Automated Glacial Lake Monitoring for Early Warning
Published:Dec 30, 2025 09:53
•1 min read
•ArXiv
Analysis
This paper addresses a critical climate change hazard (GLOFs) by proposing an automated deep learning pipeline for monitoring Himalayan glacial lakes using time-series SAR data. The use of SAR overcomes the limitations of optical imagery due to cloud cover. The 'temporal-first' training strategy and the high IoU achieved demonstrate the effectiveness of the approach. The proposed operational architecture, including a Dockerized pipeline and RESTful endpoint, is a significant step towards a scalable and automated early warning system.
Key Takeaways
- •Proposes an automated deep learning pipeline for monitoring Himalayan glacial lakes using time-series SAR data.
- •Employs a 'temporal-first' training strategy with a U-Net and EfficientNet-B3 backbone.
- •Achieves a high IoU (0.9130) demonstrating the effectiveness of the approach.
- •Introduces a Dockerized pipeline and RESTful endpoint for automated data ingestion and inference, enabling a scalable early warning system.
Reference
“The model achieves an IoU of 0.9130 validating the success and efficacy of the "temporal-first" strategy.”