研究成果

1. SIMULATIONS (Tsunami, Agent-Based, etc.)

  1. Adriano, B., Gokon, H., Mizutani, A., Mas, E. & Koshimura, S. (2025). Understanding the relationship between building damage and tsunami inundation due to the 2024 Noto Peninsula Earthquake. Ocean Engineering, 340, 122179. https://doi.org/10.1016/j.oceaneng.2025.122179
  2. Adriano, B., Jimenez, C., Mas, E. & Koshimura, S. (2025). Revising the seismic source of the 1979 Tumaco-Colombia earthquake (Mw=8.1) for future tsunami hazard assessment. Physics of the Earth and Planetary Interiors, 362, 107344. https://doi.org/10.1016/j.pepi.2025.107344
  3. Dong, X., Mas, E., Adriao, B. & Koshimura, S. (2025). Towards real-time extraction of cascading effect and spatiotemporal analysis using social media data. International Journal of Disaster Risk Reduction, 125, 105512. https://doi.org/10.1016/j.ijdrr.2025.105512
  4. Nagata, S., Mas, E., Taked, Y., Nakaya, T. & Koshimura, S. (2025). Multiple hazards and population change in Japan's Suzu City after the 2024 Noto Peninsula Earthquake. Progress in Disaster Science, 25, 100396. https://doi.org/10.1016/j.pdisas.2024.100396
  5. Koshimura, S., Mori, N., Chikasada, N., Udo, K., Ninomiya, J., Okumura, Y. & Mas, E. (2025) Probabilistic Tsunami Hazard and Risk Analysis. In Advanced Topics..., 543–559. https://doi.org/10.1016/b978-0-443-18987-6.00024-5
  6. Flores, C., Lee, H. S. & Mas, E. (2024) Understanding Tsunami Evacuation via a Social Force Model While Considering Stress Levels Using Agent-Based Modelling. Sustainability, 16, 4307. https://doi.org/10.3390/su16104307
  7. Mizutani, A., Adriano, B., Mas, E. & Koshimura, S. (2024). Fault Model of the 2024 Noto Peninsula Earthquake Based on Aftershock, Tsunami, and GNSS Data. https://doi.org/10.21203/rs.3.rs-4167995/v1
  8. Mas, E., Moya, L., Gonzales, E. & Koshimura, S. (2024). Reinforcement learning-based Tsunami evacuation guidance system. International Journal of Disaster Risk Reduction, 110, 104625. https://doi.org/10.1016/j.ijdrr.2024.104625
  9. Nagata, S., Mas, E., Taked, Y., Nakaya, T. & Koshimura, S. (2024a). Multiple hazards and population change in Japan's Suzu City after the 2024 Noto Peninsula Earthquake. Progress in Disaster Science, 100396. https://doi.org/10.1016/j.pdisas.2024.100396
  10. Adriano, B., Moya, L., Mas, E., Miura H., Matsuoka, M. & Koshimura, S. (2024). Urban Vulnerability Analysis in the Rimac River Basin using High-Resolution Imagery. IGARSS 2024, 3635–3638. https://doi.org/10.1109/igarss53475.2024.10642715
  11. Flores, C., Lee, H. S., Mas, E. & Salar, J. (2023). Tsunami evacuation in a massive crowd event using an agent-based model. Coastal Engineering Proceedings, 37, 62. https://doi.org/10.9753/icce.v37.management.62
  12. Jimenez, C., Morales, J., Estrada, M., Adriano, B., Mas, E. & Koshimura, S. (2023). Estimation of the Seismic Source of the 1974 Lima Peru Earthquake and Tsunami (Mw 8.1). Journal of Disaster Research, 18(8), 825–834. https://doi.org/10.20965/jdr.2023.p0825
  13. KAWAI, S., SATO, S., MAS, E., SHINKA, A. & IMAMURA, F. (2023). Promoting evacuation on foot to alleviate traffic congestion. Japanese Journal of JSCE, 79(17), 23–17182. https://doi.org/10.2208/jscejj.23-17182
