Erick Mas Samanez

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Associate Professor

Tohoku University

Japan

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About

Dr. Erick Mas Samanez is an Associate Professor at Tohoku University, Japan. His research primarily focuses on disaster response, specifically in the areas of tsunami evacuation, building damage assessment, and the use of social media data for real-time analysis. Recent publications include studies on tsunami inundation related to the 2024 Noto Peninsula Earthquake and advancements in machine learning for emergency response applications.

Recent Grants

Grant: Close

Increasing Urban Resilience to Large Scale Disasters: The Development of a Dynamic Integrated Model for DIsaster Management and Socio-Economic Analysis (DIM2SEA)

Open Date: 2015-12-01

Close Date: 2019-03-01

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Erick Mas Samanez

Research Interests

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Contact this professor

About

Recent Grants

Articles (21)

Understanding the relationship between building damage and tsunami inundation due to the 2024 Noto Peninsula Earthquake

Towards real-time extraction of cascading effect and spatiotemporal analysis using social media data

Understanding Tsunami Evacuation via a Social Force Model While Considering Stress Levels Using Agent-Based Modelling

Building Damage Mapping of the 2024 Noto Peninsula Earthquake, Japan, Using Semi-Supervised Learning and VHR Optical Imagery

Evaluation of Deep Learning Models for Building Damage Mapping in Emergency Response Settings

Exploring the Feasibility of Ray Tracing SAR Simulation on Building Damage Assessment

Feasibility of anomalous event detection based on Mobile Spatial Statistics: A study of six cases in Japan

A Study on Digital Model for Decision-Making in Crisis Response

Reinforcement learning-based tsunami evacuation guidance system

Evaluation of Simulated SAR images for building damage classification

Estimation of the Seismic Source of the 1974 Lima Peru Earthquake and Tsunami (Mw 8.1)

Brief communication: Radar images for monitoring informal urban settlements in vulnerable zones in Lima, Peru

A Reinforcement Learning Model of Multiple UAVs for Transporting Emergency Relief Supplies

Sparse Representation-Based Inundation Depth Estimation Using SAR Data and Digital Elevation Model

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

Applicability of Calculation Formulae of Impact Force by Tsunami Driftage

The Potential Role of News Media to Construct a Machine Learning Based Damage Mapping Framework

Pyramid Pooling Module-Based Semi-Siamese Network: A Benchmark Model for Assessing Building Damage from xBD Satellite Imagery Datasets

Technical Solution Discussion for Key Challenges of Operational Convolutional Neural Network-Based Building-Damage Assessment from Satellite Imagery: Perspective from Benchmark xBD Dataset

Learning from the 2018 Western Japan Heavy Rains to Detect Floods during the 2019 Hagibis Typhoon

Tsunami Damage Detection with Remote Sensing: A Review

Collaborators (5)

Shunichi Koshimura

Bruno Adriano

Han Soo Lee

Akihiro Shibayama

Fumio Yamazaki

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