Details of the Researcher

PHOTO

Adriano Ortega Bruno
Section
International Research Institute of Disaster Science
Job title
Associate Professor
Degree

  • Ph.D. (Tohoku University)

  • MPs. (National Graduate Institute for Policy Studies)

  • B.Eng. (National University of Engineering, Peru)

Profile

Dr. Bruno Adriano is an associate professor at the Disaster Geoinformatics Lab, International Research Institute of Disaster Science (IRIDeS), and a concurrent associate professor at the Graduate School of Civil and Environmental Engineering, both at Tohoku University. He is also a visiting research scientist with the Geoinformatics Team at the RIKEN Center for Advanced Intelligence Project (AIP).

His research focuses on developing intelligent systems that analyze the complex physical characteristics and impacts of disasters, such as earthquakes, tsunamis, floods, and landslides, using multimodal observations and physics-based simulations. Situated at the intersection of remote sensing, machine learning, and numerical modeling, he develops data-driven methods that integrate computer vision, machine learning, and data fusion to produce actionable, scalable information for disaster response and planning.

Research History 4

  • 2023/05 - Present
    RIKEN Center for Advanced Intelligence Project (AIP)　Visiting Scientist

  • 2023/02 - Present
    Tohoku University　Associate Professor

  • 2022/04 - 2023/01
    RIKEN Center for Advanced Intelligence Project (AIP)　Research Scientist

  • 2018/06 - 2022/03
    RIKEN Center for Advanced Intelligence Project (AIP)　Postdoctoral Researcher

Education 1

  • Tohoku University　Graduate School of Engineering

    2013/04 - 2016/03

Professional Memberships 4

  • American Geoscience Union

  • JAPAN GEOSCIENCE UNION

  • JAPAN SOCIETY OF CIVIL ENGINEERS

  • IEEE GRSS

Research Interests 4

  • Machine Learning

  • Disaster Management

  • Numerical Simulation

  • Remote Sensing

Research Areas 3

  • Environmental science/Agricultural science / Environmental load/risk assessment /

  • Natural sciences / Solid earth science /

  • Social infrastructure (civil Engineering, architecture, disaster prevention) / Disaster prevention engineering /

Papers 103

  1. Leveraging LLMs for rapid disaster impacts assessment through News Media: A case study of the 2025 Myanmar Earthquake

    Ruben Vescovo, Xuanyan Dong, Sesa Wiguna, Chia Yee Ho, Bruno Adriano, Erick Mas, Shunichi Koshimura

    International Journal of Disaster Risk Reduction　2026/07

    DOI： 10.1016/j.ijdrr.2026.106195 　

  2. A Simplified Methodology for Tsunami Casualty Estimation Using Geospatial Analysis and Numerical Simulation

    Angel Quesquen, Carlos Davila, Fernando Garcia, Marcello Palomino, Jorge Morales, Erick Mas, Bruno Adriano, Erika Flores, Miguel Estrada

    Environmental and Earth Sciences Proceedings　2026/05

    DOI： 10.3390/eesp2026041007 　

  3. Comparative Analysis of PALSAR-2 and Geographical Features for Mapping Urban and Non-Urban Flooded Areas

    Ryosuke Nagato, Ira Karrel San Jose, Sesa Wiguna, Ryohei Kametaka, Bruno Adriano, Erick Mas, Shunichi Koshimura

    Journal of Disaster Research　2026/02/01

    DOI： 10.20965/jdr.2026.p0201 　

  4. How accurate is accurate enough? Assessing the influence of exposure data quality on tsunami damage modelling using the 2011 Tōhoku tsunami

    Christian Geiβ, Miyo-Maria Haug, Marc Wieland, Bruno Adriano, Patrick Aravena Pelizari, Hannes Taubenböck, Shunichi Koshimura

    2026

    DOI： 10.2139/ssrn.7127925 　

  5. Extraction of Flooded Buildings From a Single SAR Image: A Double-Bounce-Aware Spatial Analysis Approach

    Ryohei Kametaka, Bruno Adriano, Erick Mas, Shunichi Koshimura

    IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing　2026

    DOI： 10.1109/JSTARS.2026.3703826 　

  6. Evaluation of open-source SAR-based flood datasets for flood extent mapping in emergency settings

    Ira Karrel San Jose, Sesa Wiguna, Ryohei Kametaka, Bruno Adriano, Erick Mas, Shunichi Koshimura

    Progress in Disaster Science　2026/01

    DOI： 10.1016/j.pdisas.2025.100507 　

  7. Integrating GAN-Generated SAR and Optical Imagery for Building Damage Mapping

    Chia Yee Ho, Bruno Adriano, Gerald Baier, Erick Mas, Sesa Wiguna, Magaly Koch, Shunichi Koshimura

    Remote Sensing　2025/12/31

    DOI： 10.3390/rs18010134 　

  8. Estimation of Building Heights in Peru from High-Resolution Stereo Optical Satellite Imagery

    Wen Liu, Mamoru Kamegawa, Bruno Adriano, Hiroyuki Miura, Masashi Matsuoka, Italo Inocente, Fernando Garcia, Jorge Morales, Miguel Diaz, Miguel Estrada

    Journal of Disaster Research　2025/12/01

    DOI： 10.20965/jdr.2025.p1023 　

  9. Parallel Computing Approach for Rapid Estimation of Tsunami Hazard and Population Exposure in Peru

    Fernando Garcia, Miguel Estrada, Julian Palacios, Carlos Davila, Angel Quesquen, Jorge Morales, Bruno Adriano, Erick Mas, Shunichi Koshimura

    Journal of Disaster Research　2025/12/01

    DOI： 10.20965/jdr.2025.p0912 　

  10. Design and Implementation of an Open-Source Web-Based GIS System for Early Earthquake Damage Estimation in Lima, Peru

    Italo Inocente, Miguel Diaz, Masashi Matsuoka, Yoshihisa Maruyama, Hiroyuki Miura, Wen Liu, Bruno Adriano, Juan C. Tarazona, Carlos Zavala, Jhianpiere Salinas

    Journal of Disaster Research　2025/12/01

    DOI： 10.20965/jdr.2025.p1034 　

  11. ABIC-based joint inversion using tsunami, GNSS and SAR data: finite fault model of the 2024 Noto Peninsula earthquake, Japan

    Ayumu Mizutani, Bruno Adriano, Erick Mas, Yusaku Ohta, Shunichi Koshimura

    Geophysical Journal International　2025/11/11

    DOI： 10.1093/gji/ggaf432 　

  12. Accurate flood extent mapping in suburban areas using a single SAR image: FFT-based artifact removal approach

    Ryohei Kametaka, Bruno Adriano, Erick Mas, Shunichi Koshimura

    International Journal of Applied Earth Observation and Geoinformation　2025/11

    DOI： 10.1016/j.jag.2025.104941 　

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

    Bruno Adriano, Hideomi Gokon, Ayumu Mizutani, Erick Mas, Shunichi Koshimura

    Ocean Engineering　2025/11

    DOI： 10.1016/j.oceaneng.2025.122179 　

  14. Accurate Digital Reconstruction of High-Steep Rock Slope via Transformer-Based Multi-Sensor Data Fusion

    Changqing Liu, Han Bao, Jingfeng Zhang, Hengxing Lan, Bruno Adriano, Shunichi Koshimura, Wei Yuan

    Remote Sensing　2025/10/28

    DOI： 10.3390/rs17213555 　

  15. Tsunami pedestrian evacuation simulation for Camaná, Peru: Perspectives for improving evacuation performance

    Jheyder Perez, Luis Moya, Julio Ramirez, Edgard Gonzales, Erick Mas, Bruno Adriano, Shunichi Koshimura

    E3S Web of Conferences　651　02011-02011　2025/10/14

    Publisher: EDP Sciences

    DOI： 10.1051/e3sconf/202565102011 　

    eISSN： 2267-1242

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    Optimizing pedestrian evacuation in the face of a tsunami remains a critical challenge for safeguarding human lives. Agent-based models combined with reinforcement learning techniques offer a powerful framework to simulate complex evacuation scenarios, where agents learn to make decisions and identify safe routes in real time. This study focuses on improving evacuation efficiency along the coast of Camaná, Arequipa, Peru. We propose the use of numerical simulations to model pedestrian movement under the guidance of a reinforcement learning-based system. Under current transportation network conditions, only 16.6% of the population is able to reach a safe area in a tsunami scenario similar to the 2001 event. To address this, several modifications to the transportation network were proposed, including the addition of new evacuation paths and the construction of vertical evacuation structures. With the incorporation of 12 new paths and 6 vertical evacuation structures, the percentage of the population reaching safety increases to 73%. These findings provide a scientific basis for planning and implementing improvements to evacuation infrastructure in tsunami-prone areas.

