Chapter 13
Development and Future Perspectives ofTsunami Damage Assessment Technology
Field of expertise: Wide-area damage assessment research

Shunichi Koshimura & Erick Mas
SummaryThis paper outlines the current status and prospects of research on the use of remote sensing forunderstanding the wide-area damage caused by tsunamis, focusing on the following issues:
understanding the inundation area and run-up characteristics, extracting building damage,understanding debris, and searching for victims.
Keywords: tsunami disaster, wide-area damage assessment, remote sensing, geo-informatics,
disaster response

Introduction
Remote sensing is a crucial technology to assess damage in a wide area during a majordisaster. Remote sensing was also used effectively in the 2011 Great East Japan Earthquake andmade contributions to assessing damage and monitoring recovery in the affected areas. In ten
years since then, significant changes and advances have been achieved in sensor performance,diversification of platforms, and sophistication of analysis technology.

1: Problems Revealed by the Great East Japan Earthquake
What happened?
One of the most critical issues in disaster response and relief activities in the affected areas
is understanding the full extent of the impacts. In the aftermath of a disaster, information from theseverely affected areas is fragmented, making it extremely difficult to assess the full extent of thedamage, and relief and recovery activities in the affected areas would be challenging. After the2011 Great East Japan Earthquake disaster, the affected areas were vast, and it was tough toquickly determine where the severely damaged areas were. The survey's duration and humanresources for damage assessment efforts were restricted, so it was necessary to assess thesituation from outside the affected areas.
The reality of the damage
1Immediately after the 2011 Great East Japan Earthquake, several organizations conductedemergency observations. For instance, Japan Aerospace Exploration Agency (JAXA) wasoperating the Advanced Land Observing Satellite "Daichi" during the 2011 event. The satellite
contained three observation sensors: the Panchromatic Remote-sensing Instrument for Stereo
Mapping (PRISM) to generate topographic data with its optical stereoscopic observation, theAdvanced Visible and Near Infrared Radiometer type 2 (AVNIR-2) to monitor land cover and landuse, and the Phased Array type L-band Synthetic Aperture Radar (PALSAR). The AVNIR-2 sensor
has three bands of visible light (RGB) and near-infrared light (NIR), and its ground resolution is 10
meters in the nadir direction. Figure 13-1 shows the inundation area map using the ALOS AVNIR-2
image on March 14, 2011. The inundation area was identified by focusing on the spectralreflectance characteristics of water with the threshold values.

Figure 13-1. The tsunami inundation area (blue area) extracted using ALOS AVNIR-2 imagery. The
black solid line is the inundation line extracted from aerial photographs by the Geospatial
Information Authority of Japan, and the green dots are the results of field surveys pointing out thetsunami inundation limits.
In addition, aerial photographs were widely used to assess building damage. In fact, aerial
photographs taken by the Geospatial Information Authority of Japan (GSI) made significant
contributions to understanding building damage levels by visual interpretation. The authors used
these aerial photographs of the disaster area in Miyagi Prefecture to extract damage information of160,000 buildings. One drawback of this effort was the time requirement because it is interpreted
and deciphered by human eyes. This problem will soon be solved with advances of machinelearning (AI) which have been developing rapidly in recent years.

2: Paradigms Destroyed by the Earthquake
Conventional wisdom and necessary responses (Recognition, knowledge, dogma)
2Table 13-1 summarizes the sensor platforms and analysis methods used for assessing
wide-area damage in research efforts conducted immediately after the 2011 Great East Japan
earthquake disaster. There is now a wide range of platforms used for remote sensing including
satellites, aircraft, and unmanned aerial vehicles (UAVs). Optical sensors that capture visible andnear-infrared light, synthetic aperture radar (SAR), and LiDAR (Light Detection and Ranging) aremainly used. It is necessary to use diverse sensors and platforms, considering these needs.
Table 13-1. Sensor platforms for tsunami wide-area damage assessment
Extraction
details
Tsunamiinlandpenetration
and run-up
Tsunamiinundationzone
Building
damageTsunamidebris

Searching forVictimsSensor/Platform

Analysis method

Aircraft, UAV/Optical sensor, Video

Visual inspection, edge extraction

Satellite, Aircraft, UAV/
Optical and infrared sensor,
Synthetic Aperture Radar(SAR)
Satellite, Aircraft, UAV/
High-resolution opticalsensor, Synthetic Aperture
Radar (SAR)
Satellite, Aircraft, UAV/
High-resolution opticalsensor, Synthetic Aperture
Radar (SAR), (Light
Detection and Ranging(LiDAR)
Aircraft, UAV/ Highresolution optical sensor,
Video

