The 2019 ACCESS announcement specifically sought technology developments for one or a combination of the following areas:
- Machine Learning (ML) for Earth Science Data Systems (including new training datasets for ML),
- Enabling Science in the Cloud, and
- Improvement, Maintenance, and Support of High-value Open Source Earth Science Tools and Libraries.
Successful proposals clearly identified user communities that will benefit from the technology and demonstrated linkages to pressing Earth science data management problems or the need for open source library and tool maintenance.
The Earth Science Division of NASA's Science Mission Directorate selected 11 proposals for three-year awards pending satisfactory budget and work plan negotiations. The selected investigations are listed below, including principal investigator and institution.
- Advancing an Open-Access Repository for Earth Observation Training Data and Machine Learning Models, Seyed Hamed Alemohammad, Open Imagery Network, Inc.
- Developing Passive Satellite Cloud Remote Sensing Algorithms Using Collocated Observations, Numerical Simulation and Deep Learning, Jianwu Wang, University of Maryland, Baltimore County
- Enabling Cloud-Based InSAR Science for an Exploding NASA InSAR Data Archive, David Bekaert, NASA's Jet Propulsion Laboratory
- GeoWeaver: Building An Open-Source Platform for Enabling Ad Hoc Management, Open Sharing, and Robust Reuse of NASA Earth Data-Driven Hybrid AI Workflows, Ziheng Sun, George Mason University
- GNSS Radio Occultation Data in the AWS Cloud, Stephen Leroy, Atmospheric & Environmental Research, Inc.
- Large-Scale Operational Data Matchup Service for Multiple Platform Types, Nga Chung, NASA's Jet Propulsion Laboratory
- Machine Learning Datasets for the Earth's Natural Microwave Emission, Carl Mears, Remote Sensing Systems of California Corporation
- Machine Learning Planet High Resolution Training Data for Medium Resolution Land Cover and Disturbance Mapping, David Roy, Michigan State University
- Pangeo ML: Open Source Tools and Pipelines for Scalable Machine Learning Using NASA Earth Observation Data, Joseph Hamman, University Corporation for Atmospheric Research
- Spatio-Temporal Machine Learning and Cloud Computing for Predicting Dynamics of Global Vegetation Structure from Active Satellite Sensors, Sassan Saatchi, NASA Jet Propulsion Laboratory
- Training Data for Streamflow Estimation, Fritz Policelli, NASA's Goddard Space Flight Center
ACCESS 2017 teams were required to include both information technology and Earth science expertise, and projects were required to be tied directly to specific issues facing Earth science and applied science users interacting with NASA's Earth Observing System Data and Information System (EOSDIS). Technology focus areas include machine learning, advanced search capabilities, and cloud-optimized preprocessing and data transmission.
- Community Tools for Analysis of NASA Earth Observation System Data in the Cloud, Anthony Arendt, University of Washington, Seattle
- Multi-Temporal Anomaly Detection for SAR Earth Observations, Hook Hua, NASA's Jet Propulsion Laboratory
- Systematic Data Transformation to Enable Web Coverage Services (WCS) and ArcGIS Image Services within ESDIS Cumulus Cloud, Jeff Walter, NASA's Langley Research Center
- STARE: SpatioTemporal Adaptive-Resolution Encoding to Unify Diverse Earth Science Data for Integrative Analysis, Michael Rilee, Rilee Systems Technologies, LLC
- Data Access and the ECCO Ocean and Ice State Estimate, Patrick Heimbach, University of Texas, Austin
Browse the complete list of all previous ACCESS projects.