Automated Floodwater Depth Estimation Using Large Multimodal Model for Rapid Flood Mapping
This provides a fast, automated solution for flood response teams to assess flood severity quickly, though it is incremental as it applies an existing model to a new domain.
The paper tackled the problem of estimating floodwater depth from on-site photos by using GPT-4 Vision to analyze images with reference objects like street signs and cars, achieving rapid and reliable depth estimation for near-real-time flood mapping.
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.