Knowledge Models and Image Processing Analysis in Remote Sensing: Examples of Yakutsk (Russia) and Kaunas (Lithuania)

• Published in: Proceedings of the 5th International Conference on Geographical Information Systems Theory, Applications and Management
• Main Authors: Gadal, Sébastien, Ouerghemmi, Walid
• Other Authors: 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), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Avignon Université (AU)-Aix Marseille Université (AMU)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA), CNES CES THEIA Artificialisation et Urbanisation, INSTICC, University of Crete, IEEE GRSS, ACM SIGSPATIAL, Cédric Grueau, Robert Laurini, Lemonia Ragia, 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
• Language: English
• Published: HAL CCSD 2019
• Subjects: 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]; [SHS.STAT]Humanities and Social Sciences/Methods and statistics; [SDE.ES]Environmental Sciences/Environmental and Society; [SHS.GEO]Humanities and Social Sciences/Geography; [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation
• Online Access: https://hal-amu.archives-ouvertes.fr/hal-02120100; https://hal-amu.archives-ouvertes.fr/hal-02120100/document; https://hal-amu.archives-ouvertes.fr/hal-02120100/file/article2_HAL.pdf; https://doi.org/10.5220/0007752202820288

International audience The use of geographic knowledge in remote sensing constitutes one of the fundamental base 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 example 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 knowledge’s 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.