GIBS Machine Learning for Data Validation and Natural Event Detection(NPO-51089-1)

NASA Global Imagery Browse Services (GIBS) provides a full-resolution image archive of over 70 million images and access to services for over 700 NASA Earth science data products covering every part of the world. Most imagery is available within 3-5 hours after satellite overpass and some products span almost 30 years. While the GIBS satellite data remains highly accessible, it remains largely underexploited and analyzed due to its scale. The development of algorithms to ensure the consistency of data is critical for near-real time (NRT) applications. We identify two machine learning tasks: (1) data validation and (2) natural event detection. For data validation we will apply machine learning techniques to validate images for missing data patterns and miscoloration. For natural event detection we illustrate the promise of deep learning to leverage the scale of GIBS imagery to learn state-of-the-art algorithms. Lastly, we implement an operational machine learning system to process new images and notify GIBS operators.