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Disambiguation of "Mas, Erick"

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Tsunami evacuation simulation - case studies for tsunami mitigation at Indonesia, Thailand and Japan.

, , , , , and . SIMULTECH, page 249-254. IEEE, (2014)

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Stephan Mäs

Stephan Mäs

Sandra Erickson

Susan J Erickson

Erick Stoll

 

Other publications of authors with the same name

Technical Solution Discussion for Key Challenges of Operational Convolutional Neural Network-Based Building-Damage Assessment from Satellite Imagery: Perspective from Benchmark xBD Dataset., , , , , , , and . Remote. Sens., 12 (22): 3808 (2020)Novel Unsupervised Classification of Collapsed Buildings Using Satellite Imagery, Hazard Scenarios and Fragility Functions., , , , , and . Remote. Sens., 10 (2): 296 (2018)Developing a method for urban damage mapping using radar signatures of building footprint in SAR imagery: A case study after the 2013 Super Typhoon Haiyan., , , , , and . IGARSS, page 3579-3582. IEEE, (2015)Sparse Representation-Based Inundation Depth Estimation Using SAR Data and Digital Elevation Model., , and . IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens., (2022)Evaluation of Deep Learning Models for Building Damage Mapping in Emergency Response Settings., , , and . IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens., (2024)Flood Inundation Depth Estimation from SAR-Based Flood Extent and DEM., , and . IGARSS, page 337-340. IEEE, (2023)Optimizing the Post-disaster Resource Allocation with Q-Learning: Demonstration of 2021 China Flood., , , and . DEXA (2), volume 13427 of Lecture Notes in Computer Science, page 256-262. Springer, (2022)Towards Operational Satellite-Based Damage-Mapping Using U-Net Convolutional Network: A Case Study of 2011 Tohoku Earthquake-Tsunami., , and . Remote Sensing, 10 (10): 1626 (2018)Enhancement of Detecting Permanent Water and Temporary Water in Flood Disasters by Fusing Sentinel-1 and Sentinel-2 Imagery Using Deep Learning Algorithms: Demonstration of Sen1Floods11 Benchmark Datasets., , , , , , , , and . Remote. Sens., 13 (11): 2220 (2021)Disaster Intensity-Based Selection of Training Samples for Remote Sensing Building Damage Classification., , , , , and . IEEE Trans. Geosci. Remote. Sens., 59 (10): 8288-8304 (2021)