The observation of the advancing and retreating pattern of polar sea ice cover stands as a vital indicator of global warming. This research aims to develop a robust, effective, and scalable system for classifying polar sea ice as thick/snow-covered, young/thin, or open water using Sentinel-2 (S2) images. Since the S2 satellite is actively capturing high-resolution imagery over the earth's surface, there are lots of images that need to be classified. One major obstacle is the absence of labeled S2 training data (images) to act as the ground truth. We demonstrate a scalable and accurate method for segmenting and automatically labeling S2 images using carefully determined color thresholds. We employ a parallel workflow using PySpark to scale and achieve 9-fold data loading and 16-fold map-reduce speedup on auto-labeling S2 images based on thin cloud and shadow-filtered color-based segmentation to generate label data. The auto-labeled data generated from this process are then employed to train a U-Net machine learning model, resulting in good classification accuracy. As training the U-Net classification model is computationally heavy and time-consuming, we distribute the U-Net model training to scale it over 8 GPUs using the Horovod framework over a DGX cluster with a 7.21x speedup without affecting the accuracy of the model. Using the Antarctic's Ross Sea region as an example, the U-Net model trained on auto-labeled data achieves a classification accuracy of 98.97% for auto-labeled training datasets when the thin clouds and shadows from the S2 images are filtered out.

翻译：极地海冰覆盖的前进和消退模式观测是全球变暖的重要指标。本研究旨在开发一种稳健、高效且可扩展的系统，利用哨兵2号影像将极地海冰分类为厚/覆雪冰层、幼薄冰层或开阔水域。由于哨兵2号卫星持续获取地球表面高分辨率影像，大量待分类图像亟需处理。主要障碍在于缺乏标注后的哨兵2号训练数据作为基准真值。我们提出了一种可扩展且准确的方法，通过精心确定的颜色阈值对哨兵2号影像进行分割与自动标注。采用基于PySpark的并行工作流实现扩展：基于薄云与阴影滤除的颜色分割技术，自动标注哨兵2号影像时达到9倍数据加载加速比和16倍映射-规约加速比。由此生成的自动标注数据被用于训练U-Net机器学习模型，获得良好的分类精度。考虑到U-Net分类模型训练计算密集且耗时，我们利用DGX集群上的Horovod框架将U-Net模型训练分布至8个GPU，在保持模型精度的前提下实现7.21倍加速。以南极罗斯海区域为例，当滤除哨兵2号影像中的薄云和阴影后，基于自动标注数据训练的U-Net模型在自动标注训练数据集上的分类精度达98.97%。