Semi-supervised Identification and Mapping of Surface Water Extent using Street-level Monitoring Videos

TL;DR

This paper introduces a semi-supervised segmentation method combined with georeferencing techniques to accurately identify and analyze surface water extent from street-level monitoring videos, aiding urban flood management.

Contribution

It presents a novel semi-supervised segmentation approach and monoplotting-based georeferencing for urban water mapping from oblique camera footage, addressing data and boundary identification challenges.

Findings

Effective water boundary detection in complex urban scenes

Successful georeferencing of monitoring video pixels

Enhanced understanding of local drainage hydraulics

Abstract

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.