Automated Floodwater Depth Estimation Using Large Multimodal Model for
  Rapid Flood Mapping
Temitope Akinboyewa Huan Ning M. Naser Lessani Zhenlong Li
Information on the depth of floodwater is crucial for rapid mapping of areas
affected by floods. However, previous approaches for estimating floodwater
depth, including field surveys, remote sensing, and machine learning
techniques, can be time-consuming and resource-intensive. This paper presents
an automated and fast approach for estimating floodwater depth from on-site
flood photos. A pre-trained large multimodal model, GPT-4 Vision, was used
specifically for estimating floodwater. The input data were flooding photos
that contained referenced objects, such as street signs, cars, people, and
buildings. Using the heights of the common objects as references, the model
returned the floodwater depth as the output. Results show that the proposed
approach can rapidly provide a consistent and reliable estimation of floodwater
depth from flood photos. Such rapid estimation is transformative in flood
inundation mapping and assessing the severity of the flood in near-real time,
which is essential for effective flood response strategies.