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NASA NTRSDate not supplied

Expanding NeMO-Net Machine Learning Capabilities for Citizen Science

NASA NeMO-Net, the neural multi-modal observation and training network for global coral reef assessment, is an open-source deep convolutional neural network and interactive active learning training software aiming to accurately assess the present and past dynamics of coral reef ecosystems through determination of percent living cover and morphology as well as mapping of spatial distribution. We present an interactive citizen science video game, released this April, for desktop and iOS devices where users interactively label morphology classifications over mm-scale 3D coral reef imagery captured using diver photomosaic imagery, the UAV enabled NASA FluidCam instrument, and satellite datasets. To date, the application has had over 40,000 downloads and over 60,000 unique coral reef classifications, each filtered through a user-based rating and expert evaluation system. We also present results from NeMO-Net’s convolutional neural network (CNN) models used to semantically segment 2D satellite imagery as well as projections of 3D coral reconstructions using user input data as training datasets. Fusing datasets using machine learning from multiple remote sensing platforms presents novel methodologies for assessing the health of coral ecosystems, which are critically endangered by a changing climate. In partnering with Mission Blue, the National Oceanic and Atmospheric Administration (NOAA), and the Living Oceans Foundation (LOF), NeMO-Net leverages an international consortium of subject matter experts to provide both proper training for citizen scientists and the generation of a labeled datasets to ingest into machine learning algorithms for global coral reef identification.

NeMO-Net↗

NASA NTRSDate not supplied

Expanding NeMO-Net Machine Learning Capabilities for Citizen Science

NASA NeMO-Net, the neural multi-modal observation and training network for global coral reef assessment, is an open-source deep convolutional neural network and interactive active learning training software aiming to accurately assess the present and past dynamics of coral reef ecosystems through determination of percent living cover and morphology as well as mapping of spatial distribution. We present an interactive citizen science video game, released this April, for desktop and iOS devices where users interactively label morphology classifications over mm-scale 3D coral reef imagery captured using diver photomosaic imagery, the UAV enabled NASA FluidCam instrument, and satellite datasets. To date, the application has had over 40,000 downloads and over60,000 unique coral reef classifications, each filtered through a user-based rating and expert evaluation system. We also present results from NeMO-Net’s convolutional neural network (CNN) models used to semantically segment 2D satellite imagery as well as projections of 3D coral reconstructions using user input data as training datasets. Fusing datasets using machine learning from multiple remote sensing platforms presents novel methodologies for assessing the health of coral ecosystems, which are critically endangered by a changing climate. In partnering with Mission Blue, the National Oceanic and Atmospheric Administration (NOAA), and the Living Oceans Foundation (LOF), NeMO-Net leverages an international consortium of subject matter experts to provide both proper training for citizen scientists and the generation of a labeled datasets to ingest into machine learning algorithms for global coral reef identification.

NeMO-Net↗

NASA NTRS2016

Hybrid Reality Lab Capabilities - Video 2

Our Hybrid Reality and Advanced Operations Lab is developing incredibly realistic and immersive systems that could be used to provide training, support engineering analysis, and augment data collection for various human performance metrics at NASA. To get a better understanding of what Hybrid Reality is, let's go through the two most commonly known types of immersive realities: Virtual Reality, and Augmented Reality. Virtual Reality creates immersive scenes that are completely made up of digital information. This technology has been used to train astronauts at NASA, used during teleoperation of remote assets (arms, rovers, robots, etc.) and other activities. One challenge with Virtual Reality is that if you are using it for real time-applications (like landing an airplane) then the information used to create the virtual scenes can be old (i.e. visualized long after physical objects moved in the scene) and not accurate enough to land the airplane safely. This is where Augmented Reality comes in. Augmented Reality takes real-time environment information (from a camera, or see through window, and places digitally created information into the scene so that it matches with the video/glass information). Augmented Reality enhances real environment information collected with a live sensor or viewport (e.g. camera, window, etc.) with the information-rich visualization provided by Virtual Reality. Hybrid Reality takes Augmented Reality even further, by creating a higher level of immersion where interactivity can take place. Hybrid Reality takes Virtual Reality objects and a trackable, physical representation of those objects, places them in the same coordinate system, and allows people to interact with both objects' representations (virtual and physical) simultaneously. After a short period of adjustment, the individuals begin to interact with all the objects in the scene as if they were real-life objects. The ability to physically touch and interact with digitally created objects that have the same shape, size, location to their physical object counterpart in virtual reality environment can be a game changer when it comes to training, planning, engineering analysis, science, entertainment, etc. Our Project is developing such capabilities for various types of environments. The video outlined with this abstract is a representation of an ISS Hybrid Reality experience. In the video you can see various Hybrid Reality elements that provide immersion beyond just standard Virtual Reality or Augmented Reality.

