Manil Maskey


Manil Maskey is a Senior Research Scientist with the National Aeronautics and Space Administration (NASA). He also leads the Advanced Concepts team, within the Inter Agency Implementation and Advanced Concepts at the Marshall Space Flight Center and Science Mission Directorate’s Artificial Intelligence initiative at NASA HQ.  His research interests include computer vision, visualization, knowledge discovery, cloud computing, and data analytics. Dr. Maskey's career spans over 21 years in academia, industry, and government. Dr. Maskey is an adjunct faculty at the UAH Atmospheric Science department, a senior member of Institute of Electrical and Electronics Engineers (IEEE), chair of the IEEE Geoscience and Remote Sensing Society (GRSS) Earth Science Informatics Technical Committee, member of American Geophysical Union (AGU) and AGU Fall Meeting Planning Committee, member of European Geosciences Union (EGU), and member of Association for Advancement of Artificial Intelligence (AAAI).

Brian Freitag


Dr. Brian Freitag is a Research Scientist with the Interagency Implementation and Advanced Concepts (IMPACT) Team, where he leads the Harmonized Landsat/Sentinel-2 (HLS) production project.  His research focused on the impacts of urbanization in regions of complex terrain using a combination of mesoscale numerical weather prediction and ground-based and satellite-based observations.  During his time with the IMPACT project, Brian has supported multiple efforts including the Analysis and Review of the Common Metadata Repository (ARC), machine learning for Earth science, the Satellite Needs Working Group (SNWG) biennial assessment, and the Commercial Smallsat Data Acquisition Program.

Sean Harkins


Sean is an engineer at Development Seed. He builds infrastructure, applications, and pipelines to make massive geospatial datasets more accessible for decision-makers. Sean is passionate about open source solutions and helping to produce an ecosystem of tools that are available to everyone. He is a strong believer in the power of data and visualizations to help educate people about issues in the larger world.  Sean is the technical lead for the NASA Harmonized Landsat/Sentinel-2 data production.

Muthukumaran Ramasubramanian


Muthukumaran Ramasubramanian received the M.S. degree incomputer science from the University of Alabama in Huntsville (UAH), where heis currently pursuing the Doctorate degree in computer science. He is also aComputer Science Researcher and leads the Machine Learning Team forNASA–Interagency Implementation and Advanced Concepts Team, UAH. His workfocuses on using deep-NLP techniques to surface novel relationships from largecorpora of text and to deploy deep learning solutions to detecting earthscience phenomena on a global scale. His research interests include machinelearning, big data, computer vision, and scalable cloud services.

Iksha Gurung


Iksha Gurung is a Computer Scientist working with University of Alabama in Huntsville, supporting National Aeronautics and Space Administration Inter-Agency Implementation of Advanced Concepts Team (NASA-IMPACT). He leads the development and machine learning team in NASA-IMPACT.  His projects include applying machine learning to Earth science phenomena studies and scaling the solutions to production.

Lecture content

Data Science at Scale: Harmonized Landsat Sentinel (HLS) Case Study

Most people associate data science as the process of extracting knowledge and insights from data. While that is partly true, data science is a broader concept involving data collection, storage, integration, analysis, inference, communication, and ethics. Gaining a good grasp of these concepts is essential for anyone working in a data-rich field such as Earth Science and Remote Sensing.

This summer school session will explain the complexity of the data life cycle, the supporting data and analytical systems, and the research life cycle. Workshop participants will get a "behind the curtain" view of science data production at scale using the Harmonized Landsat Sentinel (HLS) data as a case study. The workshop will explain the challenges of designing and implementing large-scale processing pipelines on the cloud. A supporting cloud-native analytical platform to enable interactive analysis and visualization will be covered. The participants will perform hands-on scientific research using the cloud-based data and analytic framework.

Learning outcomes

  • Thorough understanding of the data science process
  • Understand the basics of data management and governance
  • Hands-on experience in developing cloud-based data processing pipelines as well as analytical scripts

Participant Requirements

  • Basic understanding of cloud computing and python programming language
  • Interest in data science, managing and analyzing Earth science data at scale.


  • Data Science Overview  - Lecture
  • Data System

                 - Management and Governance - Lecture

                 - Processing - Lecture/Demo

                 - Hands-on exercise: processing script - Practical

  • Analytical System

                   - Architecture overview - Lecture

                   - Hands-on exercise: science analysis script - Practical

  • Machine Learning Application with HLS  - Practical