  14. Hachiya, D., Mas, E. & Koshimura, S. (2022). RL model of multiple UAVs for transporting emergency supplies. Applied Sciences, 12(20), 10427. https://doi.org/10.3390/app122010427
  15. Mas, E., M.D.,Egawa, S., M.D., Sasaki, H. & Koshimura, S. (2022). Modeling search and rescue, medical team response and patient transport after a tsunami. E3S Web of Conferences, 340, 05001. https://doi.org/10.1051/e3sconf/202234005001
  16. Dong, L., Bai, Y., Xu, Q. & Mas, E. (2022) Optimizing post-disaster resource allocation with Q-learning. In LNCS, 256–262. https://doi.org/10.1007/978-3-031-12426-6_21
  17. Mas, E., Moya,L. & Koshimura, S. (2022). Optimization of Tsunami Evacuation with Reinforcement Learning. SSRN. https://doi.org/10.2139/ssrn.4214384
  18. Hashimoto, M., Mas, E., Egawa S., Sano, D. & Koshimura, S. (2022). Flood Hazard-Based Evacuation Curve Using Mobile Spatial Statistics. SSRN. https://doi.org/10.2139/ssrn.4271169
  19. Ito, E., Kosaka, T., Hatayama, M., Urra, L., Mas, E. & Koshimura, S. (2021). Extracting difficult-to-evacuate areas using tsunami simulation. IJDRR, 64, 102486. https://doi.org/10.1016/j.ijdrr.2021.102486
  20. León, J., Mas, E., Catalan, P. A., et al. (2021). Calibrated tsunami evacuation models using real-world data (Coquimbo–La Serena). IOP EES. https://doi.org/10.1088/1755-1315/630/1/012005
  21. Nagasawa, R., Mas, E., Moya,L. & Koshimura, S. (2021). Model-based analysis of multi-UAV path planning for post-disaster damage surveying. Scientific Reports, 11, 18588. https://doi.org/10.1038/s41598-021-97804-4
  22. Mas, E., Abe, Y., M.D., Egawa, S., M.D., Sasaki, H. & Koshimura, S. (2021). Agent-based modeling of disaster response teams after the 2011 Tohoku tsunami. AIWEST2021.
  23. Nakano, G., Yamori, K., Miyashita, T., Urra, L., Mas, E. & Koshimura, S. (2020). School evacuation drills + tsunami simulation for consensus-making. IJDRR, 51, 101803. https://doi.org/10.1016/j.ijdrr.2020.101803
  24. Paez-Ramirez, J., Lizarazo-Marriaga, J., Medina, S., Estrada, M., Mas, E. & Koshimura, S. (2020). Comparative study of fragility functions for tsunami building damage (Tumaco). Coastal Engineering Journal, 62(3), 1–11. https://doi.org/10.1080/21664250.2020.1726558
  25. Maly, E., Terada, K., LeVeque, R. J., ... Mas, E. (2020) International collaboration on M9 Disaster Science: session report. Journal of Disaster Research, 15(7), 890–899. https://doi.org/10.20965/jdr.2020.p0890
  26. Mas, E. & Koshimura, S. (2020). Tsunamis in Latin American countries. Coastal Engineering Journal, 62(3), 349–349. https://doi.org/10.1080/21664250.2020.1798147
  27. Yamamoto, Y., Kozono, Y., Mas, E., ... (021). Applicability of calculation formulae of impact force by tsunami driftage. JMSE, 9(5), 493. https://doi.org/10.3390/jmse9050493
  28. Yosritzal, Putra, H., Kemal, B. M., Mas, E. & Purnawan. (2020). Factors influencing evacuation walking speed in Padang. Advances in Engineering Research, 193, 125–130. https://doi.org/10.2991/aer.k.200220.026
  29. Mas, E., Moya,L. & Koshimura, S. (2020, Sept 13). Tsunami evacuation guidance using RL. 17th WCEE.
  30. Escobar, R. S., Diaz, L. O., ... Mas, E. (2020) Tsunami hazard assessment for Colombia. Coastal Engineering Journal, 62(4), 1–13. https://doi.org/10.1080/21664250.2020.1818362