  16. Multi-source 3D point clouds fusion for potential rock mass hazard evaluation in high-steep rock slopes

    Wei Yuan, Changqing Liu, Tianyi Wang, Bruno Adriano, Han Bao, Ryosuke Shibasaki, Shunichi Koshimura

    The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences　XLVIII-G-2025　1663-1668　2025/08/02

    Publisher: Copernicus GmbH

    DOI： 10.5194/isprs-archives-xlviii-g-2025-1663-2025 　

    eISSN： 2194-9034

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    Abstract. Accurate characterization and evaluation of hazardous rockmass sources prove essential for rockfall risk mitigation. Structural properties of rock masses play a decisive role in evaluating these risks. This study presents an integrated approach that combines Terrestrial Laser Scanning (TLS) and Unmanned Aerial Vehicle (UAV) photogrammetry to address data limitations in complex terrain. The practical validation was carried out on the basis of a case study on a high and steep rock slope. The results demonstrate that the fusion of TLS-UAV multi-source data enhances spatial coverage and point cloud density by 19%, enabling comprehensive slope modeling and improving multi-angle structural characterization of target rock masses. An approach integrating multiple algorithms enables the automatic identification of rock joints from multi-source 3D point clouds, achieving high recognition accuracy. And the key geometric and mechanical parameters were extracted and analyzed to quantify joint properties. Furthermore, a novel rock hazard index (RHI) is proposed, which takes into account joint geometric features, joint mechanical features, and slope quality grade to assess risk levels across slope domains. The proposed framework provides an efficient solution for joint-controlled hazardous rockmass assessment, offering theoretical insights and practical applications for infrastructure-related geohazard prevention. This study contributes to enhancing risk assessment methodologies for high and steep slope environments.

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

    Xuanyan Dong, Erick Mas, Bruno Adriano, Shunichi Koshimura

    International Journal of Disaster Risk Reduction　2025/07

    DOI： 10.1016/j.ijdrr.2025.105512 　

  18. Improving Indonesia's tsunami early warning. Part II: Hybridized deep learning and metaheuristic algorithm for forecasting and optimizing

    Muhammad Rizki Purnama, Bruno Adriano, Elisa Lahcene, Anawat Suppasri, Fumihiko Imamura, Mohammad Farid, Mohammad Bagus Adityawan

    Ocean Engineering　2025/07

    DOI： 10.1016/j.oceaneng.2025.121496 　

  19. Estimation of high-resolution tsunami inundation depth using deep learning models: Case study of Pangandaran, Indonesia

    Sesar P.D. Sriyanto, Bruno Adriano, Yushiro Fujii, Shunichi Koshimura

    Ocean Engineering　2025/06

    DOI： 10.1016/j.oceaneng.2025.121019 　

  20. Revising the seismic source of the 1979 Tumaco-Colombia earthquake (Mw = 8.1) for future tsunami hazard assessment Peer-reviewed

    Bruno Adriano, Cesar Jimenez, Erick Mas, Shunichi Koshimura

    Physics of the Earth and Planetary Interiors　362　107344-107344　2025/05

    Publisher: Elsevier BV

    DOI： 10.1016/j.pepi.2025.107344 　

    ISSN： 0031-9201

  21. The 2024 Noto Peninsula earthquake building damage dataset: Multi-source visual assessment Peer-reviewed

    Ruben Vescovo, Bruno Adriano, Sesa Wiguna, Chia Yee Ho, Jorge Morales, Xuanyan Dong, Shin Ishii, Kazuki Wako, Yudai Ezaki, Ayumu Mizutani, Erick Mas, Satoshi Tanaka, Shunichi Koshimura

    Earth Syst. Sci. Data Discuss. [preprint]　2025/03/05

    Publisher: Copernicus GmbH

    DOI： 10.5194/essd-2024-363 　

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    Abstract. We present a building damage dataset following the 2024 Noto Peninsula Earthquake. The database was compiled from freely available, multi-source, remote sensing data, verified through opt-in crowd-sourced information. The dataset consists of geo-referenced vector polygons representing the pre-event building footprints of 140,208 structures. Each building was classified through visual inspection using pre-disaster and post disaster vertical, oblique, survey, and verifiable news reporting imagery. Entries were validated using voluntary-submission data sourced through a web-API hosting a live version of the database. We calculate classification metrics for a subset of the database where ground survey photographs were provided by independent surveyors. An average F1-score of 0.94 suggests that the proposed assessment is consistent and high quality. We aim to inform future disaster research such as disaster dynamics models; statistical and machine learning damage models; logistics and evacuation studies. The present work describes the data collection process, damage assessment methodology, and rationale; including limitations encountered, the crowd sourcing validation process, and the dataset structure.

  22. Evaluation of Simulated SAR Images for Building Damage Classification

    Yudai Ezaki, Chia Yee Ho, Bruno Adriano, Erick Mas, Shunichi Koshimura

    IEEE Geoscience and Remote Sensing Letters　2025

    DOI： 10.1109/LGRS.2024.3520251 　

  23. The Performance of the Optical Flow Field based Dense Image Matching for UAV Imagery

    Wei Yuan, Weihang Ran, Bruno Adriano, Ryosuke Shibasaki, Shunichi Koshimura

    ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences　X-4-2024　433-440　2024/10/18

    Publisher: Copernicus GmbH

    DOI： 10.5194/isprs-annals-x-4-2024-433-2024 　

    eISSN： 2194-9050

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    Abstract. With the rapid development of sensing platforms, unmanned aerial vehicle (UAV)-based mapping has become increasingly popular because of its economic efficiency and flexibility, especially for providing 3D information to support urban growth monitoring and change detection to meet sustainable development goals (SDGs). This paper presents an improved optical flow field-based dense matching algorithm (OFFDM) for low-altitude UAV images based on the Ph.D. thesis of Yuan (Yuan, 2018). First, high-precision seed points were used to compute the optical flow field within stereo pairs, effectively minimizing redundant calculations during the fine-matching phase. Second, a fine-matching approach, integrating multiple constraints, was applied to refine the coarse matching results based on the optical flow field. Extensive dense matching experiments on UAV low-altitude aerial imagery assessed the performance of OFFDIM across four dimensions: 3D point cloud visualization, matching success rate, precision, and reliability. Extensive experiments on low-altitude UAV imagery, characterized by a resolution of 7cm per pixel over a 10,608×8,608 pixel dimension and a 60% forward overlap, evaluate the OFFDM's efficacy. The quantitative evaluation revealed that the proposed method achieved an accuracy of ±0.7 pixels in image coordinates and ±20 cm on the ground, with a matching success rate exceeding 97%. The processing time was approximately 272 seconds for handling one single stereo pair. When compared to the widely adopted PMVS algorithm, known for its effectiveness in dense matching for UAV images, the proposed method demonstrated higher completeness and improved matching efficiency by more than five times. These results demonstrated that the proposed approach is more suitable for dense matching on UAV imagery-based high-precision 3D spatial data extraction, supporting global mapping tasks more effectively.

  24. Tracing the sources of paleotsunamis using Bayesian frameworks

    Erick R. Velasco-Reyes, Daisuke Sugawara, Bruno Adriano

    Communications Earth & Environment　5　(1)　2024/09/02

    Publisher: Springer Science and Business Media LLC

    DOI： 10.1038/s43247-024-01643-w 　

    eISSN： 2662-4435

  25. Fault Model of the 2024 Noto Peninsula Earthquake Based on Aftershock, Tsunami, and GNSS Data

    Ayumu Mizutani, Bruno Adriano, Erick Mas, Shunichi Koshimura

    2024/04/08

    Publisher: Research Square Platform LLC

    DOI： 10.21203/rs.3.rs-4167995/v1 　

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    <title>Abstract</title> On January 1, 2024, at 16:10 (local time), a magnitude (Mw) 7.5 earthquake occurred on the Noto Peninsula, Japan. Japan Meteorological Agency seismic intensity scale of 7 was observed and a tsunami warning was issued for a wide area along the Japan Sea. In this study, we constructed the finite fault model of this earthquake. First, we estimated the fault geometry based on the aftershock within 7 days after the mainshock, and then, estimated the slip distribution using the tsunami and GNSS data. Most of the obtained fault geometry agreed with the one detected by the Japan Sea earthquake and tsunami project (JSPJ) in the strike angle but not in the dip angle; the angles were more gradual. The slip distribution had two large slip areas on the east and west sides of the hypocenter. The model also indicated that the eastern part of the fault, or the fault buried under the sea floor, generated the tsunami which was dominant east of the peninsula. In the west of the peninsula, on the other hand, the tsunami generated by the westernmost fault, or the inland fault under the peninsula, hit first, and then the tsunami propagating from the east of the peninsula arrived.

