high mountain areas / Benedikt Hiebl in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-2-2022 (2022 edition)  

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