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Related Experiment Videos

Vision-Based Environmental Sensing for Flood Risk Forecasting: Dataset Relabeling and Temporal Multi-Task Learning.

Seungju Lee1, Gooman Park1

  • 1Department of Smart ICT Convergence Engineering, Seoul National University of Science and Technology, Seoul 01811, Republic of Korea.

Sensors (Basel, Switzerland)
|June 12, 2026
PubMed
Summary

Related Concept Videos

Design Example: Analyzing Capacity Contours for Flood Risk Assessment01:17

Design Example: Analyzing Capacity Contours for Flood Risk Assessment

Flood risk assessment involves careful planning and analysis to ensure the safety of communities near water retention structures. Capacity contours are a vital tool in this process, as they illustrate the potential spread of water at specific levels in a given area. In the context of building a bund across a small valley, these contours play a critical role in evaluating the safety of nearby residential areas.In this example, the bund is intended to store stormwater in the valley. The engineers...

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This study reformulates CCTV flood data and uses temporal modeling for better flood risk forecasting. An image-only model outperformed multimodal approaches, highlighting the need for data consistency in flood prediction systems.

Area of Science:

  • Environmental Science
  • Computer Science
  • Artificial Intelligence

Background:

  • River flooding and urban inundation necessitate advanced forecasting systems capable of predicting future risks.
  • Existing closed-circuit television (CCTV)-based flood datasets often suffer from imbalanced or temporally inconsistent risk labels.
  • Current image-based flood analysis methods are largely confined to static scene understanding.

Purpose of the Study:

  • To propose a dataset reformulation and temporal multi-task forecasting framework for CCTV-based flood-risk prediction.
  • To address limitations in existing flood datasets and image-based approaches for dynamic risk assessment.
  • To investigate the effectiveness of multimodal sensor fusion in flood forecasting.

Main Methods:

  • A site-relative relabeling strategy was developed to convert noisy frame-level annotations into four distinct risk levels using visual and environmental cues.
Keywords:
CCTVflood predictionmultimodal learningrisk estimationsemantic segmentationtime-series forecasting

Related Experiment Videos

  • The dataset was transformed from frame-based to site-hour sequences to enable multi-horizon forecasting (1, 3, and 6 hours).
  • Image-only, weather-only, and naive multimodal configurations were evaluated to assess sensor fusion limitations.

    • Main Results:

    • The reformulated dataset enabled an image-only temporal model to achieve superior performance, with a mean Intersection over Union (mIoU) of 0.892 and a Dice score of 0.940.
    • Naive multimodal fusion significantly degraded performance, reducing macro-averaged F1 score (Macro-F1) to 0.267 and high-risk recall to 0.070.
    • Ablation studies revealed that temporal modeling was crucial, with its removal decreasing Macro-F1 to 0.227 and high-risk recall to 0.000.

    Conclusions:

    • Dataset reformulation and temporal modeling are essential for advancing CCTV-based flood analysis from static estimation to dynamic risk forecasting.
    • The study underscores the challenges of multimodal sensor fusion when dealing with noisy, weakly correlated, or temporally misaligned cross-modal signals.
    • Robust cross-modal alignment is a prerequisite for achieving reliable performance gains through multimodal sensing in flood prediction.