  26. The 2004 Noto Peninsula Earthquake Tsunami - It's Generation, Propagation, Inundation, and Impact

    Shunichi Koshimura, Bruno Adriano, Ayumu Mizutani, Erick Mas, Yusaku Ohta, Shohei Nagata, Yuriko Takeda, Ruben Vescovo, Sesa Wiguna, Takashi Abe, Takayuki Suzuki

    2024/03/11

    DOI： 10.5194/egusphere-egu24-22527 　

  27. The Impact of the 2024 Noto Peninsula Earthquake Tsunami

    Shunichi Koshimura, Bruno Adriano, Ayumu Mizutani, Erick Mas, Yusaku Ohta, Shohei Nagata, Yuriko Takeda, Ruben Vescovo, Sesa Wiguna, Takashi Abe, Takayuki Suzuki

    2024/03/11

    DOI： 10.5194/egusphere-egu24-22523 　

  28. Tsunami Digital Twin &#8211; Concept, Progress, and Application to the 2024 Noto Peninsula Earthquake Tsunami Disaster, Japan

    Shunichi Koshimura, Bruno Adriano, Erick Mas, Shohei Nagata, Yuriko Takeda

    2024/03/09

    DOI： 10.5194/egusphere-egu24-14673 　

  29. COMPARATIVE ANALYSIS OF DETAILED FEATURES IN 3D MODELS FOR SAR SIMULATION

    Chia Yee Ho, Erick Mas, Bruno Adriano, Shunichi Koshimura

    IGARSS 2024-2024 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM, IGARSS 2024　1750-1754　2024

    DOI： 10.1109/IGARSS53475.2024.10640983 　

    ISSN： 2153-6996

  30. URBAN VULNERABILITY ANALYSIS IN THE TRIBUTARY BASIN OF THE RIMAC RIVER, PERU USING HIGH-RESOLUTION REMOTE SENSING IMAGERY

    Bruno Adriano, Luis Moya, Erick Mas, Hiroyuki Miura, Masashi Matsuoka, Shunichi Koshimura

    IGARSS 2024-2024 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM, IGARSS 2024　3635-3638　2024

    DOI： 10.1109/IGARSS53475.2024.10642715 　

    ISSN： 2153-6996

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

    Sesa Wiguna, Bruno Adriano, Erick Mas, Shunichi Koshimura

    IGARSS 2024-2024 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM, IGARSS 2024　1736-1740　2024

    DOI： 10.1109/IGARSS53475.2024.10641821 　

    ISSN： 2153-6996

  32. Exploring the feasibility of Ray Tracing SAR simulation on building damage assessment

    Chia Yee Ho, Erick Mas, Bruno Adriano, Shunichi Koshimura

    IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing　1-15　2024

    Publisher: Institute of Electrical and Electronics Engineers (IEEE)

    DOI： 10.1109/jstars.2024.3418412 　

    ISSN： 1939-1404

    eISSN： 2151-1535

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

    Sesa Wiguna, Bruno Adriano, Ruben Vescovo, Erick Mas, Ayumu Mizutani, Shunichi Koshimura

    IEEE Geoscience and Remote Sensing Letters　21　1-5　2024

    Publisher: Institute of Electrical and Electronics Engineers (IEEE)

    DOI： 10.1109/lgrs.2024.3407725 　

    ISSN： 1545-598X

    eISSN： 1558-0571

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

    Sesa Wiguna, Bruno Adriano, Erick Mas, Shunichi Koshimura

    IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing　2024

    DOI： 10.1109/JSTARS.2024.3367853 　

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

    Cesar Jimenez, Jorge Morales, Miguel Estrada, Bruno Adriano, Erick Mas, Shunichi Koshimura

    Journal of Disaster Research　2023/12/01

    DOI： 10.20965/jdr.2023.p0825 　

  36. Tsunami wave characteristics in Sendai Bay, Japan, following the 2016 Mw 6.9 Fukushima earthquake

    An-Chi Cheng, Anawat Suppasri, Mohammad Heidarzadeh, Bruno Adriano, Constance Ting Chua, Fumihiko Imamura

    Ocean Engineering　2023/11

    DOI： 10.1016/j.oceaneng.2023.115676 　

  37. Beyond tsunami fragility functions: experimental assessment for building damage estimation

    Ruben Vescovo, Bruno Adriano, Erick Mas, Shunichi Koshimura

    Scientific Reports　13　(1)　2023/08/31

    Publisher: Springer Science and Business Media LLC

    DOI： 10.1038/s41598-023-41047-y 　

    eISSN： 2045-2322

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    Abstract Tsunami fragility functions (TFF) are statistical models that relate a tsunami intensity measure to a given building damage state, expressed as cumulative probability. Advances in computational and data retrieval speeds, coupled with novel deep learning applications to disaster science, have shifted research focus away from statistical estimators. TFFs offer a “disaster signature” with comparative value, though these models are seldom applied to generate damage estimates. With applicability in mind, we challenge this notion and investigate a portion of TFF literature, selecting three TFFs and two application methodologies to generate a building damage estimation baseline. Further, we propose a simple machine learning method, trained on physical parameters inspired by, but expanded beyond, TFF intensity measures. We test these three methods on the 2011 Ishinomaki dataset after the Great East Japan Earthquake and Tsunami in both binary and multi-class cases. We explore: (1) the quality of building damage estimation using TFF application methods; (2) whether TFF can generalize to out-of-domain building damage datasets; (3) a novel machine learning approach to perform the same task. Our findings suggest that: both TFF methods and our model have the potential to achieve good binary results; TFF methods struggle with multiple classes and out-of-domain tasks, while our proposed method appears to generalize better.

  38. Combining Deep Learning and Numerical Simulation to Predict Flood Inundation Depth

    Bruno Adriano, Naoto Yokoya, Kazuki Yamanoi, Satoru Oishi

    IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium　2023/07/16

    Publisher: IEEE

    DOI： 10.1109/igarss52108.2023.10282463 　

  39. Revising the 2007 Peru Earthquake Damage Monitoring Using Machine Learning Models and Satellite Imagery

    Bruno Adriano, Hiroyuki Miura, Wen Liu, Masashi Matsuoka, Eduardo Portuguez, Miguel Diaz, Miguel Estrada

    Journal of Disaster Research　2023/06/01

    DOI： 10.20965/jdr.2023.p0379 　

  40. National high-resolution cropland classification of Japan with agricultural census information and multi-temporal multi-modality datasets

    Junshi Xia, Naoto Yokoya, Bruno Adriano, Keiichiro Kanemoto

    International Journal of Applied Earth Observation and Geoinformation　117　103193-103193　2023/03

    Publisher: Elsevier BV

    DOI： 10.1016/j.jag.2023.103193 　

    ISSN： 1569-8432

  41. OpenEarthMap: A Benchmark Dataset for Global High-Resolution Land Cover Mapping.

    Junshi Xia, Naoto Yokoya, Bruno Adriano, Clifford Broni-Bediako

    IEEE/CVF Winter Conference on Applications of Computer Vision(WACV)　6243-6253　2023

    Publisher: IEEE

    DOI： 10.1109/WACV56688.2023.00619 　

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

    Luis Moya, Fernando Garcia, Carlos Gonzales, Miguel Diaz, Carlos Zavala, Miguel Estrada, Fumio Yamazaki, Shunichi Koshimura, Erick Mas, Bruno Adriano

    Natural Hazards and Earth System Sciences　22　(1)　65-70　2022/01/12

    Publisher: Copernicus GmbH

    DOI： 10.5194/nhess-22-65-2022 　

    eISSN： 1684-9981

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    Abstract. Lima, Peru's capital, has about 9.6 million inhabitants and keeps attracting more residents searching for a better life. Many citizens, without access to housing subsidies, live in informal housing and shack settlements. A typical social phenomenon in Lima is the sudden illegal occupation of areas for urban settlements. When such areas are unsafe against natural hazards, it is important to relocate such a population to avoid significant future losses. In this communication, we present an application of Sentinel-1 synthetic aperture radar (SAR) images to map the extension of a recent occupation of an area with unfavorable soil conditions against earthquakes.

