ELEV-VISION: Automated Lowest Floor Elevation Estimation from Segmenting
  Street View Images
Yu-Hsuan Ho Cheng-Chun Lee Nicholas D. Diaz Samuel D. Brody Ali Mostafavi
We propose an automated lowest floor elevation (LFE) estimation algorithm
based on computer vision techniques to leverage the latent information in
street view images. Flood depth-damage models use a combination of LFE and
flood depth for determining flood risk and extent of damage to properties. We
used image segmentation for detecting door bottoms and roadside edges from
Google Street View images. The characteristic of equirectangular projection
with constant spacing representation of horizontal and vertical angles allows
extraction of the pitch angle from the camera to the door bottom. The depth
from the camera to the door bottom was obtained from the depthmap paired with
the Google Street View image. LFEs were calculated from the pitch angle and the
depth. The testbed for application of the proposed method is Meyerland (Harris
County, Texas). The results show that the proposed method achieved mean
absolute error of 0.190 m (1.18 %) in estimating LFE. The height difference
between the street and the lowest floor (HDSL) was estimated to provide
information for flood damage estimation. The proposed automatic LFE estimation
algorithm using Street View images and image segmentation provides a rapid and
cost-effective method for LFE estimation compared with the surveys using total
station theodolite and unmanned aerial systems. By obtaining more accurate and
up-to-date LFE data using the proposed method, city planners, emergency
planners and insurance companies could make a more precise estimation of flood
damage.