TH1.R15.1

ASSESSMENT OF DEEP LEARNING MODELS TRAINED USING GLOBAL REMOTE SENSING IMAGERY IN REAL-CONTEXT EMERGENCY RESPONSE

Sesa Wiguna, Bruno Adriano, Erick Mas, Shunichi Koshimura, Tohoku University, Japan

Session:
TH1.R15: Machine Learning and Remote Sensing Data for Rapid Disaster Response I Oral

Track:
Community-Contributed Sessions

Location:
Mercury Hall

Presentation Time:
Thu, 11 Jul, 09:00 - 09:14

Session Co-Chairs:
Marc Wieland, German Aerospace Center (DLR) and Nina Merkle, German Aerospace Center (DLR)
Presentation
Discussion
Resources
No resources available.
Session TH1.R15
TH1.R15.1: ASSESSMENT OF DEEP LEARNING MODELS TRAINED USING GLOBAL REMOTE SENSING IMAGERY IN REAL-CONTEXT EMERGENCY RESPONSE
Sesa Wiguna, Bruno Adriano, Erick Mas, Shunichi Koshimura, Tohoku University, Japan
TH1.R15.2: Conditional experts for improved building damage assessment across satellite imagery view angles
Philipe Ambrozio Dias, Jacob Arndt, Marie Urban, Dalton Lunga, Oak Ridge National Laboratory, United States
TH1.R15.3: Disaster Damage Visualization by VLM-based Interactive Image Retrieval and Cross-View Image Geo-Localization
Naoya Sogi, Takashi Shibata, Makoto Terao, Kenta Senzaki, Masahiro Tani, NEC Corporation, Japan; Royston Rodrigues, NEC Asia Pacific Pte Ltd, Singapore
TH1.R15.4: COMPARATIVE ANALYSIS OF DETAILED FEATURES IN 3D MODELS FOR SAR SIMULATION
Chia Yee Ho, Tohoku University, Japan; Erick Mas, Bruno Adriano, Shunichi Koshimura, IRIDeS, Japan
TH1.R15.5: UNIVERSAL AND AUTOMATED METHODS FOR DETECTING FOREST SURFACE ANOMALIES
Junying Song, Xiufang Zhu, Rui Guo, Beijing Normal University, China
TH1.R15.6: ADVANCED MACHINE LEARNING STRATEGIES FOR LANDSLIDE DETECTION
Mohammad Amin Khalili, University of Naples, Italy; Behzad Voosoghi, K.N.TOOSI University of Technology, Iran; Domenico Calcaterra, University of Naples, Italy; Amirbahador Kouchakkapourchali, Tehran University, Iran; Chiara Di Muro, University of Naples, Italy; Sadegh Madadi, Kharazmi University, Iran; Rita Tufano, Diego Di Martire, University of Naples, Italy
TH1.R15.7: UNCERTAINTY ESTIMATION IN SAR-BASED FLOOD MAPPING VIA DENSITY-AWARE DEEP NEURAL NETWORKS
Yu Li, Patrick Matgen, Marco Chini, Luxembourg Institute of Science and Technology (LIST), Luxembourg
Resources
No resources available.