  43. OpenEarthMap: A Benchmark Dataset for Global High-Resolution Land Cover Mapping.

    Junshi Xia, Naoto Yokoya, Bruno Adriano, Clifford Broni-Bediako

    CoRR　abs/2210.10732　2022

    DOI： 10.48550/arXiv.2210.10732 　

  44. "Disaster Detection from SAR Images with Different Off-Nadir Angles Using Unsupervised Image Translation.

    Jian Song, Bruno Adriano, Naoto Yokoya

    Proceedings of the Second Workshop on Complex Data Challenges in Earth Observation (CDCEO 2022) co-located with 31st International Joint Conference on Artificial Intelligence and the 25th European Conference on Artificial Intelligence (IJCAI-ECAI 2022)(CDCEO@IJCAI)　14-20　2022

    Publisher: CEUR-WS.org

  45. Predicting Flood Inundation Depth Based-on Machine Learning and Numerical Simulation

    Bruno Adriano, Naoto Yokoya, Kazuki Yamanoi, Satoru Oishi

    CEUR Workshop Proceedings　3207　58-64　2022

    ISSN： 1613-0073

  46. Learning from multimodal and multitemporal earth observation data for building damage mapping

    Bruno Adriano, Naoto Yokoya, Junshi Xia, Hiroyuki Miura, Wen Liu, Masashi Matsuoka, Shunichi Koshimura

    ISPRS Journal of Photogrammetry and Remote Sensing　175　132-143　2021/05

    Publisher: Elsevier BV

    DOI： 10.1016/j.isprsjprs.2021.02.016 　

    ISSN： 0924-2716

  47. Learning from multimodal and multitemporal earth observation data for building damage mapping

    Bruno Adriano, Naoto Yokoya, Junshi Xia, Hiroyuki Miura, Wen Liu, Masashi Matsuoka, Shunichi Koshimura

    ISPRS Journal of Photogrammetry and Remote Sensing　175　132-143　2021/05

    DOI： 10.1016/j.isprsjprs.2021.02.016 　

    ISSN： 0924-2716

  48. Tsunami damage estimation in Esmeraldas, Ecuador using fragility functions

    Teresa Vera San Martin, Leonardo Gutierrez, Mario Palacios, Erick Mas, Adriano Bruno, Shunichi Koshimura

    AIMS GEOSCIENCES　7　(4)　669-694　2021

    DOI： 10.3934/geosci.2021040 　

    ISSN： 2471-2132

  49. Building Damage Mapping with Self-PositiveUnlabeled Learning.

    Junshi Xia, Naoto Yokoya, Bruno Adriano

    CoRR　abs/2111.02586　2021

  50. A Benchmark High-Resolution GaoFen-3 SAR Dataset for Building Semantic Segmentation

    Junshi Xia, Naoto Yokoya, Bruno Adriano, Lianchong Zhang, Guoqing Li, Zhigang Wang

    IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING　14　5950-5963　2021

    DOI： 10.1109/JSTARS.2021.3085122 　

    ISSN： 1939-1404

    eISSN： 2151-1535

  51. Tsunami hazard assessment for the central and southern pacific coast of Colombia

    Ronald Sanchez Escobar, Luis Otero Diaz, Anlly Melissa Guerrero, Milton Puentes Galindo, Erick Mas, Shunichi Koshimura, Bruno Adriano, Luisa Urra, Paola Quintero

    Coastal Engineering Journal　1-13　2020/09/23

    Publisher: Informa UK Limited

    DOI： 10.1080/21664250.2020.1818362 　

    ISSN： 2166-4250

    eISSN： 1793-6292

  52. Characteristics of Tsunami Fragility Functions Developed Using Different Sources of Damage Data from the 2018 Sulawesi Earthquake and Tsunami Peer-reviewed

    Erick Mas, Ryan Paulik, Kwanchai Pakoksung, Bruno Adriano, Luis Moya, Anawat Suppasri, Abdul Muhari, Rokhis Khomarudin, Naoto Yokoya, Masashi Matsuoka, Shunichi Koshimura

    PURE AND APPLIED GEOPHYSICS　177　(6)　2437-2455　2020/06

    DOI： 10.1007/s00024-020-02501-4 　

    ISSN： 0033-4553

    eISSN： 1420-9136

  53. Detecting urban changes using phase correlation and l(1)-based sparse model for early disaster response: A case study of the 2018 Sulawesi Indonesia earthquake-tsunami Peer-reviewed

    Luis Moya, Abdul Muhari, Bruno Adriano, Shunichi Koshimura, Erick Mas, Luis R. Marval-Perez, Naoto Yokoya

    REMOTE SENSING OF ENVIRONMENT　242　2020/06

    DOI： 10.1016/j.rse.2020.111743 　

    ISSN： 0034-4257

    eISSN： 1879-0704

  54. A Semiautomatic Pixel-Object Method for Detecting Landslides Using Multitemporal ALOS-2 Intensity Images Peer-reviewed

    Bruno Adriano, Naoto Yokoya, Hiroyuki Miura, Masashi Matsuoka, Shunichi Koshimura

    Remote Sensing　2020/02

    DOI： 10.3390/rs12030561 　

  55. Breaking Limits of Remote Sensing by Deep Learning From Simulated Data for Flood and Debris-Flow Mapping

    Naoto Yokoya, Kazuki Yamanoi, Wei He, Gerald Baier, Bruno Adriano, Hiroyuki Miura, Satoru Oishi

    IEEE Transactions on Geoscience and Remote Sensing　1-15　2020

    Publisher: Institute of Electrical and Electronics Engineers (IEEE)

    DOI： 10.1109/tgrs.2020.3035469 　

    ISSN： 0196-2892

    eISSN： 1558-0644

  56. Damage Characterization in Urban Environments from Multitemporal Remote Sensing Datasets Built from Previous Events.

    Bruno Adriano, Junshi Xia, Naoto Yokoya, Hiroyuki Miura, Masashi Matsuoka, Shunichi Koshimura

    IEEE International Geoscience and Remote Sensing Symposium(IGARSS)　3751-3754　2020

    Publisher: IEEE

    DOI： 10.1109/IGARSS39084.2020.9323415 　

  57. Cross-Domain-Classification of Tsunami Damage Via Data Simulation and Residual-Network-Derived Features from Multi-Source Images Peer-reviewed

    Bruno Adriano, Naoto Yokoya, Junshi Xia, Gerald Baier, Shunichi Koshimura

    International Geoscience and Remote Sensing Symposium (IGARSS)　4947-4950　2019/07

    DOI： 10.1109/IGARSS.2019.8899155 　

  58. Multi-Source Data Fusion Based on Ensemble Learning for Rapid Building Damage Mapping during the 2018 Sulawesi Earthquake and Tsunami in Palu, Indonesia Peer-reviewed

    Bruno Adriano, Junshi Xia, Gerald Baier, Naoto Yokoya, Shunichi Koshimura

    Remote Sensing　2019/04

    DOI： 10.3390/rs11070886 　

  59. Robust Nonlocal Low-Rank Sar Stack Despeckling With Application To Change Detection.

    Gerald Baier, Wei He 0003, Bruno Adriano, Junshi Xia, Naoto Yokoya

    2019 IEEE International Geoscience and Remote Sensing Symposium(IGARSS)　5205-5208　2019

    Publisher: IEEE

    DOI： 10.1109/IGARSS.2019.8900331 　

  60. Building Damage Mapping Via Transfer Learning. Peer-reviewed

    Junshi Xia, Bruno Adriano, Gerald Baier, Naoto Yokoya

    2019 IEEE International Geoscience and Remote Sensing Symposium(IGARSS)　4841-4844　2019

    Publisher: IEEE

    DOI： 10.1109/IGARSS.2019.8900447 　

  61. BUILDING DAMAGE MAPPING VIA TRANSFER LEARNING Peer-reviewed

    Junshi Xia, Bruno Adriano, Gerald Baier, Naoto Yokoya

    2019 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS 2019)　4841-4844　2019

    ISSN： 2153-6996

  62. New Insights into Multiclass Damage Classification of Tsunami-Induced Building Damage from SAR Images Peer-reviewed

    Yukio Endo, Bruno Adriano, Erick Mas, Shunichi Koshimura

    Remote Sensing　2018/12

    DOI： 10.3390/rs10122059 　

  63. Tsunami source and inundation features around Sendai Coast, Japan, due to the November 22, 2016 Mw 6.9 Fukushima earthquake Peer-reviewed

