An Overview of MLCommons Cloud Mask Benchmark: Related Research and Data
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Abstract (arXiv)
Cloud masking is a crucial task that is well-motivated for meteorology and its applications in environmental and atmospheric sciences. Its goal is, given satellite images, to accurately generate cloud masks that identify each pixel in image to contain either cloud or clear sky. In this paper, we summarize some of the ongoing research activities in cloud masking, with a focus on the research and benchmark currently conducted in MLCommons Science Working Group. This overview is produced with the hope that others will have an easier time getting started and collaborate on the activities related to MLCommons Cloud Mask Benchmark.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 3 of its 20 references that are in the map. First posted on arXiv 8 Dec 2023.
Builds on
3 of 20 references.
References · 3
- 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
- 2018Deep learning based cloud detection for medium and high resolution remote sensing images of different sensorscited by 20
- 2021MLPerf HPC: A Holistic Benchmark Suite for Scientific Machine Learning on HPC Systemscited by 15
Built on by
Citing papers per year: 2023: 1.
1 paper.
- 2023MLCommons Cloud Masking Benchmark with Early Stoppingcited by 0
- 2023MLCommons Cloud Masking Benchmark with Early Stopping11 Dec 2023