AI for Space and Aerospace
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Artificial Intelligence, Machine Learning, Deep Le↗
Engineering topics
Nargess Memarsadeghi
Publications and source records attributed to Nargess Memarsadeghi.
Report on Workshop on Artificial Intelligence in Strategic Planning and Science Prioritization
This report details the observations from a two-day virtual workshop, held May 12-13, 2020, focused on whether, and how, artificial intelligence (AI) could assist humans in strategic planning, specifically in science and technology prioritization. The participants identified several “key challenges” that AI might tackle in this area. To further understand the value of these key challenges the workshop then developed related test cases that would demonstrate specifically how AI/machine learning (ML) could provide assistance to humans. Approximately 40 subject matter experts (SMEs), with backgrounds in AI, strategic planning for science, and scientific data, were gathered for the conference. This report collates the details of the output of the workshop. The “best” test cases include (in no particular order):Use of AI to assist in selecting Decadal Survey priorities. * Use of AI to identify new, or previously unidentified, science topics for prioritization. * Using AI to better label and increase discoverability of scientific literature and proposals. * Use of AI to enhance current observation capabilities for scientific missions. * Using AI to mitigate biases in selection of proposal reviewers and membership of advisory committees. Examination of these test cases indicates that Natural Language Processing (NLP) is a common capability found in most of the ”best” (top-rated) test cases and is a valuable, multi-purpose tool which enables ML in this area.
strategic planning↗
Virtual and Augmented Reality Applications in Science and Engineering
The articles in this special section focus on virtual and augmented reality applications in science and engineering. There has been an explosive growth in visually augmenting the spaces around us by creating environments where visual, aural, and kinesthetic immersive experiences afforded by virtual and augmented reality (AR) powerfully engage us in a way no other medium can. Virtual reality (VR) recreates the sensory world around us entirely through computer-generated signals of sight, sound, touch (and in some cases smell and taste). AR overlays the computer-generated sensory signals on the real world allowing the user to experience a rich juxtaposition of the virtual and the real worlds simultaneously. Together, these technologies are transforming the way people from all walks of life—scientists, engineers, educators, industrial workers, health care professionals, artists, and everyday people— see and use the information that matters most to them, in an intuitive embodied way. Just as mobile technology has revolutionized how we communicate with each other and with our digital worlds, ubiquitous VR and AR will fundamentally alter how our society creates, inspires, engages, and learns from the information-rich and enriched cyberspaces around us. While affordable consumer- quality VR and AR hardware is becoming available, significant work is needed to adopt VR and AR for important and difficult scientific and societal applications.
Virtual Reality, Augmented Reality↗
Supporting Aquaculture in the Chesapeake Bay Using Artificial Intelligence to Detect Poor Water Quality with Remote Sensing
Reliable information on water quality is not currently available at the space and time scales that are required for aquaculture and other resource management needs. For example, shellfish growing areas maybe impacted by harmful algal blooms or runoff from land that increases turbidity, lowers salinity, or introduces contaminants. Shellfish resource managers in the Chesapeake Bay are especially concerned with sources of bacteria from land such as failing onsite waste systems, failing wastewater infrastructure, and concentrated animal feeding operations. There is an urgent need for remote sensing of water quality indicators beyond chlorophyll-a and suspended sediments to augment field sampling programs. Artificial Intelligence trained with simultaneous in situ and satellite observations is explored in preparation for future hyperspectral satellite missions, which offer potential to detect additional water quality indicators not previously possible. This first step identifies and develops a method to harmonize disparate, unlinked aquatic datasets to derive information about where water quality is likely degraded.
Stephanie Schollaert Uz↗
It's Not Rocket Science, It's Data Science! Women Doing Data Science at NASA
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Community outreach↗
GSFC Activities in AI and Data Science
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Community Outreach↗
In Situ Water Quality Data for the Chesapeake Bay
This paper examines in situ water quality datameasured during2020-2021in the Chesapeake Bay for comparison with optical satellite data. Thiscollection was performed as part of a NASA project aiming to develop new methods for water quality monitoring from satellite remote sensingusing artificial intelligence. Our objective is to use insitu data as ground-truth to provide water quality classifications, or labels,to their overlapping (in time and location)satellite imagery. Having such labeled data, can help us achieve our project’s longer-termgoal:to train artificial intelligencemodelsto recognize features in spectral informationfor monitoringwater qualityfrom satellites. Because routine monitoring by state agencies is conducted at discrete locations, we obtained a flow-through system operated from small boats to measure waterquality parameters along transects for comparison with two-dimensional maps collected from space, with an initial focus on low oxygenevents, due to their large spatial extent and regular occurrence each summer.We also evaluated similar in situ data collected during 1984-2021by the Chesapeake Program.
Nargess Memarsadeghi↗
Anomaly Detection for the Roman Space Telescope Wide Field Instrument’s Science Data Processing Pipeline
The Roman Space Telescope (RST) Wide Field Instrument (WFI) will be utilizing a preliminary Science Data Processing (SDP) pipeline during its Integration and Test, and to some extent during Operations, to track basic statistics and identify known features such as cosmic rays, snowballs as well as possible anomalies in raw detector data. In our detectors, these anomalies appear as jumps in the ramp of a readout and are classified as cosmic rays if they appear as a streak or snowballs if they’re more circular. The WFI employs an array of 18 H4RG-10 detectors that collect image samples. Each set of raw frames within a non-destructive exposure is packaged by the SDP pipeline into image cubes for each detector. Each cube is a time series of 4096 × 4096 accumulating pixel frames. The preliminary analysis pipeline is used to locate anomalies in these time-series accumulation frames and identify the type of anomaly, either natural phenomena or detector characteristic. To compare different methods, we’ve implemented both heuristic-based and data-driven methods to identify anomalies. For the heuristic-based approach, we identify snowballs and cosmic rays by the size and shape of outlier pixel clusters between consecutive frames. For data driven methods, we evaluated a Convolutional Neural Network (CNN) model, and more traditional methods like Principal Component Analysis (PCA). CNN is a supervised learning/classification method. Thus, we used a labeled dataset of anomalies to perform segmentation of the image and identify anomalies. We used previously identified cosmic rays and snowballs to measure the accuracy and efficiency of the mentioned approaches. In evaluating these methods, we aim to pick the best fit for the SDP pipeline’s anomaly detection in terms of both performance and runtime.
Paul Horton↗