  Urban flooding is becoming a common and devastating hazard to cause life loss
and economic damage. Monitoring and understanding urban flooding in the local
scale is a challenging task due to the complicated urban landscape, intricate
hydraulic process, and the lack of high-quality and resolution data. The
emerging smart city technology such as monitoring cameras provides an
unprecedented opportunity to address the data issue. However, estimating the
water accumulation on the land surface based on the monitoring footage is
unreliable using the traditional segmentation technique because the boundary of
the water accumulation, under the influence of varying weather, background, and
illumination, is usually too fuzzy to identify, and the oblique angle and image
distortion in the video monitoring data prevents georeferencing and
object-based measurements. This paper presents a novel semi-supervised
segmentation scheme for surface water extent recognition from the footage of an
oblique monitoring camera. The semi-supervised segmentation algorithm was found
suitable to determine the water boundary and the monoplotting method was
successfully applied to georeference the pixels of the monitoring video for the
virtual quantification of the local drainage process. The correlation and
mechanism-based analysis demonstrates the value of the proposed method in
advancing the understanding of local drainage hydraulics. The workflow and
created methods in this study has a great potential to study other street-level
and earth surface processes.
