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Machine Learning Project

Building tools and pipelines that apply machine learning algorithms to NASA Earth science datasets to improve data discovery

The increase in Earth science observation instruments and platforms throughout the world has resulted in an exponential rate of data growth. This presents the Earth science community with a rich and ever expanding archive of useful data. 

Machine learning (ML) algorithms offer the potential for innovative new data analysis and the generation of valuable insights from this massive archive of Earth observation data. However, despite the popularity of applying ML to problems in other fields, there is only limited adoption of ML in the Earth science community. This lack of ML utilization in the Earth sciences has largely been credited to a lack of labeled training data.

The Machine Learning Project was developed by NASA's Interagency Implementation and Advanced Concepts Team (IMPACT) to address these issues and increase the use of ML among Earth scientists.

Building Tools, Pipelines, and Labeled Datasets

The application of ML algorithms to Earth science phenomena entails the development of ML tools and pipelines. These tools also require an archive of labeled data for scientists to leverage in building ML applications. IMPACT’s ML team develops needed resources and promotes an open approach to machine learning in the Earth sciences.

Adoption of Machine Learning Algorithms Using Earth Science Data

The ML team works to encourage the use of applied artificial intelligence and ML to answer questions in Earth science. Members of the team advertise and support the usability of ML in NASA’s Earth Science division. To accomplish this goal, the team incorporates machine learning into different stages of the data lifecycle to improve functionality and operations.

Explore Avenues to Support Machine Learning Efforts in ESDS

The ML team tracks new developments in the areas of artificial intelligence and ML to contribute to advancing NASA’s Earth Science Data Systems (ESDS). As part of this focus, the team applies ML perspectives to analytics architecture designs. The team also strives to utilize the full capacity of machine learning advances in high-end computing and cloud computing in order to achieve NASA Science’s research goals. Along the way, the team collaborates with academia, industry and other government agencies.

The IMPACT Machine Learning Project team has shared their work at conferences around the world and in leading academic publications.

“Employing Deep Learning to Enable Visual Exploration of Earth Science Events” 
“ES2Vec: Earth Science Metadata Keyword Assignment Using Domain-Specific Word Embeddings”
“Machine Learning Lifecycle for Earth Science Application: A Practical Insight into Production Deployment” 
“Pixel Level Smoke Detection Model with Deep Neural Network” 
“Surveying the Machine Learning Landscape in Earth Sciences” 
“Image Labeler: A Web Interface to Catalog Earth Science Events”
“Pixel-Based Smoke Detection Using Machine Learning for the Next Generation Geostationary Satellite Imagery”
“Pixel Based Model for High Latitude Dust Detection”
“Deep Learning-based Tropical Cyclone Intensity Estimation Portal”
“End-to-end Machine Learning Applications Framework for Earth Science”
“ES2Vec: Earth Science Metadata Keyword Assignment Using Domain-Specific Word Embeddings”