組織・メンバー

• 災害ジオインフォマティクス研究分野

Vescovo, R., Adriano, B., Wiguna, S., Ho, C. Y., Morales, J., Dong, X., Ishii, S., Wako, K., Ezaki, Y., Mizutani, A., Mas, E., Tanaka, S., and Koshimura, S.: The 2024 Noto Peninsula earthquake building damage dataset: Multi-source visual assessment, Earth Syst. Sci. Data Discuss. [preprint], https://doi.org/10.5194/essd-2024-363, in review, 2025.

Vescovo, R., Mas, E., Adriano, B., Koshimura, S. (2023): Deep learning of tsunami building damage from multimodal physical parameters for real-time damage assessment, XXVIII General Assembly of the International Union of Geodesy and Geophysics (IUGG) (Berlin 2023).
https://doi.org/10.57757/IUGG23-2256

Vescovo, R., Adriano, B., Mas, E. et al. Beyond tsunami fragility functions: experimental assessment for building damage estimation. Sci Rep 13, 14337 (2023). https://doi.org/10.1038/s41598-023-41047-y

S. Wiguna, B. Adriano, E. Mas and S. Koshimura, "Evaluation of deep learning models for building damage mapping in emergency response settings", IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., vol. 17, pp. 5651-5667, 2024.