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.
Abstract (translated)
冰川湖突发洪水（GLOFs）是气候变化引发的最具破坏性的灾害之一。现有的远程监测方法往往优先考虑最大化空间覆盖范围以训练通用模型，或者依赖于受持续云层遮挡限制的光学图像。本文介绍了一种端到端、自动化的深度学习管道，用于利用时间序列Sentinel-1 SAR对喜马拉雅高危冰川湖进行有针对性的监控。我们引入了“时间优先”的训练策略，使用EfficientNet-B3作为骨干网络的U-Net，在一组4个湖泊（Tsho Rolpa、Chamlang Tsho、Tilicho和Gokyo Lake）的精心策划的数据集上进行了训练。该模型实现了0.9130的IoU值，验证了“时间优先”策略的有效性，这对于向早期预警系统过渡是必要的。 除了模型之外，我们还提出了一种操作工程架构：一个Docker化的管道，通过ASF Search API自动摄取数据，并通过RESTful端点公开推断结果。该系统将范式从静态制图转向动态和自动化早期预警，为未来在早期预警系统的开发中提供可扩展的架构基础。
URL
https://arxiv.org/abs/2512.24117
