This photo shows the distribution of damage estimated
by the convolutional neural network model for Mashiki town
in the 2016 Kumamoto earthquake (L) and Nishinomiya City
in the 1995 Kobe earthquake (R). Hiroshima University researchers
created a post-disaster damage assessment CNN model
that does not need pre-disaster images to make an evaluation.
Researchers at Hiroshima University have taught
an AI to look at post-disaster aerial images
and accurately determine how battered the buildings
are – a technology that crisis responders can use to map damage
and identify extremely devastated areas where help is needed the most.
Quick action in the first 72 hours after a calamity is critical
in saving lives. And the first thing disaster officials
need to plan an effective response is accurate damage assessment.
But anyone who has seen aftermath scenes of a natural
catastrophe knows the many logistical challenges
that can make on-site evaluation a danger to the lives of crisis responders.
Using convolutional neural network (CNN) – a deep learning
algorithm inspired by the human brain’s image recognition
process – a team led by Associate Professor Hiroyuki Miura
of Hiroshima University’s Graduate School of Advanced Science
and Engineering trained an AI to finish in an instant
a task that usually requires us to devote crucial hours
and personnel at a time when resources are scarce.
Previous CNN models that assess damage require both before
and after photos to give an evaluation. But Miura’s model
doesn’t need pre-disaster images.
It only relies on post-disaster photos to determine building damage.
It works by classifying buildings as collapsed, non-collapsed,
or blue tarp-covered based on the seven damage scales (D0-D6)
used in the 2016 Kumamoto earthquakes by the Architectural Institute of Japan.
A collapsed building is defined as D5-D6 or major damage.
Non-collapse is interpreted as D0-D1 or negligible damage.
Intermediate damage, which was rarely considered in previous CNN models,
is designated as D2-D3 or moderate damage.
A comparison of the damage scales used by the Architectural
Institute of Japan or AIJ scale and the European Macroseismic
Scale or EMS-98. Hiroshima University researchers trained
the AI to classify D0-D1 damage as non-collapse
while D5-D6 are interpreted as collapsed.
Researchers trained their CNN model using post-disaster aerial
images and building damage inventories by experts during
the 1995 Kobe and 2016 Kumamoto earthquakes.
The researchers overcame the challenge of identifying
buildings that suffered intermediate damage after
confirming that blue tarp-covered structures in photos
used to train the AI predominantly represented D2-D3 levels of devastation.
Since ground truth data from field investigations
of structural engineers were used to teach the AI,
the team believes its evaluations are more reliable
than other CNN models that depended on visual interpretations of non-experts.
When they tested it on post-disaster aerial images
of the September 2019 typhoon that hit Chiba,
results showed that damage levels of approximately
94% of buildings were correctly classified.
Now, the researchers want their AI to outdo itself
by making its damage assessment more powerful.
“We would like to develop a more robust damage
identification method by learning more training data
obtained from various disasters such as landslides,
tsunami, and etcetera,” Miura said.
“The final goal of this study is the implementation
of the technique to the real disaster situation.
If the technique is successfully implemented,
it can immediately provide accurate damage maps not only
damage distribution but also the number of damaged buildings
to local governments and governmental agencies.”
Provided by the IKCEST Disaster Risk Reduction Knowledge Service System