    Bruno Adriano, Yushiro Fujii, Shunichi Koshimura

    Geoscience Letters　5　(1)　2018/12

    Publisher: Springer Nature

    DOI： 10.1186/s40562-017-0100-9 　

  64. An integrated method to extract collapsed buildings from satellite imagery, hazard distribution and fragility curves Peer-reviewed

    Luis Moya, Erick Mas, Bruno Adriano, Shunichi Koshimura, Fumio Yamazaki, Wen Liu

    International Journal of Disaster Risk Reduction　31　1374-1384　2018/10

    DOI： 10.1016/j.ijdrr.2018.03.034 　

    ISSN： 2212-4209

  65. Sequential SAR Coherence Method for the Monitoring of Buildings in Sarpole-Zahab, Iran Peer-reviewed

    Sadra Karimzadeh, Masashi Matsuoka, Masakatsu Miyajima, Bruno Adriano, Abdolhossein Fallahi, Jafar Karashi

    Remote Sensing　2018/08/10

    DOI： 10.3390/rs10081255 　

  66. Identifying building damage patterns in the 2016 Meinong, Taiwan earthquake using post-event dual-polarimetric ALOS-2/PALSAR-2 imagery Peer-reviewed

    Yanbing Bai, Yanbing Bai, Bruno Adriano, Bruno Adriano, Erick Mas, Erick Mas, Shunichi Koshimura, Shunichi Koshimura

    Journal of Disaster Research　13　(2)　291-302　2018/03/01

    DOI： 10.20965/jdr.2018.p0291 　

    ISSN： 1881-2473

    eISSN： 1883-8030

  67. Novel Unsupervised Classification of Collapsed Buildings Using Satellite Imagery, Hazard Scenarios and Fragility Functions Peer-reviewed

    Luis Moya, Luis Marval Perez, Erick Mas, Bruno Adriano, Shunichi Koshimura, Fumio Yamazaki

    Remote Sensing　2018/02/14

    DOI： 10.3390/rs10020296 　

  68. Tsunami Source Inversion Using Tide Gauge and DART Tsunami Waveforms of the 2017 Mw8.2 Mexico Earthquake Peer-reviewed

    Bruno Adriano, Yushiro Fujii, Shunichi Koshimura, Erick Mas, Angel Ruiz-Angulo, Miguel Estrada

    Pure and Applied Geophysics　175　(1)　35-48　2018/01/16

    Publisher: Springer Nature

    DOI： 10.1007/s00024-017-1760-2 　

  69. Damage mapping after the 2017 Puebla earthquake in Mexico using high-resolution ALOS2 PALSAR2 data Peer-reviewed

    Adriano B, Koshimura S, Karimzadeh S, Matsuoka M, Koch M

    International Geoscience and Remote Sensing Symposium (IGARSS)　2018-July　870-873　2018

    DOI： 10.1109/IGARSS.2018.8517933 　

    ISSN： 2153-6996

  70. A Framework of Rapid Regional Tsunami Damage Recognition From Post-event TerraSAR-X Imagery Using Deep Neural Networks Peer-reviewed

    Yanbing Bai, Chang Gao, Sameer Singh, Magaly Koch, Bruno Adriano, Erick Mas, Shunichi Koshimura

    IEEE Geoscience and Remote Sensing Letters　15　(1)　43-47　2018/01

    Publisher: Institute of Electrical and Electronics Engineers ({IEEE})

    DOI： 10.1109/LGRS.2017.2772349 　

  71. Analysis of Spatio-Temporal Tsunami Source Models for Reproducing Tsunami Inundation Features Peer-reviewed

    Bruno Adriano, Satomi Hayashi, Shunichi Koshimura

    Geosciences　8　(1)　3-3　2017/12/25

    Publisher: {MDPI} {AG}

    DOI： 10.3390/geosciences8010003 　

  72. Machine Learning Based Building Damage Mapping from the ALOS-2/PALSAR-2 SAR Imagery: Case Study of 2016 Kumamoto Earthquake Peer-reviewed

    Yanbing Bai, Bruno Adriano, Erick Mas, Shunichi Koshimura, Graduate School of Engineering, Tohoku University 6-6-4 Aramaki-Aza Aoba, Aob-ku, Senda, Miyagi 980-8579, Japan, International Research Institute of Disaster Science, Tohoku University, Miyagi, Japan

    Journal of Disaster Research　12　(sp)　646-655　2017/06

    Publisher: Fuji Technology Press Ltd.

    DOI： 10.20965/jdr.2017.p0646 　

  73. Object-Based Building Damage Assessment Methodology Using Only Post Event ALOS-2/PALSAR-2 Dual Polarimetric SAR Intensity Images Peer-reviewed

    Yanbing Bai, Graduate School of Engineering, Tohoku University Aoba 468-1, Aramaki, Aoba-ku, Sendai 980-0845, Japan, Bruno Adriano, Erick Mas, Hideomi Gokon, Shunichi Koshimura, International Research Institute of Disaster Science, Tohoku University, Sendai, Japan, Institute of Industrial Science, The University of Tokyo, Tokyo, Japan

    Journal of Disaster Research　12　(2)　259-271　2017/03

    Publisher: Fuji Technology Press Ltd.

    DOI： 10.20965/jdr.2017.p0259 　

  74. Possible Failure Mechanism of Buildings Overturned during the 2011 Great East Japan Tsunami in the Town of Onagawa Peer-reviewed

    Panon Latcharote, Anawat Suppasri, Akane Yamashita, Bruno Adriano, Shunichi Koshimura, Yoshiro Kai, Fumihiko Imamura

    Frontiers in Built Environment　3　2017/03

    Publisher: Frontiers Media {SA}

    DOI： 10.3389/fbuil.2017.00016 　

  75. Tsunami Evacuation in the Pacific and Caribbean Coast of Colombia Peer-reviewed

    Mas, E, Adriano, B, Sanchez, R, Murao, O, Koshimura, S

    Proceeding of the 16th World Conference on Earthquake Engineering　No.2743-No.2743　2017/01

  76. Building Damage Assessment in the 2015 Gorkha, Nepal, Earthquake Using Only Post-Event Dual Polarization Synthetic Aperture Radar Imagery Peer-reviewed

    Bai Yanbing, Bruno Adriano, Erick Mas, Shunichi Koshimura

    Earthquake Spectra　2017

    Publisher: Earthquake Engineering Research Institute

    DOI： 10.1193/121516eqs232m 　

    ISSN： 8755-2930

  77. A proposed methodology for deriving tsunami fragility functions for buildings using optimum intensity measures Peer-reviewed

    Joshua Macabuag, Tiziana Rossetto, Ioanna Ioannou, Anawat Suppasri, Daisuke Sugawara, Bruno Adriano, Fumihiko Imamura, Ian Eames, Shunichi Koshimura

    Natural Hazards　84　(2)　1257-1285　2016/08/11

    Publisher: Springer Nature

    DOI： 10.1007/s11069-016-2485-8 　

  78. A PRECISE TSUNAMI NUMERICAL ANALYSIS OF LATTICE BOLTZMANN METHOD FOCUSING ON THE EXTERNAL FORCE TERM

    SATO Kenta, ADRIANO Bruno, KOSHIMURA Shunichi

    Journal of Japan Society of Civil Engineers, Ser. B3 (Ocean Engineering)　72　(2)　I_145-I_150　2016

    Publisher: Japan Society of Civil Engineers

    DOI： 10.2208/jscejoe.72.I_145 　

    More details Close

    &nbsp;Lattice Boltzmann Method (LBM) is the one of the new and efficient Computational Fluid Dynamics (CFD) solvers. It has become an alternative powerful method compared with the other conventional CFD solvers. In the current study, we developed a robust tsunami simulation model by applying enhanced LBM for shallow water equations with treatments of the bed slope in the external force term in the Lattice Boltzmann equation.<br>&nbsp;The proposed model is verified by the comparison with the conventional tsunami numerical model which is based on the Finite Difference Method (FDM). As a result, LBM results are in good agreement with the FDM results. The results imply that LBM based on shallow water equations has sufficient applicability of tsunami propagation and inundation.

