Knowledge Models and Image Processing Analysis in Remote Sensing: Examples of Yakutsk (Russia) and Kaunas (Lithuania)
International audience The use of geographic knowledge in remote sensing constitutes one of the fundamental bases of the methodologies of image processing. Image processing, image analysis, and oriented-object recognition are based on the geographic knowledge. More specifically, the large panel of s...
Published in: | Proceedings of the 5th International Conference on Geographical Information Systems Theory, Applications and Management |
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ftanrparis:oai:HAL:hal-02120100v1 2024-09-30T14:30:39+00:00 Knowledge Models and Image Processing Analysis in Remote Sensing: Examples of Yakutsk (Russia) and Kaunas (Lithuania) Gadal, Sébastien Ouerghemmi, Walid Aix Marseille Université (AMU) Études des Structures, des Processus d’Adaptation et des Changements de l’Espace (ESPACE) Université Nice Sophia Antipolis (1965 - 2019) (UNS)-Avignon Université (AU)-Aix Marseille Université (AMU)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UniCA) CNES CES THEIA Artificialisation et Urbanisation ANR-15-CE22-0006,PUR,Pôles URbains(2015) ANR-14-CE22-0016,HYEP,Imagerie hyperspectrale pour la planification urbaine environnementale(2014) ANR-10-EQPX-0020,GEOSUD,GEOSUD : Infrastructure nationale d'imagerie satellitaire pour la recherche sur l'environnement et les territoires et ses applications à la gestion et aux politiques publiques(2010) 2019-05-03 https://amu.hal.science/hal-02120100 https://amu.hal.science/hal-02120100/document https://amu.hal.science/hal-02120100/file/article2_HAL.pdf https://doi.org/10.5220/0007752202820288 en eng HAL CCSD info:eu-repo/semantics/altIdentifier/doi/10.5220/0007752202820288 hal-02120100 https://amu.hal.science/hal-02120100 https://amu.hal.science/hal-02120100/document https://amu.hal.science/hal-02120100/file/article2_HAL.pdf doi:10.5220/0007752202820288 http://creativecommons.org/licenses/by-nc/ info:eu-repo/semantics/OpenAccess 5th International Conference on Geographical Information Systems Theory, Applications and Management https://amu.hal.science/hal-02120100 5th International Conference on Geographical Information Systems Theory, Applications and Management, 1 (ISBN 978-989-758-371-1), pp.282-288, 2019, Proceedings of the 5th International Conference on Geographical Information Systems Theory, Applications and Management, ⟨10.5220/0007752202820288⟩ Russia Kaunas Arctic Lithuania Temporal Analysis Yakutsk Artificial Intelligence Geographic Knowledge Remote Sensing Spectral Databases Morphometric attributes Geographic Ontologies [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV] [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation [SDE.ES]Environmental Sciences/Environment and Society [SHS.GEO]Humanities and Social Sciences/Geography [SHS.STAT]Humanities and Social Sciences/Methods and statistics info:eu-repo/semantics/other Proceedings 2019 ftanrparis https://doi.org/10.5220/0007752202820288 2024-09-11T23:45:10Z International audience The use of geographic knowledge in remote sensing constitutes one of the fundamental bases of the methodologies of image processing. Image processing, image analysis, and oriented-object recognition are based on the geographic knowledge. More specifically, the large panel of supervised classifications methods are one of the main examples where geographic knowledge is necessary for both algorithms training and results validation. Recently, with the coming back of the artificial intelligence (AI) wave, it appears that a large spectrum of usually employed methodologies in remote sensing and image processing, are one of the main drivers of AI: machine learning, deep learning are the most effective’s examples. As well as many based processing algorithms like the Support Vector Machine (SVM) or the Random Forest (RF). However, despite the constant performances of the methods of calculus; the geographic knowledges determines the accuracy of recognition and classification in image processing and spatial modelling generated. In regard of the fast seasonal and annual landscape changes in the Arctic climate, and complex urban structures, Yakutsk and Kaunas cities contribute to the reflexion. Conference Object Arctic Yakutsk Portail HAL-ANR (Agence Nationale de la Recherche) Arctic Yakutsk Proceedings of the 5th International Conference on Geographical Information Systems Theory, Applications and Management 282 288 |
institution |
Open Polar |
collection |
Portail HAL-ANR (Agence Nationale de la Recherche) |
op_collection_id |
ftanrparis |
language |
English |
topic |
Russia Kaunas Arctic Lithuania Temporal Analysis Yakutsk Artificial Intelligence Geographic Knowledge Remote Sensing Spectral Databases Morphometric attributes Geographic Ontologies [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV] [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation [SDE.ES]Environmental Sciences/Environment and Society [SHS.GEO]Humanities and Social Sciences/Geography [SHS.STAT]Humanities and Social Sciences/Methods and statistics |
spellingShingle |
