- 【Updated on May 12, 2025】 Integration of CiNii Dissertations and CiNii Books into CiNii Research
- Trial version of CiNii Research Automatic Translation feature is available on CiNii Labs
- Suspension and deletion of data provided by Nikkei BP
- Regarding the recording of “Research Data” and “Evidence Data”
Tsunami Damage Detection with Remote Sensing: A Review
-
- Shunichi Koshimura
- International Research Institute of Disaster Science, Tohoku University, Sendai 980-8572, Japan
-
- Luis Moya
- International Research Institute of Disaster Science, Tohoku University, Sendai 980-8572, Japan
-
- Erick Mas
- International Research Institute of Disaster Science, Tohoku University, Sendai 980-8572, Japan
-
- Yanbing Bai
- Center for Applied Statistics, School of Statistics, Renmin University of China, Beijing 100872, China
Description
<jats:p>Tsunamis are rare events compared with the other natural disasters, but once it happens, it can be extremely devastating to the coastal communities. Extensive inland penetration of tsunamis may cause the difficulties of understanding its impact in the aftermath of its generation. Therefore the social needs to technologies of detecting the wide impact of great tsunamis have been increased. Recent advances of remote sensing and technologies of image analysis meet the above needs and lead to more rapid and efficient understanding of tsunami affected areas. This paper provides a review of how remote sensing methods have developed to contribute to post-tsunami disaster response. The evaluations in the performances of the remote sensing methods are discussed according to the needs of tsunami disaster response with future perspective.</jats:p>
Journal
-
- Geosciences
-
Geosciences 10 (5), 177-, 2020-05-12
MDPI AG
- Tweet
Keywords
- FOS: Political science
- Aerospace Engineering
- FOS: Mechanical engineering
- FOS: Law
- Environmental science
- damage detection
- Remote Sensing
- remote sensing
- Global Flood Risk Assessment and Management
- Engineering
- Meteorology
- Media Technology
- Disaster response
- Environmental resource management
- Political science
- Environmental planning
- QE1-996.5
- Global and Planetary Change
- Geography
- Synthetic Aperture Radar Interferometry
- Natural disaster
- deep learning
- Geology
- Remote sensing
- Hyperspectral Image Analysis and Classification
- Computer science
- Emergency management
- machine learning
- Physical Sciences
- Environmental Science
- tsunami
- Law
Details 詳細情報について
-
- CRID
- 1360853567350749056
-
- ISSN
- 20763263
-
- Article Type
- journal article
-
- Data Source
-
- Crossref
- KAKEN
- OpenAIRE