The new year kicks off with the American Meteorological Society’s annual meeting, and IMPACT will be represented. Below are previews of the must-see presentations IMPACT team members will be delivering on commercial satellite data, improved data resolution, and improving data discoverability and access.

Commercial Small Satellite Data Discovery and Access to Support Scientific Research
Wednesday, January 13, 2021. 12:50 PM — 12:55 PM

The NASA Commercial Smallsat Data Acquisition (CSDA) program identifies, evaluates, and acquires data from commercial satellite companies. This data complements NASA’s Earth science missions and research goals. Data acquired as part of this program includes satellite imagery as well as Global Navigation Satellite System radio occultation and ionosphere monitoring products. …

NASA’s Earth Science Data Systems (ESDS) program that manages all of NASA’s Earth science data is facing new sets of challenges in data management, processing, and analysis due to the anticipated growth of Earth science data. To address these unprecedented challenges, ESDS is starting to leverage advanced data-driven technologies such as artificial intelligence and machine learning (AI/ML) in all facets of the science data lifecycle management.

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Dr. Manil Maskey, a research scientist with NASA’s Marshall Space Flight Center, Huntsville and lead of IMPACT’s Advanced Concepts team, will be presenting a webinar for the IEEE Geoscience and Remote Sensing Society titled Earth Science Informatics: Artificial Intelligence and Machine Learning for Data Systems which details the ways ESDS is using these technologies to extract knowledge, improve operations, enhance data discovery, and support applications. …

The EGU General Assembly 2021 is fast approaching, and IMPACT is convening the session Addressing Training Data Challenges to Accelerate Earth Science Machine Learning. Progress in artificial intelligence (AI) and machine learning (ML) is driven by data. Data, specifically, large-scale and openly-accessible training data are critical to the adoption and acceleration of ML.

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Access to high-quality labeled training data is required to enable ML practitioners to tackle supervised learning problems in Earth science. However, creating labeled data that scales is still a bottleneck, and new strategies to increase the size and diversity of training datasets need to be explored. …

This year’s AGU Fall meeting has ended. Earlier we posted about a group of high school students who presented their research at AGU in partnership with an IMPACT team member. This is not the only way in which IMPACT supports student scientists at AGU. Four IMPACT team members who are graduate research assistants or undergraduate interns at the University of Alabama in Huntsville also had the opportunity to present research at AGU.

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Deep Learning PM2.5 Estimation

Graduate research assistant Manisha Khatri presented a deep learning approach to estimating surface PM2.5 levels. This research uses satellite and meteorological data and applies deep learning methods to predict levels of the aerosol pollutant PM2.5. Ms. Khatri believes this approach will help surpass spatial and temporal limitations of ground-based measurement techniques. Working with the IMPACT machine learning team on this project is the first intensive research work with which Ms. Khatri has been involved. …

AGU is almost here. Below are previews of three presentations you will not want to miss.

Phenomena Portal: Large-Scale Visual Exploration of Atmospheric Phenomena
IN028 — Applying Artificial Intelligence Tools and Services on Earth System Science Data II

The Earth science community is experiencing a high influx of remote sensing data due to recent advancements in sensor technology. This enables the community to extend their research on a larger scale than ever before. Unfortunately, traditional data processing techniques do not scale well to these new, high volume data sources. State-of-the-art machine learning (ML) pipelines have been proven to overcome these burdens in various other fields but are underexploited within the physical sciences community. Moreover, ML is reliant on labeled data, which is currently sparsely available in the Earth science domain. …

At IMPACT we’re all about science: Earth science, computer science, data science. We also make sure to nurture the next generation of scientists. Day-to-day that typically involves incorporating graduate research assistants and undergraduate interns into our research and development processes. But we also have team members who work with emerging scientists at the high school level. One example is IMPACT team member Dr. Chelle Gentemann, and her colleague Dr. García Reyes, who work with the Careers in Science (CiS) intern program which is part of the California Academy of Sciences in San Francisco, CA. …

We are now not all that far from AGU Fall 2020, and our team is hard at work preparing a number of presentations. We have been posting a series of presentation previews (the first two are here and here). Below are three more presentations we will be sharing with you during the conference.

Improved Data Communication, Understanding and Discovery Using Algorithm Theoretical Basis Documents
Session IN047 — Recent Advancements in Earth Science Data Discovery and Metadata Stewardship Practices

Scientists and data repositories depend on the effective communication of the scientific and physical theories used to derive Earth observation datasets from raw instrument data. An understanding of these theories is important to understanding and properly using the data. The NASA Earth science data community communicates this information through Algorithm Theoretical Basis Documents (ATBDs). However, ATBDs lack a formal, standardized structure, which often results in ATBDs containing inadequate information to understand the algorithm. The non-standard structures also impede the ability to efficiently parse the document’s content for the desired information. Additionally, science teams typically provide ATBDs in human, but not machine readable formats, which makes it difficult for modern information processing technologies to process the data. …

The American Geophysical Union’s #AGU20 Fall Meeting is just around the corner. We have another set of previews of the presentations that IMPACT team members will be delivering at #AGU20.

Pipeline for Applications-Based Data Discovery
IN047 — Recent Advancements in Earth Science Data Discovery and Metadata Stewardship Practices

From disaster response and mitigation to monitoring water quality or protecting wildlife habitat, satellite Earth observation data can be applied in countless ways to meet pressing needs and benefit society. The crucial first step toward successful data application is data discovery. …

We are into October, and the American Geophysical Union’s #AGU20 Fall Meeting is fast approaching. Even though it will be a virtual conference, we are looking forward to interacting with all of you. Over the next several weeks we will be previewing a number of the presentations that IMPACT team members will be delivering at #AGU20.

A Deep Learning Approach for Surface PM2.5 Estimations from Geostationary Satellite and Numerical Model Data
Session A008 — Earth Observations from Geostationary Satellites: Applied Research and Applications III Posters

Fine particles released by activities such as vehicle exhaust, the burning of fuels, and forest fires are known to cause severe impacts on public health. Particulate matter (PM) with a diameter less than or equal to 2.5 μm, referred to as PM2.5, is known to cause or exacerbate cardiovascular and respiratory illnesses. The analysis of data and the derivation of the relationships among them to estimate surface PM2.5 can be a tedious task and can require significant computation and processing. A deep learning approach is appropriate for such complex estimation problems as it defines relations among multiple non-linear parameters. …

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The Space Apps Challenge is all about maximizing the scientific return of Earth observation missions by improving the use of and access to Earth science data. The Space Apps Challenge is an annual hackathon hosted by NASA, CSA, CNES, JAXA, and ESA. For the October 2020 challenge, IMPACT has contributed a challenge that calls for the exploration of machine learning that is aimed at enhancing understanding of our planet.

More specifically, the challenge asks participants to contribute to automated phenomena detection in satellite imagery. Phenomenon detection is a difficult human skill to emulate. Natural phenomena, such as smoke and dust storms, have major impacts on ecosystems, economies, and human safety. …


IMPACT Unofficial

This is the unofficial blog of the Interagency Implementation and Advanced Concepts Team.

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