  79. Revisiting the 2001 Peruvian earthquake and Tsunami impact along camana beach and the coastline using numerical modeling and satellite imaging

    Bruno Adriano, Erick Mas, Shunichi Koshimura, Yushiro Fujii, Hideaki Yanagisawa, Miguel Estrada

    Coastal Research Library　14　1-16　2016

    DOI： 10.1007/978-3-319-28528-3_1 　

    ISSN： 2211-0577

    eISSN： 2211-0585

  80. Understanding the extreme tsunami inundation in Onagawa Town by the 2011 Tohoku Earthquake, Its effects in urban structures and coastal facilities Peer-reviewed

    Adriano, B., Hayashi, S., Gokon, H., Mas, E., Koshimura, S.

    Coastal Engineering Journal　58　(4)　1640013-1640013　2016

    Publisher: World Scientific Pub Co Pte Lt

    DOI： 10.1142/S0578563416400131 　

  81. Recent Advances in Agent-Based Tsunami Evacuation Simulations: Case Studies in Indonesia, Thailand, Japan and Peru Peer-reviewed

    Erick Mas, Shunichi Koshimura, Fumihiko Imamura, Anawat Suppasri, Abdul Muhari, Bruno Adriano

    Pure and Applied Geophysics　172　(12)　3409-3424　2015/05

    Publisher: Springer Nature

    DOI： 10.1007/s00024-015-1105-y 　

  82. 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 Peer-reviewed

    Bruno Adriano, Erick Mas, Shunichi Koshimura, Hideomi Gokon, Wen Liu, Masashi Matsuoka

    2015 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS)　2015-November　3579-3582　2015

    DOI： 10.1109/IGARSS.2015.7326595 　

    ISSN： 2153-6996

  83. Buildings damage due to the 2013 Haiyan Typhoon inferred from SAR intensity images Peer-reviewed

    Bruno Adriano, Erick Mas, Shunichi Koshimura

    2015 IEEE 5TH ASIA-PACIFIC CONFERENCE ON SYNTHETIC APERTURE RADAR (APSAR)　667-671　2015

    DOI： 10.1109/APSAR.2015.7306294 　

  84. Reconstruction process and social issues after the 1746 earthquake and tsunami in Peru: Past and present challenges after tsunami events Peer-reviewed

    Erick Mas, Bruno Adriano, Julio Kuroiwa Horiuchi, Shunichi Koshimura

    Advances in Natural and Technological Hazards Research　44　97-109　2015

    Publisher: Springer Netherlands

    DOI： 10.1007/978-3-319-10202-3_7 　

    ISSN： 2213-6959 1878-9897

  85. Field survey report and satellite image interpretation of the 2013 Super Typhoon Haiyan in the Philippines Peer-reviewed

    Mas, E., Bricker, J., Kure, S., Adriano, B., Yi, C., Suppasri, A., Koshimura, S.

    Natural Hazards and Earth System Sciences　15　(4)　2015

    DOI： 10.5194/nhess-15-805-2015 　

  86. Developing a building damage function using SAR images and post-event data after the Typhoon Haiyan in The Philippines Peer-reviewed

    Bruno ADRIANO, Erick MAS, Shunichi KOSHIMURA

    Journal of Japan Society of Civil Engineers, Ser. B2 (Coastal Engineering)　71　(2)　I{\_}1729-I{\_}1734　2015

    Publisher: Japan Society of Civil Engineers

    DOI： 10.2208/kaigan.71.i_1729 　

  87. Mechanism and stability analysis of overturned buildings by the 2011 Great East Japan earthquake and tsunami in Onagawa town Peer-reviewed

    Latcharote, P, Suppasri, A, Yamashita, A, Adriano, B, Koshimura, S, Kai, Y, Imamura, F

    14th Japan earthquake engineering symposium　2014/12/04

  88. Improving Tsunami Numerical Simulation with the Time-Dependent Building Destruction Model Peer-reviewed

    HAYASHI Satomi, ADRIANO Bruno, MAS Erick, KOSHIMURA Shunichi

    Journal of Japan Society of Civil Engineers, Ser. B2 (Coastal Engineering)　70　(2)　I{\_}346-I_350　2014

    Publisher: Japan Society of Civil Engineers

    DOI： 10.2208/kaigan.70.I_346 　

    ISSN： 1884-2399

    More details Close

    Tsunami flow velocity on land is important for estimating tsunami load on structures. Numerical simulation is widely used to estimate tsunami load with flow velocity estimation. However, the simulation results have not been validated sufficiently because of the lack of observed information. In this study, through several case studies incorporating different initial and boundary conditions, authors simulated the 2011 Tohoku tsunami to discuss its verification with particular regard to tsunami inundation velocities. To better simulate tsunami inundation velocities, authors developed the tsunami run-up model with time-dependent building destruction model.

  89. DAMAGE DETECTION DUE TO THE TYPHOON HAIYAN FROM HIGH-RESOLUTION SAR IMAGES Peer-reviewed

    Wen Liu, Masashi Matsuoka, Bruno Adriano, Erick Mas, Shunichi Koshimura

    2014 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS)　2014

    DOI： 10.1109/IGARSS.2014.6947575 　

    ISSN： 2153-6996

  90. EXTRACTION OF DAMAGED AREAS DUE TO THE 2013 HAIYAN TYPHOON USING ASTER DATA Peer-reviewed

    Bruno Adriano, Hideomi Gokon, Erick Mas, Shunichi Koshimura, Wen Liu, Masashi Matsuoka

    2014 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS)　2154-2157　2014

    DOI： 10.1109/IGARSS.2014.6946893 　

    ISSN： 2153-6996

  91. Tsunami evacuation simulation – Case studies for tsunami mitigation at Indonesia, Thailand and Japan Peer-reviewed

    Mas, E, Koshimura, S, Imamura, F, Muhari, A, Adriano, B, Suppasri, A

    SIMULTECH2014　2014

  92. Tsunami waveform inversion of the 2007 peru (M&lt;inf&gt;w&lt;/inf&gt;8.1) earthquake Peer-reviewed

    Jimenez, C., Moggiano, N., Mas, E., Adriano, B., Fujii, Y., Koshimura, S.

    Journal of Disaster Research　9　(6)　954-960　2014

    Publisher: Fuji Technology Press Ltd.

    DOI： 10.5194/nhess-12-2689-2012 　

  93. Simulation of Tsunami inundation in central Peru from future megathrust earthquake scenarios Peer-reviewed

    Mas, E., Adriano, B., Pulido, N., Jimenez, C., Koshimura, S.

    Journal of Disaster Research　9　(6)　2014

  94. Field survey and damage inspection after the 2013 Typhoon Haiyan in The Philippines Peer-reviewed

    Erick MAS, Shuichi KURE, Jeremy D. BRICKER, Bruno ADRIANO, Carine YI, Anawat SUPPASRI, Shunichi KOSHIMURA

    Journal of Japan Society of Civil Engineers, Ser. B2 (Coastal Engineering)　70　(2)　I{\_}1451-I{\_}1455　2014

    Publisher: Japan Society of Civil Engineers

    DOI： 10.2208/kaigan.70.i_1451 　

  95. Development of building height data in Peru from high-resolution SAR imagery Peer-reviewed

    Liu, W., Yamazaki, F., Adriano, B., Mas, E., Koshimura, S.

    Journal of Disaster Research　9　(6)　1042-1049　2014

    Publisher: Fuji Technology Press Ltd.

    DOI： 10.20965/jdr.2014.p1042 　

  96. Spatial Variation of Damage due to Storm Surge and Waves during Typhoon Haiyan in the Philippines Peer-reviewed

    Jeremy D. BRICKER, Hiroshi TAKAGI, Erick MAS, Shuichi KURE, Bruno ADRIANO, Carine YI, Volker ROEBER

    Journal of Japan Society of Civil Engineers, Ser. B2 (Coastal Engineering)　70　(2)　I{\_}231-I{\_}235　2014

    Publisher: Japan Society of Civil Engineers

    DOI： 10.2208/kaigan.70.i_231 　

  97. Scenarios of earthquake and tsunami damage probability in callao region, Peru using tsunami fragility functions Peer-reviewed

    Adriano, B., Mas, E., Koshimura, S., Estrada, M., Jimenez, C.

