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

by   Ruo-Qian Wang, et al.

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.


page 14

page 15

page 16

page 17


Semi-Supervised Semantic Segmentation in Earth Observation: The MiniFrance Suite, Dataset Analysis and Multi-task Network Study

The development of semi-supervised learning techniques is essential to e...

Water Surface Patch Classification Using Mixture Augmentation for River Scum Index

Urban rivers provide a water environment that influences residential liv...

Not Another Day Zero: Design Hackathons for Community-Based Water Quality Monitoring

This study looks at water quality monitoring and management as a new for...

A Fully Automated and Scalable Surface Water Mapping with Topographic Airborne LiDAR Data

Reliable and accurate high-resolution maps of surface waters are critica...

Neuroevolution deep learning architecture search for estimation of river surface elevation from photogrammetric Digital Surface Models

Development of the new methods of surface water observation is crucial i...

Predictive modeling of microbiological seawater quality classification in karst region using cascade model

In this paper, an in-depth analysis of Escherichia coli seawater measure...

Please sign up or login with your details

Forgot password? Click here to reset