Li et al. (2026) Rapid Flood Mapping: Outcome of the 2024 IEEE GRSS Data Fusion Contest

Identification

• Journal: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
• Year: 2026
• Date: 2026-01-01
• Authors: Jiepan Li, He Huang, Wei He, Hongyan Zhang, Liangpei Zhang, Ting Liu, Mengke Yuan, Chaoran Lu, Kaixuan Lu, Baochai Peng, Heyang Duan, Mengya Li, Pan Zhang, Tao Wang, Tongkui Liao, Yansheng Li, Bo Dang, Fanyi Wei, Jieyi Tan, Yangjie Lin, Claudio Persello, Saurabh Prasad, V. Lonjou, Raquel Rodriguez-Suquet, Pauline Guntzburger, Vincent Poulain, Jacqueline Le Moigne, Benjamin Smith, Sujay V. Kumar, Thomas Huang, Sophie Ricci, Thanh Huy Nguyen, ANDREA PIACENTINI
• DOI: 10.1109/jstars.2026.3652462

Research Groups

[Not specified in the provided text, as this is an outcome report of a contest rather than a traditional research paper with listed groups.]

Short Summary

This paper details the outcomes and findings of the 2024 IEEE GRSS Data Fusion Contest, which focused on advancing rapid flood mapping techniques.

Objective

• To present the results, methodologies, and insights derived from the 2024 IEEE GRSS Data Fusion Contest on rapid flood mapping.

Study Configuration

• Spatial Scale: Not explicitly stated, but the context of flood mapping typically involves regional to local scales.
• Temporal Scale: Not explicitly stated, but "rapid" implies near real-time or very short-term analysis for flood events.

Methodology and Data

• Models used: Not specified in the provided text, but the contest likely involved various machine learning, deep learning, and remote sensing algorithms for data fusion and flood detection.
• Data sources: Not specified in the provided text, but a "Data Fusion Contest" implies the integration of multiple remote sensing data types (e.g., synthetic aperture radar (SAR), optical imagery, digital elevation models).

Main Results

[Specific results are not provided in the given text, as it only states the paper is the "Outcome" of the contest.]

Contributions

• Documents the state-of-the-art and emerging techniques in rapid flood mapping as showcased by participants in the 2024 IEEE GRSS Data Fusion Contest.
• Provides a benchmark for current capabilities and highlights challenges in data fusion for flood detection.

Funding

[Not specified in the provided text.]

Citation

@article{Li2026Rapid,
  author = {Li, Jiepan and Huang, He and He, Wei and Zhang, Hongyan and Zhang, Liangpei and Liu, Ting and Yuan, Mengke and Lu, Chaoran and Lu, Kaixuan and Peng, Baochai and Duan, Heyang and Li, Mengya and Zhang, Pan and Wang, Tao and Liao, Tongkui and Li, Yansheng and Dang, Bo and Wei, Fanyi and Tan, Jieyi and Lin, Yangjie and Persello, Claudio and Prasad, Saurabh and Vivone, Gemine and Lonjou, V. and Bretar, Frédéric and Rodriguez-Suquet, Raquel and Guntzburger, Pauline and Poulain, Vincent and Moigne, Jacqueline Le and Smith, Benjamin and Kumar, Sujay V. and Huang, Thomas and Ricci, Sophie and Nguyen, Thanh Huy and PIACENTINI, ANDREA},
  title = {Rapid Flood Mapping: Outcome of the 2024 IEEE GRSS Data Fusion Contest},
  journal = {IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing},
  year = {2026},
  doi = {10.1109/jstars.2026.3652462},
  url = {https://doi.org/10.1109/jstars.2026.3652462}
}