    Journal of Disaster Research　9　(6)　968-975　2014

    Publisher: Fuji Technology Press Ltd.

    DOI： 10.20965/jdr.2014.p0968 　

  98. Identifying evacuees’ demand of tsunami shelters using agent based simulation Peer-reviewed

    Erick Mas, Bruno Adriano, Shunichi Koshimura, Fumihiko Imamura, Julio H. Kuroiwa, Fumio Yamazaki, Carlos Zavala, Miguel Estrada

    Advances in Natural and Technological Hazards Research　35　347-358　2014

    Publisher: Springer Netherlands

    DOI： 10.1007/978-94-007-7269-4_19 　

    ISSN： 1878-9897

    eISSN： 2213-6959

  99. An Integrated Simulation of Tsunami Hazard and Human Evacuation in La Punta, Peru Peer-reviewed

    Erick Mas, Laboratory of Remote Sensing and Geoinformatics for Disaster Management, International Research Institute of Disaster Science, Tohoku University, Aoba 6-6-3, Sendai 980-8579, Japan, Bruno Adriano, Shunichi Koshimura

    Journal of Disaster Research　8　(2)　285-295　2013/03/01

    Publisher: Fuji Technology Press Ltd.

    DOI： 10.20965/jdr.2013.p0285 　

  100. An integrated simulation of tsunami hazard and human evacuation in La Punta, Peru Peer-reviewed

    Mas, E., Adriano, B., Koshimura, S.

    Journal of Disaster Research　8　(2)　285-295　2013

    Publisher: Fuji Technology Press Ltd.

    DOI： 10.20965/jdr.2013.p0285 　

  101. Tsunami inundation mapping in lima, for two tsunami source scenarios Peer-reviewed

    Adriano, B., Mas, E., Koshimura, S., Fujii, Y., Yauri, S., Jimenez, C., Yanagisawa, H.

    Journal of Disaster Research　8　(2)　274-284　2013

  102. Seismic source of 1746 callao earthquake from tsunami numerical modeling Peer-reviewed

    Jimenez, C., Moggiano, N., Mas, E., Adriano, B., Koshimura, S., Fujii, Y., Yanagisawa, H.

    Journal of Disaster Research　8　(2)　266-273　2013

    DOI： 10.20965/jdr.2013.p0266 　

  103. Validation of tsunami inundation modelling for the june 23, 2001 peru earthquake Peer-reviewed

    Adriano, B., Koshimura, S., Fujii, Y.

    Bulletin of the International Institute of Seismology and Earthquake Engineering　45　127-132　2011

Show all ︎Show first 5

Misc. 14

  1. 有事を想定した被災前後SAR画像を用いた深層学習による土砂崩れ域の推定手法の検討

    大平尚輝, 郷右近英臣, ADRIANO Bruno, 三浦弘之, MAS Erick, 越村俊一

    土木学会全国大会年次学術講演会(Web)　80th　2025

  2. Fault Model of the 2024 Noto Earthquake Estimated Using Aftershock, GNSS, and Tsunami Data

    水谷歩, ADRIANO Bruno, MAS Erick, 越村俊一

    日本地球惑星科学連合大会予稿集(Web)　2024　2024

  3. Tsunami analytical fragility curves for the Colombian Pacific coast: A reinforced concrete building example

    Sergio Medina, Juan Lizarazo-Marriaga, Martin Estrada, Shunichi Koshimura, Erick Mas, Bruno Adriano

    Engineering Structures　196　2019/10/01

    DOI： 10.1016/j.engstruct.2019.109309 　

    ISSN： 0141-0296

    eISSN： 1873-7323

  4. BUILDING DAMAGE MAPPING USING CHANGE DETECTION OF ALOS‐2 PALSAR‐2 SAR IMAGES AND STRONG MOTION DATA

    MOYA Luis, MAS Erick, ADRIANO Bruno, ADRIANO Bruno, KOSHIMURA Shunichi, YAMAZAKI Fumio

    日本リモートセンシング学会学術講演会論文集(CD-ROM)　62nd　ROMBUNNO.D‐22　2017/05/17

  5. Tsunami source of the Mw7 2016 Fukushima Earthquake inferred from tide gauge and GPS buoy records

    ADRIANO Bruno, FURUYA Takasi, MAS Erick, KOSHIMURA Shunichi

    日本地球惑星科学連合大会予稿集(Web)　2017　ROMBUNNO.HDS12‐P09 (WEB ONLY)　2017

  6. Lバンド合成開口レーダ画像を用いた平成27年9月関東・東北豪雨の湛水域抽出

    織田征和, ADRIANO Bruno, 郷右近英臣, 越村俊一

    土木学会年次学術講演会講演概要集(CD-ROM)　71st　ROMBUNNO.II‐119　2016/08/01

  7. TSUNAMI EVACUATION PLANNING AND RESPONSE SUPPORTED BY SIMULATION TOOLS

    MAS Erick, ADRIANO Bruno, KOSHIMURA Shunichi

    日本地震工学会年次大会梗概集(CD-ROM)　11th　ROMBUNNO.B-6　2015

  8. APPLICATION OF A PHASE‐BASED CORRELATION METHOD TO EXTRACT DAMAGE AREAS, CASE OF STUDY: 2013 HAIYAN TYPHOON

    ADRIANO Bruno, GOKON Hideomi, MAS Erick, KOSHIMURA Shunichi, LIU Wen, MATSUOKA Masashi

    日本地震工学シンポジウム論文集(CD-ROM)　14th　ROMBUNNO.OS12-SAT-AM-6　2014/11/17

  9. シミュレーションと空間情報学を融合した津波被災地の被害把握

    越村俊一, ADRIANO Bruno, 郷右近英臣, MAS Erick

    日本船舶海洋工学会講演会論文集(CD-ROM)　(18)　ROMBUNNO.2014S-OS3-4　2014/05

    ISSN： 2185-1840

  10. 建物破壊状況に応じた時間発展型合成等価粗度モデルの検討

    林 里美, Adriano Bruno, Mas Erick, 越村 俊一

    津波工学研究報告 = Tsunami engineering　31　(31)　93-103　2014/03

    Publisher: 東北大学

    ISSN： 0916-7099

  11. COSMO-SkyMed強度画像を用いたフィリピン台風被害の検出

    LIU Wen, 松岡昌志, ADRIANO Bruno, MAS Erick, 越村俊一

    日本リモートセンシング学会学術講演会論文集　56th　2014

  12. Improving Tsunami Numerical Simulation with the Time-Dependent Building Destruction Model

    林里美, ADRIANO Bruno, MAS Erick, 越村俊一

    土木学会論文集 B2(海岸工学)(Web)　70　(2)　2014

    ISSN： 1883-8944

  13. BASIC STUDY ON THE CONTRIBUTION OF TSUNAMI MULTILAYER PROTECTION TO TSUNAMI EVALUATION AND COASTAL COMMUNITY RESILIENCE

    MAS Erick, MUHARI Abdul, ADRIANO Bruno, KOSHIMURA Shunichi, IMAMURA Fumihiko

    Proceedings of International Sessions in Conference on Coastal Engineering, JSCE　4　85-89　2013/11

  14. EVALUATION OF TSUNAMI EVACUATION BUILDING DEMAND THROUGH THE MULTI‐AGENT SYSTEM SIMULATION OF RESIDENTS’ BEHAVIOR

    MAS Erick, ADRIANO Bruno, KOSHIMURA Shunichi, IMAMURA Fumihiko, KUROIWA H. Julio, YAMAZAKI Fumio, ZAVALA Carlos, ESTRADA Miguel

    Proceedings of International Sessions in Conference on Coastal Engineering, JSCE　3　61-65　2012/11

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Presentations 6

  1. A Multitask Learning Framework for Rapid Tsunami Inundation Assessment at Coastal

    Muhammad Rizki Purnama, Elisa Lahcene, Bruno Adriano, Mohammad Farid, Mohammad Bagus Adityawan, Alvin Yesaya, Sesar Prabu Dwi Sriyanto, Anawat Suppasri, Fumihiko Imamura

    The 18th Aceh International Workshop and Expo on Sustainable Disaster Recovery (AIWEST-DR) Conference, Bangkok, Thailand　2026/07/24