Russia Kaunas Arctic Lithuania Temporal Analysis Yakutsk Artificial Intelligence Geographic Knowledge Remote Sensing Spectral Databases Morphometric attributes Geographic Ontologies [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV] [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation [SDE.ES]Environmental Sciences/Environment and Society [SHS.GEO]Humanities and Social Sciences/Geography [SHS.STAT]Humanities and Social Sciences/Methods and statistics Gadal, Sébastien Ouerghemmi, Walid Knowledge Models and Image Processing Analysis in Remote Sensing: Examples of Yakutsk (Russia) and Kaunas (Lithuania) |
topic_facet |
Russia Kaunas Arctic Lithuania Temporal Analysis Yakutsk Artificial Intelligence Geographic Knowledge Remote Sensing Spectral Databases Morphometric attributes Geographic Ontologies [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV] [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation [SDE.ES]Environmental Sciences/Environment and Society [SHS.GEO]Humanities and Social Sciences/Geography [SHS.STAT]Humanities and Social Sciences/Methods and statistics |
description |
International audience The use of geographic knowledge in remote sensing constitutes one of the fundamental bases of the methodologies of image processing. Image processing, image analysis, and oriented-object recognition are based on the geographic knowledge. More specifically, the large panel of supervised classifications methods are one of the main examples where geographic knowledge is necessary for both algorithms training and results validation. Recently, with the coming back of the artificial intelligence (AI) wave, it appears that a large spectrum of usually employed methodologies in remote sensing and image processing, are one of the main drivers of AI: machine learning, deep learning are the most effective’s examples. As well as many based processing algorithms like the Support Vector Machine (SVM) or the Random Forest (RF). However, despite the constant performances of the methods of calculus; the geographic knowledges determines the accuracy of recognition and classification in image processing and spatial modelling generated. In regard of the fast seasonal and annual landscape changes in the Arctic climate, and complex urban structures, Yakutsk and Kaunas cities contribute to the reflexion. |
author2 |
Aix Marseille Université (AMU) Études des Structures, des Processus d’Adaptation et des Changements de l’Espace (ESPACE) Université Nice Sophia Antipolis (1965 - 2019) (UNS)-Avignon Université (AU)-Aix Marseille Université (AMU)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UniCA) CNES CES THEIA Artificialisation et Urbanisation ANR-15-CE22-0006,PUR,Pôles URbains(2015) ANR-14-CE22-0016,HYEP,Imagerie hyperspectrale pour la planification urbaine environnementale(2014) ANR-10-EQPX-0020,GEOSUD,GEOSUD : Infrastructure nationale d'imagerie satellitaire pour la recherche sur l'environnement et les territoires et ses applications à la gestion et aux politiques publiques(2010) |
format |
Conference Object |
author |
Gadal, Sébastien Ouerghemmi, Walid |
author_facet |
Gadal, Sébastien Ouerghemmi, Walid |
author_sort |
Gadal, Sébastien |
title |
Knowledge Models and Image Processing Analysis in Remote Sensing: Examples of Yakutsk (Russia) and Kaunas (Lithuania) |
title_short |
Knowledge Models and Image Processing Analysis in Remote Sensing: Examples of Yakutsk (Russia) and Kaunas (Lithuania) |
title_full |
Knowledge Models and Image Processing Analysis in Remote Sensing: Examples of Yakutsk (Russia) and Kaunas (Lithuania) |
title_fullStr |
Knowledge Models and Image Processing Analysis in Remote Sensing: Examples of Yakutsk (Russia) and Kaunas (Lithuania) |
title_full_unstemmed |
Knowledge Models and Image Processing Analysis in Remote Sensing: Examples of Yakutsk (Russia) and Kaunas (Lithuania) |
title_sort |
knowledge models and image processing analysis in remote sensing: examples of yakutsk (russia) and kaunas (lithuania) |
publisher |
HAL CCSD |
publishDate |
2019 |
url |
https://amu.hal.science/hal-02120100 https://amu.hal.science/hal-02120100/document https://amu.hal.science/hal-02120100/file/article2_HAL.pdf https://doi.org/10.5220/0007752202820288 |
geographic |
Arctic Yakutsk |
geographic_facet |
Arctic Yakutsk |
genre |
Arctic Yakutsk |
genre_facet |
Arctic Yakutsk |
op_source |
5th International Conference on Geographical Information Systems Theory, Applications and Management https://amu.hal.science/hal-02120100 5th International Conference on Geographical Information Systems Theory, Applications and Management, 1 (ISBN 978-989-758-371-1), pp.282-288, 2019, Proceedings of the 5th International Conference on Geographical Information Systems Theory, Applications and Management, ⟨10.5220/0007752202820288⟩ |
op_relation |
info:eu-repo/semantics/altIdentifier/doi/10.5220/0007752202820288 hal-02120100 https://amu.hal.science/hal-02120100 https://amu.hal.science/hal-02120100/document https://amu.hal.science/hal-02120100/file/article2_HAL.pdf doi:10.5220/0007752202820288 |
op_rights |
http://creativecommons.org/licenses/by-nc/ info:eu-repo/semantics/OpenAccess |
op_doi |
https://doi.org/10.5220/0007752202820288 |
container_title |
Proceedings of the 5th International Conference on Geographical Information Systems Theory, Applications and Management |
container_start_page |
282 |
op_container_end_page |
288 |
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