MO3.M1.2
Improving Forest Burned Area Mapping Using Weak Labels and Probabilistic Deep Learning
Benyamin Hosseiny, University of Tehran, Iran; Pouria Ramzi, Government of Western Australia, Australia; Saeid Homayouni, Institut National de la Recherche Scientifique, Canada
Session:
MO3.M1: New Frontiers in Satellite Remote Sensing for Geohazard Monitoring: Advanced Algorithms to Real-world Applications I Oral
Track:
Community-Contributed Sessions
Location:
Mezzanine: Room M1
Presentation Time:
Monday, 4 August, 13:15 - 13:30
Session Co-Chairs:
Pietro Mastro , IREA-CNR and Pouria Ramzi, Senior research scientist/Government of Western Australia
Session MO3.M1
MO3.M1.1: A universal adapter in segmentation models for transferable landslide mapping
Ruilong Wei, Institute of Mountain Hazards and Environment, China; Yamei Li, Institute of Tibetan Plateau Research, China; Yao Li, Tsinghua University, China; Bo Zhang, Jiao Wang, Chunhao Wu, Institute of Mountain Hazards and Environment, China; Shunyu Yao, China Institute of Water Resources and Hydropower Research, China; Chengming Ye, Chengdu University of Technology, China
MO3.M1.2: Improving Forest Burned Area Mapping Using Weak Labels and Probabilistic Deep Learning
Benyamin Hosseiny, University of Tehran, Iran; Pouria Ramzi, Government of Western Australia, Australia; Saeid Homayouni, Institut National de la Recherche Scientifique, Canada
MO3.M1.3: DROUGHT CAUSAL ANALYSIS BASED ON REMOTE SENSING DATA AND CONVOLUTIONAL NEURAL NETWORKS
Ming Wang, Lei He, Han Luo, Chengdu University of Information Technology, China; Yuxia Li, University of Electronic Science and Technology of China, China; Yunhao He, Yuheng Lei, Chengdu University of Information Technology, China
MO3.M1.4: Generating Electromagnetic Satellite Synthetic Data for Detecting Seismic Precursors
Yaxin Bi, Ulster University, United Kingdom; Wei Zhai, Gansu Earthquake Agency, China; Maja Pavlovic, Peter Nicholl, Ulster University, United Kingdom
Resources