  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.
