Beyond boundaries: Synergizing SAR, UAV, and federated learning for flood mapping in Australia
Author Identifier (ORCID)
Abstract
Flooding has become increasingly severe in recent years, causing major displacement and infrastructure damage. While synthetic aperture radar (SAR) satellites and UAVs aid flood monitoring, SAR is limited by resolution and dynamic flood boundaries, and UAVs struggle with large-area coverage. To address this, we propose a novel framework that fuses multiscale SAR and unmanned aerial vehicles (UAVs) data, enhanced by a federated learning (FL) scheme and feedback loop to build a vertical space-air sensing structure. FL effectively handles the data heterogeneity and decentralized nature of real-world sensing platforms, avoiding the challenges of central aggregation. Tested in flood-prone Moama, NSW, Australia, the approach significantly improves flood assessment. Compared to centralized training, the FL framework boosts average kappa and F1 scores by 1.7%–4%, with the best vector (Vector 4) improving from 82.30% to 85.21% in kappa and from 83.01% to 85.87% in F1, demonstrating strong scalability, accuracy, and robustness.
Document Type
Journal Article
Date of Publication
1-1-2026
Volume
23
Publication Title
IEEE Geoscience and Remote Sensing Letters
Publisher
IEEE
School
School of Engineering
RAS ID
91295
Funders
Cooperative Research Centre for Smart Satellite Technologies and Analytics through Australian Government’s CRC Program
Copyright
subscription content
Comments
Sheng, Z., Li, C., Qi, X., Wu, K., Ni, W., Liu, R. P., & Ge, L. (2025). Beyond Boundaries: Synergizing SAR, UAV, and federated learning for flood mapping in Australia. IEEE Geoscience and Remote Sensing Letters, 23. https://doi.org/10.1109/LGRS.2025.3624200