Détail de l'auteur
Auteur Jamal Jokar Arsanjani |
Documents disponibles écrits par cet auteur (9)
Ajouter le résultat dans votre panier Affiner la recherche Interroger des sources externesvol V-4-2021 - July 2021 - [actes] XXIV ISPRS Congress "Imaging today, foreseeing tomorrow", Commission 4, 2021 edition, 5–9 July 2021 (Bulletin de ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences) / Nicolas Paparoditis
Deep learning for detecting and classifying ocean objects: application of YoloV3 for iceberg–ship discrimination / Frederik Hass in ISPRS International journal of geo-information, vol 9 n° 12 (December 2020)
Titre : Deep learning for detecting and classifying ocean objects: application of YoloV3 for iceberg–ship discrimination Type de document : Article/Communication Auteurs : Frederik Hass, Auteur ; Jamal Jokar Arsanjani, Auteur Année de publication : 2020 Article en page(s) : n° 758 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] apprentissage profond
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] Groenland
[Termes IGN] hydrocarbure
[Termes IGN] iceberg
[Termes IGN] image radar moirée
[Termes IGN] image Sentinel-SAR
[Termes IGN] navire
[Termes IGN] océan
[Termes IGN] seuillage d'image
[Termes IGN] trafic maritimeRésumé : (auteur) Synthetic aperture radar (SAR) plays a remarkable role in ocean surveillance, with capabilities of detecting oil spills, icebergs, and marine traffic both at daytime and at night, regardless of clouds and extreme weather conditions. The detection of ocean objects using SAR relies on well-established methods, mostly adaptive thresholding algorithms. In most waters, the dominant ocean objects are ships, whereas in arctic waters the vast majority of objects are icebergs drifting in the ocean and can be mistaken for ships in terms of navigation and ocean surveillance. Since these objects can look very much alike in SAR images, the determination of what objects actually are still relies on manual detection and human interpretation. With the increasing interest in the arctic regions for marine transportation, it is crucial to develop novel approaches for automatic monitoring of the traffic in these waters with satellite data. Hence, this study aims at proposing a deep learning model based on YoloV3 for discriminating icebergs and ships, which could be used for mapping ocean objects ahead of a journey. Using dual-polarization Sentinel-1 data, we pilot-tested our approach on a case study in Greenland. Our findings reveal that our approach is capable of training a deep learning model with reliable detection accuracy. Our methodical approach along with the choice of data and classifiers can be of great importance to climate change researchers, shipping industries and biodiversity analysts. The main difficulties were faced in the creation of training data in the Arctic waters and we concluded that future work must focus on issues regarding training data. Numéro de notice : A2020-808 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi9120758 Date de publication en ligne : 19/12/2020 En ligne : https://doi.org/10.3390/ijgi9120758 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96953
in ISPRS International journal of geo-information > vol 9 n° 12 (December 2020) . - n° 758[article]
est un bulletin de ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences / International society for photogrammetry and remote sensing (1980 -) (2012 - )
Titre : vol V-4-2020 - August 2020 - [Actes] XXIV ISPRS virtual Congress, Commission 4, 31th August-2nd September 2020, Nice, France Type de document : Périodique Auteurs : Nicolas Paparoditis Année de publication : 2020 Conférence : ISPRS 2020, Commission 4, virtual Congress, Imaging today foreseeing tomorrow 31/08/2020 02/09/2020 Nice (en ligne) France Annals Commission 4 Langues : Anglais (eng) Numéro de notice : sans Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Nature : Numéro de périodique nature-HAL : DirectOuvrColl/Actes En ligne : https://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/V-4-2020/ Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=bulletin_display&id=32713 [n° ou bulletin]
- Opportunities and challenges for augmented reality situated geographical visualization / María-Jesús Lobo in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-4-2020 (August 2020)
- A regression model of spatial accuracy prediction for Openstreetmap buildings / Ibrahim Maidaneh Abdi in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-4-2020 (August 2020)
Titre : Citizen empowered mapping Type de document : Monographie Auteurs : Michael Leitner, Éditeur scientifique ; Jamal Jokar Arsanjani, Éditeur scientifique Editeur : Berlin, Heidelberg, Vienne, New York, ... : Springer Année de publication : 2017 Collection : Geotechnologies and the Environment, ISSN 2365-0575 num. 18 Importance : 300 p. ISBN/ISSN/EAN : 978-3-319-51629-5 Langues : Anglais (eng) Numéro de notice : 22747 Affiliation des auteurs : non IGN Nature : Recueil / ouvrage collectif En ligne : http://dx.doi.org/10.1007/978-3-319-51629-5 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=86169 Mapping and the citizen sensor, ch 10. The relevance of protocols for VGI collection / Marco Minghini (2017)
Titre de série : Mapping and the citizen sensor, ch 10 Titre : The relevance of protocols for VGI collection Type de document : Chapitre/Contribution Auteurs : Marco Minghini, Auteur ; Vyron Antoniou, Auteur ; Cidália Costa Fonte, Auteur ; Jacinto Estima, Auteur ; Ana-Maria Olteanu-Raimond Editeur : Londres : Ubiquity press Année de publication : 2017 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web
[Termes IGN] acquisition de données
[Termes IGN] données localisées des bénévoles
[Termes IGN] protocole
[Termes IGN] qualité des donnéesRésumé : (auteur) Volunteered Geographic Information (VGI) has become a rich and well established source of geospatial data. From the popular OpenStreetMap (OSM) to many citizen science projects and social network platforms, the amount of geographically referenced information that is constantly being generated by citizens is burgeoning. The main issue that continues to hamper the full exploitation of VGI lies in its quality, which is by its nature typically undocumented and can range from very high quality to very poor. A crucial step towards improving VGI quality, which impacts on VGI usability, is the development and adoption of protocols, guidelines and best practices to assist users when collecting VGI. This chapter proposes a generic and flexible protocol for VGI data collection, which can be applied to new as well as to existing projects regardless of the specific type of geospatial information collected. The protocol is meant to balance the contrasting needs of providing VGI contributors with precise and detailed instructions while maintaining and growing the enthusiasm and motivation of contributors. Two real-world applications of the protocol are presented, which guide the collection of VGI in respectively the generation and updating of thematic information in a topographic building database; and the uploading of geotagged photographs for the improvement of land use and land cover maps. Technology is highlighted as a key factor in determining the success of the protocol implementation. Numéro de notice : H2017-020 Affiliation des auteurs : LASTIG COGIT+Ext (2012-2019) Thématique : GEOMATIQUE Nature : Chapître / contribution nature-HAL : ChOuvrScient DOI : 10.5334/bbf.j Date de publication en ligne : 11/09/2017 En ligne : https://doi.org/10.5334/bbf.j Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89340
