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Geomorphological mapping and anthropogenic landform change in an urbanizing watershed using structure-from-motion photogrammetry and geospatial modeling techniques / Peter G. Chirico in Journal of maps, vol 17 n° 4 (October 2021)
[article]
Titre : Geomorphological mapping and anthropogenic landform change in an urbanizing watershed using structure-from-motion photogrammetry and geospatial modeling techniques Type de document : Article/Communication Auteurs : Peter G. Chirico, Auteur ; Sarah E. Bergstresser, Auteur ; Jessica D. DeWitt, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 241 - 252 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] aménagement du territoire
[Termes IGN] archives
[Termes IGN] bassin hydrographique
[Termes IGN] cartographie géomorphologique
[Termes IGN] croissance urbaine
[Termes IGN] détection de changement
[Termes IGN] érosion anthropique
[Termes IGN] modèle numérique de surface
[Termes IGN] modélisation spatiale
[Termes IGN] photogrammétrie numérique
[Termes IGN] photographie aérienne
[Termes IGN] structure-from-motion
[Termes IGN] Virginie (Etats-Unis)Résumé : (auteur) Increasing urbanization and suburban growth in cities globally has highlighted the importance of land planning using detailed geomorphologic maps that depict anthropogenic landform changes. Such mapping provides information crucial for land management, hazard identification, and the management of the challenges arising from urbanization. The development and use of quantitative and repeatable methods to map anthropogenic and natural processes are required to advance the science of urban geomorphological mapping. This study investigated the application of geospatial modeling, structure-from-motion (SfM) photogrammetric methods and DEM differencing as means of quantifying anthropogenic landform changes from archival aerial imagery. Anthropogenic landforms were incorporated into a detailed geomorphologic map in an urbanizing watershed located in the Washington, D.C. metropolitan suburb of Vienna, Virginia. Numéro de notice : A2021-813 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/IMAGERIE Nature : Article DOI : 10.1080/17445647.2020.1746419 Date de publication en ligne : 01/04/2020 En ligne : https://doi.org/10.1080/17445647.2020.1746419 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98887
in Journal of maps > vol 17 n° 4 (October 2021) . - pp 241 - 252[article]Monitoring 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)
[article]
Titre : Monitoring 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. Type de document : Article/Communication Auteurs : Massimo Fabris, Auteur Année de publication : 2021 Article en page(s) : n° 529 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] analyse spatio-temporelle
[Termes IGN] archives
[Termes IGN] cartographie ancienne
[Termes IGN] détection de changement
[Termes IGN] données lidar
[Termes IGN] données multitemporelles
[Termes IGN] modèle numérique de terrain
[Termes IGN] montée du niveau de la mer
[Termes IGN] photogrammétrie aérienne
[Termes IGN] Pô (delta)
[Termes IGN] semis de points
[Termes IGN] surveillance du littoral
[Termes IGN] trait de côteRésumé : (auteur) Interaction between land subsidence and sea level rise (SLR) increases the hazard in coastal areas, mainly for deltas, characterized by flat topography and with great social, ecological, and economic value. Coastal areas need continuous monitoring as a support for human intervention to reduce the hazard. Po River Delta (PRD, northern Italy) in the past was affected by high values of artificial land subsidence: even if at low rates, anthropogenic settlements are currently still in progress and produce an increase of hydraulic risk due to the loss of surface elevation both of ground and levees. Many authors have provided scenarios for the next decades with increased flooding in densely populated areas. In this work, a contribution to the understanding future scenarios based on the morphological changes that occurred in the last century on the PRD coastal area is provided: planimetric variations are reconstructed using two archival cartographies (1911 and 1924), 12 multi-temporal high-resolution aerial photogrammetric surveys (1933, 1944, 1949, 1955, 1962, 1969, 1977, 1983, 1990, 1999, 2008, and 2014), and four LiDAR (light detection and ranging) datasets (acquired in 2006, 2009, 2012, and 2018): obtained results, in terms of emerged surfaces variations, are linked to the available land subsidence rates (provided by leveling, GPS—global positioning system, and SAR—synthetic aperture radar data) and to the expected SLR values, to perform scenarios of the area by 2100: results of this work will be useful to mitigate the hazard by increasing defense systems and preventing the risk of widespread flooding. Numéro de notice : A2021-199 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/rs13030529 Date de publication en ligne : 02/02/2021 En ligne : https://doi.org/10.3390/rs13030529 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97151
in Remote sensing > Vol 13 n° 3 (February 2021) . - n° 529[article]Connecting images through time and sources: Introducing low-data, heterogeneous instance retrieval / Dimitri Gominski (2021)
Titre : Connecting images through time and sources: Introducing low-data, heterogeneous instance retrieval Type de document : Article/Communication Auteurs : Dimitri Gominski , Auteur ; Valérie Gouet-Brunet , Auteur ; Liming Chen, Auteur Editeur : Ithaca [New York - Etats-Unis] : ArXiv - Université Cornell Année de publication : 2021 Projets : Alegoria / Gouet-Brunet, Valérie Importance : 5 p. Format : 21 x 30 cm Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage profond
[Termes IGN] base de données d'images
