Automated urban waterlogging assessment and early warning through a mixture of foundation models

TL;DR

This paper introduces UWAssess, an AI framework that automatically detects urban waterlogging in images and generates detailed assessment reports, enhancing real-time monitoring and response capabilities amid climate change challenges.

Contribution

The study develops a foundation model-driven system with semi-supervised fine-tuning and chain-of-thought prompting for waterlogging assessment, addressing data scarcity and improving report accuracy.

Findings

Significant improvements in perception accuracy on visual benchmarks.

Reliable textual reports describing waterlogging details.

Framework supports urban management and disaster response.

Abstract

With climate change intensifying, urban waterlogging poses an increasingly severe threat to global public safety and infrastructure. However, existing monitoring approaches rely heavily on manual reporting and fail to provide timely and comprehensive assessments. In this study, we present Urban Waterlogging Assessment (UWAssess), a foundation model-driven framework that automatically identifies waterlogged areas in surveillance images and generates structured assessment reports. To address the scarcity of labeled data, we design a semi-supervised fine-tuning strategy and a chain-of-thought (CoT) prompting strategy to unleash the potential of the foundation model for data-scarce downstream tasks. Evaluations on challenging visual benchmarks demonstrate substantial improvements in perception performance. GPT-based evaluations confirm the ability of UWAssess to generate reliable textual reports that accurately describe waterlogging extent, depth, risk and impact. This dual capability enables a shift of waterlogging monitoring from perception to generation, while the collaborative framework of multiple foundation models lays the groundwork for intelligent and scalable systems, supporting urban management, disaster response and climate resilience.