damage. The fine-scale damage detection products described
in this research should provide valuable information to image
analysts and subsequently to emergency response personnel,
as they will allow the detection and delineation, in near real-
time, of specific damage features that could pose a threat to
critical infrastructure. The addition of this actionable informa-
tion to the disaster response process could drastically reduce
the amount of time and resources needed to ensure the safety
and structural integrity of the critical infrastructure surveyed.
The incorporation of this method would add another strong
link in the image chain, and represents a fertile area for further
exploration and development in the fields of both remote
sensing and disaster management (Yamazaki, 2001). Informa-
tion gained through this research could support emergency
management personnel during the response and mitigation
phase after a natural or anthropogenic disaster by increasing
situational awareness through the accurate identification of
damage to critical infrastructure that is automatically gener-
ated in near real-time.
Our future research will involve damage detection using
convolutional neural networks (
CNNS
) and other advanced
machine learning algorithms, by building on the procedures
and successes of the raster processing model. The ability
of
CNNS
to accurately detect and delineate cracks on many
images having different spatial resolutions and orientations,
without extensive preprocessing, makes it attractive for rapid,
post-hazard damage assessment.
Acknowledgments
The work was partially funded by the National Science
Foundation Directorate of Engineering, Infrastructure Man-
agement and Extreme Events (
IMEE
) program (Grant Number
G00010529), United States Department of Transportation (
US-
DOT
) Office of the Assistant Secretary for Research & Technolo-
gy (
OST-R
) Commercial Remote Sensing and Spatial Information
(
CRS&SI
) Technologies Program (Cooperative Agreement Num-
ber
OASRTRS
-14-
H
-
UNM
), and by the Department of Geography
at San Diego State University (
SDSU
). The authors acknowledge
the contributions of Andrew Loerch (
SDSU
), Richard McCreight
of
NEOS
,
LTD
, and researchers at University of New Mexico for
their help in collecting aerial imagery used in this study.
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