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] descripteur
[Termes IGN] données d'entrainement (apprentissage automatique)
[Termes IGN] données hétérogènes
[Termes IGN] exploration de données
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] image multi sources
[Termes IGN] indexation sémantique
[Termes IGN] précision de la classification
[Termes IGN] recherche d'image basée sur le contenuRésumé : (auteur) With impressive results in applications relying on feature learning, deep learning has also blurred the line between algorithm and data. Pick a training dataset, pick a backbone network for feature extraction, and voilà; this usually works fora variety of use cases. But the underlying hypothesis that there exists a training dataset matching the use case is not alwaysmet. Moreover, the demand for interconnections regardless of the variations of the content calls for increasing generalization and robustness in features. An interesting application characterized by these problematics is the connection of historical and cultural databases of images.Through the seemingly simple task of instance retrieval, wepropose to show that it is not trivial to pick features respondingwell to a panel of variations and semantic content. Introducing anew enhanced version of the ALEGORIA benchmark, we compare descriptors using the detailed annotations. We further give in sights about the core problems in instance retrieval, testing fourstate-of-the-art additional techniques to increase performance. Numéro de notice : P2021-001 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Thématique : IMAGERIE Nature : Preprint nature-HAL : Préprint DOI : 10.48550/arXiv.2103.10729 Date de publication en ligne : 21/03/2021 En ligne : https://doi.org/10.48550/arXiv.2103.10729 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97398
Titre : Création d’un portail de datavisualisation open source Type de document : Mémoire Auteurs : Benjamin Fau, Auteur Editeur : Champs-sur-Marne : Ecole nationale des sciences géographiques ENSG Année de publication : 2021 Importance : 80 p. Format : 21 x 30 cm Note générale : Bibliographie
Rapport de fin d'étude, cycle Ingénieur 3e année, master CarthagéoLangues : Français (fre) Descripteur : [Vedettes matières IGN] Géomatique web
[Termes IGN] analyse des besoins
[Termes IGN] entrepôt de données localisées
[Termes IGN] jeu de données localisées
[Termes IGN] logiciel libre
[Termes IGN] partage de données localisées
[Termes IGN] portail
[Termes IGN] serveur cartographique (programme)
[Termes IGN] utilisateur
[Termes IGN] visualisation de donnéesIndex. décimale : DCAR Mémoires de l'ex DESS cartographie et du Master CARTHAGEO Résumé : (Auteur) Le monde de la datavisualisation est riche en outils et propose de nombreuses solutions pour qui veut organiser et mettre en forme ses données. Parmi ces solutions, un certain nombre ont fait le choix de suivre les principes du logiciel libre, ou open source. Ces derniers donnent à l’ensemble de la communauté une liberté concernant la visualisation, l’utilisation et la modification du code. Ces outils ont chacun leur domaine d’application, leurs avantages et limites. Ce stage se propose de sélectionner et d’adapter un ensemble de solutions de datavisualisation open source, traitant des données géographiques ou non-géographiques, afin de les faire fonctionner au sein d’un environnement cohérent capable de répondre à une grande diversité de besoins. Note de contenu :
Introduction
1. Présentation du stage
2. Solutions de datavisualisation composant le portail
3. Développement du portail
4. Résultats
ConclusionNuméro de notice : 26698 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Mémoire de fin d'études IT Organisme de stage : Magellium Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99106 Documents numériques
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Titre : Deep learning for feature based image matching Type de document : Thèse/HDR Auteurs : Lin Chen, Auteur ; Christian Heipke, Directeur de thèse Editeur : Munich : Bayerische Akademie der Wissenschaften Année de publication : 2021 Collection : DGK - C, ISSN 0065-5325 num. 867 Importance : 159 p. Format : 21 x 30 cm Note générale : bibliographie
Diese Arbeit ist gleichzeitig veröffentlicht in: Wissenschaftliche Arbeiten der Fachrichtung Geodäsie und Geoinformatik der Leibniz UniversitätHannoverISSN 0174-1454, Nr. 369, Hannover 2021Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Photogrammétrie numérique
[Termes IGN] appariement d'images
[Termes IGN] chaîne de traitement
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] descripteur
[Termes IGN] image aérienne oblique
[Termes IGN] orientation d'image
[Termes IGN] orthoimageRésumé : (auteur) Feature based image matching aims at finding matched features between two or more images. It is one of the most fundamental research topics in photogrammetry and computer vision. The matching features area prerequisite for applications such as image orientation, Simultaneous Localization and Mapping (SLAM) and robot vision. A typical feature based matching algorithm is composed of five steps: feature detection, affine shape estimation, orientation, description and descriptor matching. Today, the employment of deep neural network has framed those different steps as machine learning problems and the matching performance has been improved significantly. One of the main reasons why feature based image matching may still prove difficult is the complex change between different images, including geometric and radiometric transformations. If the change between images exceeds a certain level, it will also exceed the tolerance of those aforementioned separate steps and, in turn, cause feature based image matching to fail.
