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International Geoscience and Remote Sensing Symposium

In 2021 a joint initiative of Belgium and The Netherlands

TU2.MM-25.2

CYCLONE IDENTIFY USING TWO-BRANCH CONVOLUTIONAL NEURAL NETWORK FROM GLOBAL FORECASTING SYSTEM ANALYSIS

Fan Meng, Qingyu Tian, China University of Petroleum (East China), China; Handan Sun, China University of Petroleum, China; Danya Xu, Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), China; Tao Song, China University of Petroleum (East China), China
Session:
Machine Learning Methods in Hazard Assessment
Track:
Special Themes
Presentation Time:
Tue, 13 Jul, 11:05-11:10 (UTC)
Tue, 13 Jul, 13:05-13:10 Central Europe Summer Time (UTC +2)
Tue, 13 Jul, 19:05-19:10 China Standard Time (UTC +8)
Tue, 13 Jul, 07:05-07:10 Eastern Daylight Time (UTC -4)
Session Co-Chairs:
Fan Meng, China University of Petroleum(East China) and Davide De Santis, University of Rome
Session Manager:
Mar Ariza
Presentation
Discussion
Resources
Session TU2.MM-25
TU2.MM-25.1: Automatic Detection of Widely Distributed Local-scale Subsidence Bowls in Rapidly Urbanizing Metropolitan Region using Time-series InSAR and Deep Learning Methods
Zherong Wu, Zhuoyi Zhao, Yi Zheng, Peifeng Ma, Chinese University of Hong Kong, China
TU2.MM-25.2: CYCLONE IDENTIFY USING TWO-BRANCH CONVOLUTIONAL NEURAL NETWORK FROM GLOBAL FORECASTING SYSTEM ANALYSIS
Fan Meng, Qingyu Tian, China University of Petroleum (East China), China; Handan Sun, China University of Petroleum, China; Danya Xu, Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), China; Tao Song, China University of Petroleum (East China), China
TU2.MM-25.3: TROPICAL CYCLONE SIZE ESTIMATION USING DEEP CONVOLUTIONAL NEURAL NETWORK
Fan Meng, Pengfei Xie, Ying Li, China University of Petroleum (East China), China; Handan Sun, China University of Petroleum, China; Danya Xu, Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), China; Tao Song, China University of Petroleum (East China), China
TU2.MM-25.4: USE ENSEMBLE LEARNING TO ESTIMATE THE POPULATION AND ASSETS EXPOSED TO TROPICAL CYCLONES
Fan Meng, China University of Petroleum (East China), China; Tongmao Ma, Polytechnical University of Madrid, Spain; Pengfei Xie, China University of Petroleum (East China), China; Handan Sun, China University of Petroleum, China; Danya Xu, Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), China; Tao Song, China University of Petroleum (East China), China
TU2.MM-25.5: VOLCANIC SO2 NEAR-REAL TIME RETRIEVAL USING TROPOMI DATA AND NEURAL NETWORKS: THE DECEMBER 2018 ETNA TEST CASE
Davide De Santis, Ilaria Petracca, University of Rome, Italy; Stefano Corradini, Lorenzo Guerrieri, Istituto Nazionale di Geofisica e Vulcanologia, Italy; Matteo Picchiani, GEO-K s.r.l, Italy; Luca Merucci, Dario Stelitano, Istituto Nazionale di Geofisica e Vulcanologia, Italy; Fabio Del Frate, University of Rome, Italy; Fred Prata, AIRES Pty Ltd., Australia; Giovanni Schiavon, University of Rome, Italy
TU2.MM-25.6: Towards improved forecasting of volcanic hazards using machine learning applied to InSAR data
Andrew Hooper, Matt Gaddes, University of Leeds, United Kingdom; Marco Bagnardi, NASA, United States; Fabien Albino, University of Bristol, United Kingdom
TU2.MM-25.7: A MACHINE LEARNING METHODOLOGY FOR NEXT DAY WILDFIRE PREDICTION
Stella Girtsou, Alexis Apostolakis, National Observatory of Athens, Greece; Giorgos Giannopoulos, Athena Research Center, Greece; Charalampos Kontoes, National Observatory of Athens, Greece
TU2.MM-25.8: SEMI-SUPERVISED PHENOLOGY ESTIMATION IN COTTON PARCELS WITH SENTINEL-2 TIME-SERIES
Vasileios Sitokonstantinou, Alkiviadis Koukos, Charalampos Kontoes, Nikolaos S. Bartsotas, National Observatory of Athens, Greece; Vassilia Karathanassi, National Technical University of Athens, Greece
TU2.MM-25.9: Water Body Detection using Deep Learning with Sentinel-1 SAR satellite data and Land Cover Maps
Hyungyun Jeon, Duk-jin Kim, Junwoo Kim, Seoul National University, Korea (South)
TU2.MM-25.10: Deep Reinforcement Learning Interdependent Healthcare Critical Infrastructure Simulation model for Dynamically Varying COVID-19 scenario – A case study of a Metro City
Srikanth Gollavilli, Nivedita Nukavarapu, Surya Durbha, Indian Institute of Technology Bombay, India

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