Adding up to all the things that Artificial Intelligence
is capable of doing, rescue during floods can be added to
the list. A group of researchers from Tohoku University
has come up with a machine learning model which can identify
accurately the flooded building within 24 hours of the disaster.
Artificial Intelligence (AI) has been considered to be a vital
tool which can be used for various purposes. Artificial Intelligence
has proved to be helpful in quite a any situations when the
human capabilities are limited. AI has been used in the medical
field as well as engineering, it is capable to handle diverse
amount of problems.
Adding up to all the things that Artificial Intelligence is capable
of doing, rescue during floods can be added to the list. A group
of researchers from Tohoku University has come up with a machine
learning model which can identify accurately the flooded building
within 24 hours of the disaster.
The machine learning model uses photos that are given out by the
news media in order to identify the flooded buildings. This will
be considered to be very helpful in the rescue missions as the
involvement of Artificial Intelligence will be able to speed up
the process to detect the buildings which are flooded immediately
after a flood occurring on a large scale. This will also allow
the emergency rescue personnel to perform the rescue efficiently.
This research study was published in the journal Remote Sensing
on April, 2021. Shunichi Koshimura of Tohoku University’s International
Research Institute of Disaster Science who is the co-author of this
study said, “Our model demonstrates how the rapid reporting of
news media can speed up and increase the accuracy of damage mapping
activities, accelerating disaster relief and response decisions.”
This study also makes a very good use of the news media while
developing this system. Since the news crews and the media usually
arrive on the scene first in order to broadcast the images of the
disaster for the viewers, the research team working on this study
decided to incorporate this information in the machine learning
process and the AI algorithms.
The resulted classification shows flooded buildings (red), non-flooded
buildings (blue), the learning data from news media (green) and the flooded
area (yellow). About 80% of the estimated flooded buildings were actually
flooded in the event.
These algorithms are developed to classify objects with the help of image
analysis. And for the Artificial Intelligence and the Machine Learning
process to be accurate and effective, a lot of data is require to train
these models, in this case, it’s the data about floods.
The difficulty faced over here is, one cannot simply use flood data from
past events as the situation is different every time the calamity strikes.
The situation and the damage done changes for each event. Thus on site
data has the highest amount of reliability.
The application of the model was done at the Mabi-Cho, Kurashiki city in
Okayama Prefecture. This area was affected by heavy rains across western
Japan in the year 2018. The researchers started by identifying the photos
which were given out by the press and then based on the landmarks and
other clues which were apparent in the photo, they to geolocate them as
accurately as possible.
They made use of the synthetic aperture radar (SAR) PALSAR-2 images
provided by JAXA to discretize flooded and non-flooded conditions of
unknown areas. One of the machine learning techniques, support vector
machine (SVM), was employed in order to classify the buildings which
were surrounded by the floodwaters and within non flooded areas.
Shunichi Koshimura speaking about the project said that, “The performance
of our model resulted in an 80% estimation accuracy,”
Provided by the IKCEST Disaster Risk Reduction Knowledge Service System