This thesis focuses on improving feature based image matching against large viewpoint and viewing direction change between images. In order to improve the feature based image matching performance under these circumstances, affine shape estimation, orientation and description are solved with deep learning architectures. In particular, Convolutional Neural Networks (CNN) are used. For the affine shape and orientation learning, the main contribution of this thesis is two fold. First, instead of a Siamese CNN, only one branch is needed and the loss is built based on the geometric measures calculated from the mean gradient or second moment matrix. Therefore, for each of the input patches, a global minimum, namely the canonical feature, exists. Second, both the affine shape and orientation are solved simultaneously within one network by combining the loss used for affine shape and orientation learning. To the best of the author’s knowledge, this is the first time these two modules are reported to have been successfully trained simultaneously. For the descriptor learning part, a new weak match is defined. For any input feature patch, a slightly transformed patch that lies far from the input feature patch in descriptor space is defined as a weak match feature. A weak match finder network is proposed to actively find these weak match features. In a following step, the found weak matches are used in the standard descriptor learning framework. In this way, the intra-variance of the appearance of matched feature patch pairs is explored in depth and, accordingly, the invariance of feature descriptors against viewpoint and viewing direction change is improved. The proposed feature based image matching method is evaluated on standard benchmarks and is used to solve for the parameters of image orientation. For the image orientation task, aerial oblique images are taken into account. Through analysis of the experiments conducted for small image blocks, it is shown that deep learning feature based image matching leads to more registered images, more reconstructed 3D points and a more stable block connection.Note de contenu : 1- Introduction
2- Basics
3- Related work
4- Deep learning feature representation
5- Experiments and results
6- Discussion
7- Conclusion and outlookNuméro de notice : 17673 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Thèse étrangère Note de thèse : PhD dissertation : Fachrichtung Geodäsie und Geoinformatik : Hanovre : 2021 En ligne : https://dgk.badw.de/fileadmin/user_upload/Files/DGK/docs/c-867.pdf Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97999 Improving image description with auxiliary modality for visual localization in challenging conditions / Nathan Piasco in International journal of computer vision, vol 29 n° 1 (January 2021)PermalinkUne méthodologie et un outil d'évaluation du niveau de "FAIRness" pour les ressources sémantiques : le cas d'AgroPortal / Emna Amdouni (2021)PermalinkPermalinkMise en place d’une infrastructure de données spatiales sur le risque de piqures de tiques / Lilian Calas (2021)PermalinkPermalinkPermalinkSensor tasking for search and catalog maintenance of geosynchronous space objects / Han Cai in Acta Astronautica, vol 175 (October 2020)PermalinkUrban Wi-Fi fingerprinting along a public transport route / Guenther Retscher in Journal of applied geodesy, vol 14 n° 4 (October 2020)PermalinkVolunteered geographic information research in the first decade: a narrative review of selected journal articles in GIScience / Yingwei Yan in International journal of geographical information science IJGIS, vol 34 n° 9 (September 2020)PermalinkInteroperable information model for geovisualization and interaction in XR environments / Daeil Seo in International journal of geographical information science IJGIS, vol 34 n° 7 (July 2020)Permalink