Tsunami Damage Detection with Remote Sensing: A Review

Abstract

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.

The 2011 Tohoku Tsunami from the Sky: A Review on the Evolution of Artificial Intelligence Methods for Damage Assessment

“…In particular, we want to highlight how these methods have evolved through time, from simple index computation and manual mapping using thresholds, to powerful Deep Learning Algorithms that require high-precision images and data. Our review can be seen as complementary to the one from Koshimura et al [6], in which the authors discuss different advances in remote-sensing technologies and their impact on the study of tsunamis in several aspects, such as the physics and acquisition of tsunami features, manual and automated damage interpretation with different modes of acquisition, and global response frameworks from remote-sensing images. In addition to a broader coverage of recent methods, our review focuses only on the artificial intelligence aspects, with a stronger focus on Deep Learning, and goes into the details of a dozen recent algorithms for automated damage assessment.…”

“…In particular, just as for optical images, certain land cover surfaces, such as water, forests areas and urban areas, have known backscattering and reflectance histograms. For example, in [6,38], the authors used the reflectance and signal backscattering histograms to detect water bodies, which may indicate the ocean, but also flooded areas. Numerous AI approaches have also been used to detect damages at the building-unit scale, most of them relying on TerraSAR-X images [7,8,10].…”

“…As a result, our review of the literature indicates that the former community under-exploits existing AI methods and still heavily relies on manual data curation and basic, on-the-shelf AI algorithms, while the latter community lacks the field knowledge to apply state-of-the-art AI algorithms in the ways that would be most useful for real applications. We encourage potential readers to consider reading both reviews if they are interested in obtaining a broader picture of the subject, and to focus on the review from Koshimura et al [6] for a more application-focused perspective, or if they want details on the damage-assessment methods that are not covered in this work. Another, and certainly more modest, contribution is that, in this paper, we try to unify the different notations and terms used for the different categories of damage, especially the terminology of building damage, which varies greatly between studies.…”

Inundation Assessment of the 2019 Typhoon Hagibis in Japan Using Multi-Temporal Sentinel-1 Intensity Images

“…Klemas [12] and Lin et al [13] summarized recent studies on flood assessments using optical and SAR sensors. Koshimura et al [14] published a review on the application of remote sensing to tsunami disasters. Optical satellites are often used to collect post-flood information but are limited by weather conditions, making it difficult to monitor flood situations continuously.…”

Multi-UAV Path Planning Methodology for Postdisaster Building Damage Surveying

“…Especially when the accident at the Fukushima Daiichi Nuclear Power Plant occurred, satellite remote sensing and unmanned aerial vehicles (UAVs) were crucial platforms to avoid human exposure to radiation on the premises of the power plant 5,20 . Significant progress has been made in the estimation of damage sustained from a disaster using satellite remote sensing 7,19 ; however, the details of damage to buildings typically remain unclear because of the scarcity of information about the condition of building walls. For this reason, increasing attention is being paid to structure-from-motion (SfM) and multiview stereo (MVS) photographic surveys conducted using UAVs 11,21,22 .…”