JP

Organization

Research Division

Risk Evaluation and Disaster Mitigation Research Division
Disaster Geo-informatics Lab
Associate Professor
Ph.D. (Engineering)
Concurrent: Graduate School of Engineering
Research Subject(s)
My research interests include earth observation, machine learning, and high-performance computing technologies with applications to disaster science and urban environmental monitoring. My research focuses on intelligent data processing to automatically extract land information, such as disaster-induced changes from remote sensing images, and numerical simulation to understand the physical characteristics of large-scale disasters. I mainly focus on developing methods for pre-disaster mitigation efforts, such as analysis of land cover and land use conditions, and post-disaster response actions, such as rapid recognition of affected areas.
Key Words
Earth observation data analysis / Machine learning / Geoinformatics / Numerical modeling / Data fusion
Website
Research Activities

Earth observation and Machine learning for Disaster response.
Accurate and rapid understanding of the full-scale damage after a disaster is fundamental for efficient response and relief post-disaster actions. However, affected areas are often inaccessible after a disaster occurs. In such scenarios, earth observation technologies allow us to observe places inaccessible to humans. Yet, the enormous diversity of remote sensing platforms and modalities makes their analysis difficult. So, we focus on developing intelligent remote sensing image analysis methods based on machine learning and computer vision techniques to automatically extract land information following disasters, such as earthquakes, tsunamis, and landslides.

Assessing Disasters using Numerical Simulation and Machine learning.
Large-scale disasters like tsunamis and floods present unique characteristics in different urban environments. As such, computer simulations enable us to study complex aspects and mechanisms of disasters. Further, the combined applications of machine learning algorithms and computer simulation allows us to expand the limits of standard numerical modeling. So, we focus on developing integrated technologies to rapidly estimate disaster's physics-based features, such as inundation depth and ground deformation. Mainly, we focus on developing real-time disaster modeling methods to support the emergency response capabilities of regions with limited computational resources.

Selected Works

Adriano, B., Yokoya, N., Yamanoi, K., Oishi, S. (2022). Predicting Flood Inundation Depth Based on Machine Learning and Numerical Simulation, In Proceedings of the 31st International Joint Conference on Artificial Intelligence and the 25th European Conference on Artificial Intelligence (IJCAI-ECAI), Vienna, Austria, 3207, 58-64.

Adriano, B., Yokoya, N., Miura, H.,Liu, W., Matsuoka M., Koshimura, K. (2021), Learning from multimodal and multitemporal earth observation data for building damage mapping, ISPRS Journal of Photogrammetry and Remote Sensing, 175, 132-143. https://doi.org/10.1016/j.isprsjprs.2021.02.016

Adriano, B., Yokoya, N., Miura, H., Matsuoka, M., and Koshimura, S. (2020), A Semi-automatic Pixel-Object Method for Detecting Landslides Using Multitemporal ALOS-2 Intensity Images, Remote Sensing, 12(13), 561. https://doi.org/10.3390/rs12030561 ;

Adriano, B., Xia, J., Baier, G., Yokoya, N., and Koshimura, S. (2019), Multi-Source Data Fusion Based on Ensemble Learning for Rapid Building Damage Mapping during the 2018 Sulawesi Earthquake and Tsunami in Palu, Indonesia, Remote Sensing, 11(7), 886. https://doi.org/10.3390/rs11070886

Adriano, B., Fujii, Y., Koshimura, K., Mas, E., Ruiz-Angulo, A., Estrada, M. (2017), Tsunami Source Inversion Using Tide Gauge and DART Tsunami Waveforms of the 2017 Mw8.2 Mexico Earthquake, Pure and Applied Geophysics, 175, 35-48. https://doi.org/10.1007/s00024-017-1760-2

Selected Memberships
  • IEEE Geoscience and Remote Sensing Society (GRSS)
  • Japan Society of Civil Engineers (JSCE)
  • American Geophysical Union (AGU)
Selected Awards
  • Japan Society for the Promotion of Science (JSPS), Postdoctoral Fellowship of Overseas Researchers (2016.4-2018.3).
  • Excellence Award in the Research Poster Competition of the 9th APRU Multi-Hazards Symposium.
Others

Visiting Researcher, Interdisciplinary Graduate School of Science and Technology, Tokyo Institute of Technology.

Editorial activity:
- Editorial Board Member, Environmental Remote Sensing Section, Remote Sensing
- Associate Editor, Coastal Engineering Journal
- Guest Editor, IEEE Journal on Selected Topics in Applied Earth Observations and Remote Sensing

Reviewer
- IEEE Transactions on Geoscience and Remote Sensing
- IEEE Journal of Selected Topics on Applied Remote Sensing
- IEEE Geoscience and Remote Sensing Letters
- Remote Sensing
- Remote Sensing of Environment
- ISPRS Journal of Photogrammetry and Remote Sensing

Personal Website: 
- https://brunoadriano.com