Using Non-authoritative Sources During Emergencies in Urban Areas - Computational Approaches for Urban Environments - page 340

Fig. 14.8 Storm surge extent generated by FEMA and the locations of Civil Air Patrol photos and
geolocated videos ( a and b ). Flood damage assessment generated from non-authoritative data and
the subsequent classification of potential road damages ( c and d )
to be considered and is often used with Earth science data (Oliver and Webster
1990 ;OleaandOlea 1999 ; Waters 2009 ). Ordinary kriging generated a strong inter-
polation model. Cross-validation statistics yielded a standardized mean prediction
error of 0.0008 and a standardized root-mean-squared prediction error of 0.9967.
Figure 14.8 c illustrates the damage assessment, with values ranging from 1 (no
damage) to 10 (severe damage), created from the interpolated surface which is
clipped to the boundaries of the FEMA surge extent (Fig. 14.8 a) demonstrating how
non-authoritative sources can be used to add value to the FEMA map.
Ground information in the form of geolocated videos (Fig. 14.9 ) enhances the
non-authoritative dataset by providing flood information not conveyed in the CAP
photos. As illustrated in Fig. 14.8 b, the locations of the videos (green triangles)
did not coincide with the locations of photos rated as medium/severe damage
(larger orange circles, values 7-10). Reasons for this disparity may include flooding
captured on video had receded before the Civil Air Patrol flights or were captured at
night or flooding may have occurred in areas which were not in a flight path or were
unable to be seen from aerial platforms (i.e., flooding in tunnels, under overpasses).
By using multiple data sources, flood or damage details not captured by one source
can be provided by another.