Visual inspection, Image filter,
Change detection

Visual inspection, Image filter,
Change detection, Machinelearning
Supervised and unsupervisedclassification, Machine learning, 3D
measurement, and mapping

Image recognition, Machinelearning

3: A New Approach
In particular, the analysis methods aiming at a quantitative understanding of building
damage have been developed using synthetic aperture radar, which can observe day and night,
regardless of weather conditions. The method that has been the most widely applied is the change
detection method. It is common to relate changes in pre- and post-event datasets to the degree ofdamage to determine thresholds and construct damage classifiers. Figure 13-2 shows an exampleof building damage extraction in the tsunami-affected area using TerraSAR-X images by applying atwo-step method; first extracting the building area, then constructing a classifier based on theDecision Tree method. It has been demonstrated that 80% accuracy was achieved to extractwashed-away buildings. This change detection method can show excellent results only when thedata before and after the disaster are obtained under the same acquisition conditions. Besides, this3method cannot be utilized in the case where only post-disaster images are available.
Machine learning methods can overcome this drawback. Advanced methods are being
developed to extract building damage only from post-disaster data, by applying machine learning
methods with training data on building damage prepared in advance. For instance, Figure 13-3
shows the results of extracting building areas using Deep Neural Network (DNN) called
SqueezeNet and then classifying the damage using a convolutional neural network architecture
called wide residual networks (Wide ResNet). The results are consistent with conventional change
detection methods (total accuracy of about 75%). This implies that by preparing appropriate
training data, AI can perform rapid damage assessment.
The characteristics and advantages of remote sensing include wide coverage, periodicity(the ability to observe the same place at a specific time interval), and continuity (the ability tocontinue observation for an extended time). In other words, it is possible to monitor not only widearea damage immediately after the occurrence of a disaster but also continuously monitor it. In the2011 Great East Japan earthquake, it was estimated that more than 23 million tons of debris werecaused in the three prefectures of Tohoku. The rapid removal of debris was an important andurgent issue in accelerating disaster recovery, and for this purpose quantitative monitoring ofdebris removal activity was crucial. We have developed a method to quantitatively monitor theaccumulation and distribution of debris in three-dimensional form using aerial photographs andLiDAR data. Figure 13-4 shows the results of the 3D extraction of debris in Onagawa Town, Miyagi
Prefecture. The total volume of debris in this area was estimated to be 238,000m3, and considering
the bulk density of debris of 1.2t/m (based on the National Institute for Environmental Studies), thetotal weight was estimated to be 286,000t. Despite the difficulty to measure and monitor the debris
directly on-site regularly and continuously, the remote sensing results show that the amount ofdebris could be determined quickly and quantitatively by integrated analysis of aerial photographs
and LiDAR data.

Figure 13-2. Extraction results of tsunami-induced building damage in Sendai City and WatariTown, Miyagi Prefecture. (A) and (C) are the results of the survey, and (B) and (C) are the resultsof damage extraction from TerraSAR-X images.
4Figure 13-3. Building damage extraction results obtained by deep learning only from post-event
TerraSAR-X data. (a) Building damage survey result, (b) Damage extraction result by deeplearning

Figure 13-4. Three-dimensional mapping of debris in Onagawa town, Miyagi Prefecture. The upper
figure shows a bird's eye view of the urban area of Onagawa, and the lower figure shows theestimated distribution of the debris.
5

4: Achievements and the Future
A new approach to disaster science
Remote sensing technology is expected to make great advances in the future, thanks to theuse of a variety of platforms, improvements in sensor performance and resolution, progress in
information and communication technology, and the use of AI for image recognition throughmachine learning. The challenge will be to ensure the rapidity of observation and satellite
constellation for emergency observation. Thus, international cooperation, coordination andstandardization of sensing and analysis technologies are necessary.
Unmanned aerial vehicles (UAVs) also need to improve mobility in tough conditions. The
development of hardware, sensors, analysis technologies, and interpretation methods must becarried out in a coordinated manner, and the capabilities of urgent operations of monitoringdisaster affected areas must be proved at various disaster sites while meeting the most criticaluser needs.

Conclusion - from the authors
More detailed technical explanations and examples can be found in our publications.

References
Koshimura, S., Moya, L., Mas, E., Bai, Y. (2020). Tsunami Damage Detection with RemoteSensing: A Review. Geosciences, 10(5), 177. https://doi.org/10.3390/geosciences10050177

6