Automated Floodwater Depth Estimation Using Large Multimodal Model for Rapid Flood Mapping

TL;DR

This paper introduces an automated method using GPT-4 Vision to estimate floodwater depth from on-site photos, enabling rapid and reliable flood mapping crucial for emergency response.

Contribution

It presents a novel application of a large multimodal model for floodwater depth estimation directly from flood photos, improving speed and resource efficiency.

Findings

Rapid floodwater depth estimation from photos demonstrated

Method provides consistent and reliable results

Enables near-real-time flood severity assessment

Abstract

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.