Real-time Flood Reconstruction and Virtual Water Gauge

Project Overview

This project aims to develop AI-assisted tools to analyze and reconstruct flood from image and video data captured by (survalence/traffic) cameras. The key new technologies to develop are video object segmentation (VOS) techniques, real-time detection and reconstruction techinques for objects with rapid appearance changes under severe weather.

Effectively esimating water level and constructing flood hydrographs in urban areas in real-time during flash floods, hurricanes, and other extreme weather events remains as a difficult task. Water in such scenes often has rapidly changing appearance caused by free-form self-deformation, environment illumination, reflections, wave, ripples, turbulence, sediment concentration, etc. Such rapidly changing appearance often leads to less accurate water detection and segmentation, and consequently, unreliable flood/water level estimation.

We have maintained an annotated water database and benchmark to support the training of video water detetion and segmentation.

We have developed cutting-edge deep water segmentation pipelines, and referece object detection and size estimation networks for real-time innundation map estimation.

Publications

V-FloodNet: A Video Segmentation System for Urban Flood Detection and Quantification

Yongqing Liang, Xin Li, Brian Tsai, Navid Jafari, Qin Chen

Environmental Modelling and Software, Vol. 163, Article 105586, 2022

[Paper] [Codes]



Video Object Segmentation with Adaptive Feature Bank and Uncertain-Region Refinement

Yongqing Liang, Xin Li, Navid Jafari, Qin Chen

Neural Information Processing Systems (NIPS), 2020

[Paper] [Supplementary Doc] [Codes]



WaterNet: An adaptive matching pipeline for segmenting water with volatile appearance

Yongqing Liang, Navid Jafari, Xing Luo, Qin Chen, Yanpeng Cao, and Xin Li

Computational Visual Media, 6:65-78, 2020

[Paper] [Codes]


Real-Time Water Level Monitoring using Live Cameras and Computer Vision Techniques

Navid H. Jafari, Xin Li, Qin Chen, Canyu Le, Logan P. Betzer, and Yongqing Liang

Computers and Geosciences, Vol. 147, Article 104642, 2021

[Paper] [Codes]

Dataset and Pre-trained Model

    The WaterV1 contains:

  • Training Set: 7 vdieo clips with annotation.
  • Test Set: 1 evaluation video.

    • The WaterV2 contains:

  • Training Set: 2188 water images with annotation.
  • Test Set: 20 evaluation videos.

    • A Pretrained WaterNet Model: Trained on WaterV2 Training Set for 200 Epochs

Sponsors

  • We gratefully acknowledge the support of National Science Foundation (EAR-1760582) and Louisiana State Board of Regents (ITRS LEQSF(2018-21)-RD-B-03) on this research.


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