Spatial modelling of the Arctic metapolis: Yakutsk

• Main Authors: GADAL, Sébastien, Oukhattar, Mounir, Kamičaitytė, Jūratė, Zakharov, Moisei, Ouerghemmi, W.
• Other Authors: North-Eastern Federal University, Études des Structures, des Processus d’Adaptation et des Changements de l’Espace (ESPACE), Université Côte d'Azur (UCA)-Avignon Université (AU)-Université Nice Sophia Antipolis (. - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Aix Marseille Université (AMU), Aix Marseille Université (AMU), Kaunas University of Technology (KTU), Centre National de la Recherche Scientifique (CNRS), North Eastern Federal University, Yuri Danilov, CNES AIM-CEE, ANR-15-CE22-0006,PUR,Pôles URbains(2015)
• Format: Other/Unknown Material
• Language: English
• Published: HAL CCSD 2021
• Subjects: Metropolisation; Adaptation; Simulation; Urban change modelling; Global warming; Multi-level analysis; Extreme environment; Remote Sensing; Machine learning; Deep learning; Yakutsk; Arctic; Russia; geo; hist
• Online Access: https://hal.archives-ouvertes.fr/hal-03502208

International audience The process of metropolisation of Yakutsk is one of the most unique geographic phenomena. Since the end of the Soviet Union, the city of 193000 inhabitants growing fastly to cross in 2021 up to 33000 people covering today almost 1 million square kilometres in the extreme Arctic environment and climate. The population of the Yakutsk metropolis developed strategies of adaptation we are going to analyse. Scenarios of possible transformations of the metropolis including the impacts of global warming have been developed at different scales and temporalities (Landsat Series, Sentinel 2, DMSP, and VIIRS) based on machine learning and deep learning processing.