Euler et al. (2026) Rethinking the Flow: A Canal-Based Machine Learning Approach to Urban Flood Detection

Identification

• Journal: IEEE Transactions on Geoscience and Remote Sensing
• Year: 2026
• Date: 2026-01-01
• Authors: Quentin Euler, Cynthia Gerlein‐Safdi
• DOI: 10.1109/tgrs.2026.3671457

Research Groups

• N/A (Not specified in the provided text)

Short Summary

This paper proposes a machine learning approach that utilizes canal-based data for the detection of urban floods.

Objective

• To develop and evaluate a machine learning methodology for urban flood detection, specifically leveraging canal system data.

Study Configuration

• Spatial Scale: Urban environments featuring canal infrastructure.
• Temporal Scale: N/A (Not specified in the provided text)

Methodology and Data

• Models used: Machine learning models.
• Data sources: Canal-related data.

Main Results

• N/A (Not specified in the provided text)

Contributions

• Introduces a novel canal-based machine learning approach for urban flood detection, offering a new perspective on flood monitoring strategies.

Funding

• N/A (Not specified in the provided text)

Citation

@article{Euler2026Rethinking,
  author = {Euler, Quentin and Gerlein‐Safdi, Cynthia},
  title = {Rethinking the Flow: A Canal-Based Machine Learning Approach to Urban Flood Detection},
  journal = {IEEE Transactions on Geoscience and Remote Sensing},
  year = {2026},
  doi = {10.1109/tgrs.2026.3671457},
  url = {https://doi.org/10.1109/tgrs.2026.3671457}
}