Source of most progress in artificial intelligence (AI) and machine learning (ML) can be traced back to data. Data, specifically, large-scale and openly-accessible training data are critical in adoption and acceleration of ML. While there are successful applications of ML in Earth science, the wider adoption of ML has been limited. Access to high-quality labeled training data is required to entice ML practitioners to tackle supervised learning problems in Earth science. However, creating labeled data that scales to support ML models is still a bottleneck and new strategies to increase the size and diversity of training datasets need to be explored. Additionally, enabling discovery and open sharing of existing training data and corresponding models to enable reproducibility of research and minimize duplication is a challenge.
This session seeks submissions from ML practitioners and data curators using different approaches to create labeled training data, catalog training data and models, and provide search, discovery and distribution of training data and models.
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