2. REMOTE SENSING

  1. Wiguna, S., Adriano, B., Vescovo, R., Mas, E., Mizutani, A. & Koshimura, S. (2024). Building damage mapping of the 2024 Noto Peninsula Earthquake using semi-supervised learning and VHR optical imagery. IEEE GRSL, 21, 1–5. https://doi.org/10.1109/lgrs.2024.3407725
  2. Ezaki, Y., Ho, C. Y., Adriano, B., Mas, E. & Koshimura, S. (2024). Evaluation of simulated SAR images for building damage classification. IEEE GRSL. https://doi.org/10.1109/lgrs.2024.3520251
  3. Ho, C. Y., Mas, E., Adriano, B. & Koshimura, S. (2024b). Feasibility of ray-tracing SAR simulation for building damage assessment. IEEE JSTARS. https://doi.org/10.1109/jstars.2024.3418412
  4. Ho, C. Y., Mas, E., Adriano, B. & Koshimura, S. (2024a). Comparative analysis of detailed features in 3D models for SAR simulation. IGARSS 2024, 1750–1754. https://doi.org/10.1109/igarss53475.2024.10640983
  5. Wiguna, S., Adriano, B., Mas, E. & Koshimura, S. (2024a). Assessment of DL models trained on global RS imagery in real-context emergency response. IGARSS 2024, 1736–1740. https://doi.org/10.1109/igarss53475.2024.10641821
  6. Bai, Y., Yang, Z., Yu, J., Ju, R.-Y., Yang, B., Mas, E. & Koshimura, S. (2024). Flood data analysis on SpaceNet 8 using Apache Sedona. arXiv. https://doi.org/10.48550/arxiv.2404.18235
  7. Moya, L., Mas, E. & Koshimura, S. (2023). Flood inundation depth estimation from SAR-based flood extent and DEM. IGARSS 2023, 337–340. https://doi.org/10.1109/igarss52108.2023.10283297
  8. Moya, L., Mas, E. & Koshimura, S. (2022). Sparse representation-based inundation depth estimation using SAR & DEM. IEEE JSTARS. https://doi.org/10.1109/jstars.2022.3215719
  9. Bai, Y., Wu, W., Yang, Z., Yu, J., Zhao, B., Liu, X., Yang, H., Mas, E. & Koshimura, S. (2021). Detecting permanent and temporary water by fusing Sentinel-1/2 with deep learning (Sen1Floods11). Remote Sensing, 13(11), 2220. https://doi.org/10.3390/rs13112220
  10. Moya, L., Hashimoto, M., Mas, E. & Koshimura, S. (2021). Automatic collection of training samples for flooded areas. IGARSS 2021, 8305–8308. https://doi.org/10.1109/igarss47720.2021.9554446
  11. Moya, L., Geiss, C., Hashimoto, M., Mas, E., Koshimura, S. & Strunz, G. (2021). Disaster intensity-based selection of training samples for RS building damage classification. IEEE TGRS. https://doi.org/10.1109/tgrs.2020.3046004
  12. Okada, G., Moya, L., Mas, E. & Koshimura, S. (2021). News media to support ML-based damage mapping. Remote Sensing, 13(7), 1401. https://doi.org/10.3390/rs13071401
  13. Bai, Y., Hu, J., Su, J., Liu, X., Liu, H., He, X., Meng, S., Mas, E. & Koshimura, S. (2020). Semi-Siamese benchmark for building damage (xBD). Remote Sensing, 12(24), 4055. https://doi.org/10.3390/rs12244055
  14. Moya, L., Mas, E. & Koshimura, S. (2020). Learning from 2018 Western Japan heavy rains to detect floods during Typhoon Hagibis. Remote Sensing, 12(14), 2244. https://doi.org/10.3390/rs12142244
  15. Moya, L., Muhari, A., Adriano, B., Koshimura, S., Mas, E., Marva-Perez, L. R. & Yokoya, N. (2020). Detecting urban changes for early response (Sulawesi 2018). Remote Sensing of Environment, 242, 111743. https://doi.org/10.1016/j.rse.2020.111743
  16. Koshimura, S., Moya, L., Mas, E. & ai, Y. (2020). Tsunami damage detection with remote sensing: a review. Geosciences, 10(5), 177. https://doi.org/10.3390/geosciences10050177
  17. Zhao, L., Hu, J., Bi, J., Bai, Y., Mas, E. & Koshimura, S. (2024b). AI-enhanced UAVs for wildfire surveillance (FLAME dataset). IROS 2024, 8063–8068. https://doi.org/10.1109/iros58592.2024.10801629
  18. Zhao, L., Hu, J., Bi, J., Bai, Y., Mas, E. & Koshimura, S. (2024a). AI-enhanced UAVs for wildfire surveillance (FLAME) — preprint. arXiv. https://doi.org/10.48550/arxiv.2409.00510

3. DIGITAL TWIN

  1. Koshimura, S., Adriano, B., Mas, E., Nagat, S. & Takeda, Y. (2024). Tsunami Digital Twin – Concept, progress, and application to the 2024 Noto Peninsula event. EGUsphere. https://doi.org/10.5194/egusphere-egu24-14673
  2. Kosaka, N., Moriguchi, S., Shibayama, A., Kura, T., Shigematsu, N., Okumura, K., Mas, E., Okumua, M., Koshimura, S., Terada, K., Fujino, A., Matsubara, H. & Hisada, M. (2024). A study on digital model for decision-making in crisis response. Journal of Disaster Research, 19(3), 489–500. https://doi.org/10.20965/jdr.2024.p0489
  3. Koshimura, S. & Mas, E. (2023). Digital twin computing for enhancing resilience of disaster response systems. EGUsphere abstracts. https://doi.org/10.5194/egusphere-egu23-11756