Amateur drone videos could aid in natural disaster damage assessment


Date:
August 28, 2020
Source:
Carnegie Mellon University
Summary:
It wasn't long after Hurricane Laura hit the Gulf Coast Thursday
that people began flying drones to record the damage and posting
videos on social media. Those videos are a precious resource, say
researchers who are working on ways to use them for rapid damage
assessment. By using artificial intelligence, the researchers are
developing a system that can automatically identify buildings and
make an initial determination of whether they are damaged and how
serious that damage might be.



FULL STORY ==========================================================================
It wasn't long after Hurricane Laura hit the Gulf Coast Thursday that
people began flying drones to record the damage and posting videos on
social media.

Those videos are a precious resource, say researchers at Carnegie
Mellon University, who are working on ways to use them for rapid damage assessment.


==========================================================================
By using artificial intelligence, the researchers are developing a
system that can automatically identify buildings and make an initial determination of whether they are damaged and how serious that damage
might be.

"Current damage assessments are mostly based on individuals detecting and documenting damage to a building," said Junwei Liang, a Ph.D. student
in CMU's Language Technologies Institute (LTI). "That can be slow,
expensive and labor- intensive work." Satellite imagery doesn't
provide enough detail and shows damage from only a single viewpoint -- vertical. Drones, however, can gather close-up information from a number
of angles and viewpoints. It's possible, of course, for first responders
to fly drones for damage assessment, but drones are now widely available
among residents and routinely flown after natural disasters.

"The number of drone videos available on social media soon after a
disaster means they can be a valuable resource for doing timely damage assessments," Liang said.

Xiaoyu Zhu, a master's student in AI and Innovation in the LTI, said the initial system can overlay masks on parts of the buildings in the video
that appear damaged and determine if the damage is slight or serious,
or if the building has been destroyed.


========================================================================== Story Source: Materials provided by Carnegie_Mellon_University. Original written by Byron Spice. Note: Content may be edited for style and length.


==========================================================================


Link to news story: https://www.sciencedaily.com/releases/2020/08/200828081023.htm

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