FR2.R11.1

AN INTERPRETABLE NEURAL NETWORK ALGORITHM FOR LEAKING DETECTION IN THE URBAN WATER AND SEWER PIPELINE NETWORK, TIANJIN, CHINA

Xujie Le, Hanwen Yu, Yong Wang, University of Electronic Science and Technology of China (UESTC), China
Session:
FR2.R11: SAR Advanced Applications Based on Deep Learning: Urban, Land and Water Applications I Oral
Track:
Community-Contributed Sessions
Location:
Room 106
Presentation Time:
Fri, 21 Jul, 10:15 - 10:27 Pacific Time (UTC -7)
Session Co-Chairs:
Hanwen Yu, University of Electronic Science and Technology of China and Giampaolo Ferraioli, Università di Napoli Parthenope
Session Manager:
HUMAIRA SANAM
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Session FR2.R11
FR2.R11.1: AN INTERPRETABLE NEURAL NETWORK ALGORITHM FOR LEAKING DETECTION IN THE URBAN WATER AND SEWER PIPELINE NETWORK, TIANJIN, CHINA
Xujie Le, Hanwen Yu, Yong Wang, University of Electronic Science and Technology of China (UESTC), China
FR2.R11.2: SAR IMAGE GENERATION BY INTEGRATING DIFFERENTIABLE SAR RENDERER WITH NEURAL NETWORKS
Hecheng Jia, Yanni Wang, Shilei Fu, Feng Xu, Fudan University, China
FR2.R11.3: SYNERGIC USE OF SAR AND OPTICAL DATA FOR FEATURE EXTRACTION
Antonio Mazza, Matteo Ciotola, University Federico II, Italy; Giovanni Poggi, Università degli Studi di Napoli Federico II, Italy; Giuseppe Scarpa, University Parthenope, Italy
FR2.R11.4: ANALYTIC V.S. LEARNING: SOLVING DIFFERENTIAL TOMOGRAPHIC SAR INVERSION USING HYPERLISTA
Kun Qian, Yuanyuan Wang, Peter Jung, Xiao Xiang Zhu, Technical University of Munich, Germany
FR2.R11.5: Unsupervised SAR Images for Submesoscale Oceanic Eddy Detection
Grace Vincent, North Carolina State University, United States; Kai Pak, Diego Martinez, Edwin Goh, Jinbo Wang, Brian Bue, Benjamin Holt, Brian Wilson, California Institute of Technology, United States
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