  2. Deep learning super-resolution of tsunami inundation maps: Application to the 2006 Pangandaran tsunami

    Sesar P.D. Sriyanto, Bruno Adriano, Erick Mas, Shunichi Koshimura

    The 18th Aceh International Workshop and Expo on Sustainable Disaster Recovery (AIWEST-DR) Conference, Bangkok, Thailand　2026/07/23

  3. Urban Flood Hazard Analytics: Comparing Entropy Weight Method and HEC-RAS in Himalayan Basin-Valley Cities

    Kshitiz Agarwal, Mahua Mukherjee, Bruno Adriano

    The 18th Aceh International Workshop and Expo on Sustainable Disaster Recovery (AIWEST-DR) Conference, Bangkok, Thailand　2026/07/22

  4. Developing a Machine Learning Method for Near-Real-Time Spatiotemporal Estimation of Tsunami Inundation Depths

    Bruno Adriano, Shunichi Koshimura

    The 13th National Conference on Earthquake Engineering, Portland, US　2026/07/16

  5. Flood Depth Mapping from SAR Imagery Using CS-Mamba with DEM Sensitivity Analysis

    Zhongyuan Yang, Wei Yuan, Weihang Ran, Changqing Liu, Bruno Adriano, Ryosuke Shibasaki, Shunichi Koshimura

    The XXV International Society for Photogrammetry and Remote Sensing Congress Toronto, Canada　2026/07/10

  6. AI-Based Mapping of Collapsed Buildings Using Remote Sensing Imagery for Rapid Disaster Response

    Bruno Adriano

    Resilient Societies and Crisis Response Research Workshop　2026/05/26

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Research Projects 9

  1. Building a Visual Language Benchmarking Platform for Disaster Response

    Naoto Yokoya, Kazuki Yamanoi, Bruno Adriano

    Offer Organization: Japan Science and Technology Agency

    System: JST CRONOS

    Institution: The University of Tokyo

    2025/10 - 2030/03

  2. Development of high-resolution tsunami damage assessment technology using generative AI and numerical modeling Competitive

    Bruno Adriano

    Offer Organization: Japan Society for the Promotion of Science

    Category: Grant-in-Aid for Scientific Research (C)

    Institution: Tohoku University

    2026/04 - 2029/03

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    The objective of this study is to integrate generative AI models with numerical tsunami simulations to develop a high-fidelity "hybrid tsunami damage assessment framework" that operates in near real time and achieves a spatial resolution of 1.0 m or finer. Specifically, the study pursues the following three aims: (1) Using a generative AI model trained on multi-scale simulation data, upsample medium-resolution inundation fields to ultra-high-resolution spatial predictions. (2) Incorporate the physical constraints of numerical modeling into the AI outputs to ensure physical consistency across inundation depth, flow velocity, and building-level damage probability. (3) Demonstrate and evaluate the effectiveness of the proposed method through case studies in urbanized coastal areas, using historical tsunami events (e.g., the 2024 Noto Peninsula Earthquake) and hypothetical scenarios.

  3. Enabling Human-Centered Digital Twin for Community Resilience Competitive

    Erick Mas, Bruno Adriano, Shohei Nagata, Nalini Venkatasubramanian, Magaly Koch, Ron Eguchi

    Offer Organization: Japan Science and Technology Agency (JST)

    System: Strategic International Collaborative Research Program

    Institution: International Research Institute of Disaster Science, Tohoku University

    2024/04 - 2026/03

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    The purpose of this research project is to apply and expand the concept of “Digital Twins” to disaster science and build a “Disaster Digital Twin” (DDT) which utilizes human-centered data to improve community resilience. Specifically, the DDT and a multi-agent simulation framework developed by the Japanese team will be applied in a context of the elderly, a population with personalized care needs which is disproportionately affected by disasters. This will be done through the integration of the “CareDEX” by the U.S. team, a platform which incorporates personalized care information from responders, caregivers and the elderly, with a digital twin developed by the both teams. This integration of technology developed by the two teams is expected to enable a variety of “Virtual Disaster City” (VDC) simulations which will be useful for policy design in for the elderly with the specific needs, for example medical equipment, reduced mobility, cognitive disease, in a context of disaster resilience.

  4. Development of Damage Estimation Technology Applicable to Various Disasters Using Multimodal Sensing and AI

    Offer Organization: Japan Society for the Promotion of Science

    System: Grants-in-Aid for Scientific Research

    Category: Grant-in-Aid for Scientific Research (B)

    Institution: Hiroshima University

    2022/04/01 - 2025/03/31

  5. Development of Damage Estimation Technology Applicable to Various Disasters Using Multimodal Sensing and AI

    Offer Organization: Japan Society for the Promotion of Science

    System: Grants-in-Aid for Scientific Research

    Category: B

    Institution: Hiroshima University

    2022/04 - 2025/03

  6. 2023年トルコ南部の地震と災害に関する総合調査

    楠 浩一, 青木 陽介, 西村 卓也, 小林 知勝, 近藤 久雄, Adriano Bruno, 王 功輝, Bhandary NetraPrakash, 加藤 愛太郎, 山本 揚二朗, 吉田 圭佑, 八木 勇治, 内田 直希, 汐見 勝彦, 山中 浩明, 高井 伸雄, 吉見 雅行, 地元 孝輔, 中村 洋光, 目黒 公郎, 久田 嘉章, 森 伸一郎, 清田 隆, 小野 祐輔, 後藤 浩之, 日比野 陽, 毎田 悠承, 大西 直毅, SHEGAY ALEKSEY, 阪本 真由美, 金田 義行, 木村 周平, 牧 紀男

    Offer Organization: 日本学術振興会

    System: 科学研究費助成事業

    Category: 特別研究促進費

    Institution: 東京大学

    2023/03/17 - 2024/03/31

  7. Sequential decision analysis and its application to flood risk reduction and evacuation order optimization Competitive

    Erick Mas, Shunichi Koshimura, Masakazu Hashimoto, Bruno Adriano

    Offer Organization: Japan Science and Technology Agency

    System: Strategic International Science and Technology Cooperation Promotion Program

    Institution: Tohoku University

    2022 - 2023/03

  8. Monitoring and rapid identification of landslide disaster by fusion analysis of sensing and simulation technologies

    Miura Hiroyuki

    Offer Organization: Japan Society for the Promotion of Science

    System: Grants-in-Aid for Scientific Research

    Category: Grant-in-Aid for Scientific Research (B)

    Institution: Hiroshima University

    2019/04/01 - 2022/03/31

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    This study aims the development for evaluation of potential for landslide disasters using topographical data, and remote sensing and simulation-based approach for detection of landslide and building damage as shown below. 1. Analysis of spatial data obtained debris flow disasters and simulation-based estimation of flow propagation and building damage, 2. Estimation of collapse areas and volume of debris flows by remote sensing and simulation technique, 3. Remote sensing-based building damage detection, 4. Estimation of source and volume of debris flows from seismic observation records.

  9. JSPS Postdoctoral Researcher Competitive

    Bruno ADRIANO ORTEGA

    Offer Organization: Japan Society for the Promotion of Sicence

    System: JSPS Postdoctoral Fellowship

    2016/04 - 2018/03

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Teaching Experience 2

  1. Machine Learning and Tsunami Simulation Synergy for Disaster Management　Building Research Institute

  2. Engineering English II　Tohoku University

Works 1

  1. OpenEarthMap

    Junshi Xia, Naoto Yokoya, Bruno Adriano, Clifor Broni-Bediako

    2023/01/03 - 2023/01/31

    Type: Database

    DOI： 10.1109/WACV56688.2023.00619 　

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    OpenEarthMap is a global initiative dedicated to advancing open and accessible machine learning-based mapping using remote sensing data. The project focuses on extracting semantic and height information, such as land cover maps and digital elevation models (DEMs), to support critical applications in environmental monitoring, urban planning, and disaster management. Built upon the OpenEarthMap dataset—a high-resolution land cover mapping benchmark—the project promotes the development of models that generalize worldwide, ensuring accurate and scalable geospatial analysis. By making mapping technologies openly available, OpenEarthMap aims to bridge geographic inequalities, empowering researchers, policymakers, and local communities worldwide to develop and deploy their own mapping solutions.

Academic Activities 2

  1. Coastal Engineering Journal - Associate Editor

    2024/04/01 - Present

    Activity type: Scientific advice/Review

  2. Remote Sensing MDPI - Section Editorial Board

    2020/04/01 - Present

    Activity type: Scientific advice/Review