V-FloodNet: A video segmentation system for urban flood detection and quantification

Effective monitoring and forecasting of urban flooding are crucial for climate change adaptation and resilience around the world. We proposed a novel and automatic system for urban flood detection and quantification. Our software takes image/video data of flooding as inputs because the such data source is easy to obtain and widely available compared with conventional water level sensors or flood gauges. First, the kernel of our system is a robust water region segmentation module that detects flooded regions together with surrounding reference objects from the scene. We combine image and video segmentation technologies to make the system reliable under varying weather and illumination conditions. Second, our system uses the detected situated objects to determine the inundation depth. Field experiments demonstrate that our segmentation results are accurate and reliable; and our system can detect flooding and estimate inundation depths from images and time-lapse videos. Our code is available at https://github.com/xmlyqing00/V-FloodNet.

Automated summary

We proposed a novel and automatic system for urban flood detection and quantification using image/video data as inputs. Our system effectively detects flooding and estimates inundation depths by combining water region segmentation and image/video segmentation technologies.