Targeted Semantic Segmentation of Himalayan Glacial Lakes Using Time-Series SAR: Towards Automated GLOF Early Warning

TL;DR

The paper tackles the challenge of monitoring Himalayan glacial lakes for GLOF risk under cloud-prone conditions by adopting a temporal-first, time-series SAR approach using Sentinel-1 data. It trains a U-Net with an EfficientNet-B3 encoder on a cohort of four lakes and demonstrates strong segmentation performance (IoU $=0.9130$, F1 $=0.9538$) while enabling decadal climate analysis from 2014–2025. A fully automated, Dockerised pipeline from data ingestion to inference and dissemination is proposed to support near real-time GLOF Early Warning Systems, validating the approach on high-risk lake dynamics and seasonal fluctuations. The work discusses operational deployment, limitations (topographic distortions, sub-pixel errors, winter ambiguities), and future enhancements including higher-resolution and multisensor data to strengthen timely, actionable monitoring.

Abstract

Glacial Lake Outburst Floods (GLOFs) are one of the most devastating climate change induced hazards. Existing remote monitoring approaches often prioritise maximising spatial coverage to train generalistic models or rely on optical imagery hampered by persistent cloud coverage. This paper presents an end-to-end, automated deep learning pipeline for the targeted monitoring of high-risk Himalayan glacial lakes using time-series Sentinel-1 SAR. We introduce a "temporal-first" training strategy, utilising a U-Net with an EfficientNet-B3 backbone trained on a curated dataset of a cohort of 4 lakes (Tsho Rolpa, Chamlang Tsho, Tilicho and Gokyo Lake). The model achieves an IoU of 0.9130 validating the success and efficacy of the "temporal-first" strategy required for transitioning to Early Warning Systems. Beyond the model, we propose an operational engineering architecture: a Dockerised pipeline that automates data ingestion via the ASF Search API and exposes inference results via a RESTful endpoint. This system shifts the paradigm from static mapping to dynamic and automated early warning, providing a scalable architectural foundation for future development in Early Warning Systems.

Figures (6)

• Figure 1: Schematic representation of the automated Sentinel-1 GRD preprocessing pipeline. The workflow transitions from raw data acquisition via ASF API to the generation of standardized, 8-bit GeoTIFFs.
• Figure 2: Alternative and General Pre-processing Pipeline
• Figure 3: The proposed end-to-end segmentation workflow.
• Figure 4: Training dynamics showing rapid convergence and stability $>$0.90 IoU.
• Figure 5: Inference results highlighting accurate segmentation (Pink) versus minor boundary errors (Blue/Red).