A Parallel Workflow for Polar Sea-Ice Classification using Auto-labeling of Sentinel-2 Imagery ...

• Main Authors: Iqrah, Jurdana Masuma, Wang, Wei, Xie, Hongjie, Prasad, Sushil
• Format: Article in Journal/Newspaper
• Language: unknown
• Published: arXiv 2024
• Subjects: Computer Vision and Pattern Recognition cs.CV; Distributed, Parallel, and Cluster Computing cs.DC; Machine Learning cs.LG; FOS Computer and information sciences; Sea ice
• Online Access: https://dx.doi.org/10.48550/arxiv.2403.13135; https://arxiv.org/abs/2403.13135

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 ... : Accepted in the 25th IEEE International Workshop on Parallel and Distributed Scientific and Engineering Computing (PDSEC 2024), May 2024. arXiv admin note: substantial text overlap with arXiv:2303.12719 ...