Delgado, Francisco J.↗

NASA NTRS2004

Land Use and Change

The overall purpose of this training session is to familiarize Central American project cooperators with the remote sensing and image processing research that is being conducted by the NASA research team and to acquaint them with the data products being produced in the areas of Land Cover and Land Use Change and carbon modeling under the NASA SERVIR project. The training session, therefore, will be both informative and practical in nature. Specifically, the course will focus on the physics of remote sensing, various satellite and airborne sensors (Landsat, MODIS, IKONOS, Star-3i), processing techniques, and commercial off the shelf image processing software.

Irwin, Daniel E.↗

DOE OSTI2026

VoroClust: Scalable Clustering for Remote Sensing

Although supervised machine learning provides a powerful framework for image classification and segmentation, it requires comprehensive consistent datasets, which are not available for many remote-sensing applications. Remote-sensing datasets are expensive to collect, and each is acquired under different environmental conditions or with significant variations in system operating parameters. Unsupervised clustering algorithms analyze the structure of each dataset independently, rather than drawing on similarities with existing “training” examples, and are thus well suited for practical remote-sensing applications. We introduce VoroClust, a fast density-based unsupervised clustering algorithm applicable to high-resolution and high-dimensional data. VoroClust runs as fast as distance-based clustering methods, while capturing complex regional geometries at least as well as current-density-based methods. It uses a data-centered sphere cover to reduce computational demands, while still capturing data topology. It then propagates clusters outward from local peaks in density. We show that VoroClust provides fast state-of-the-art clustering for both high-resolution polarimetric synthetic aperture radar and high-dimensional hyperspectral imaging datasets.

42 ENGINEERING↗

DOE OSTI2021

White Box Access to Quantum Testbeds for Co-Design

At Lawrence Livermore National Laboratory (LLNL), we operate and maintain the Quantum Device and Integration Testbed (QuDIT) facility, a small quantum testbed that supports about 10 active research teams (including our own) and over 50 internal and external collaborators. This testbed is designed to give remote white box access to users for research, training, and outreach. A guiding principle behind the development of our testbed infrastructure, software and user interfaces is to empower users to perform experiments at the cutting edge of quantum information science at any level of abstraction, from materials studies, device physics and control and characterization techniques to algorithm development and quantum operating system design. Our testbed targets a multilevel quantum system (qudit) to expand the accessible Hilbert space of a simple-to-manufacture quantum device and focuses on quantum simulation, typically implemented through custom gates designed with quantum optimal control methods, rather than on a universal computing framework with a fixed gate set. We leverage the Lab’s high-performance computing (HPC) program and related expertise to simulate quantum systems, develop hybrid algorithms, and generate gates optimized for given simulations. Additionally, we have adopted a co-design philosophy from the HPC community in designing new hardware, so that the systems we develop are optimized for the specific physics simulations we plan to use them for.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

NASA NTRS1975

Information systems and services, user services

The following topics were discussed: (1) data availability and distribution, (2) complete processing systems, (3) subsystems, (4) applications, (5) research for future technology, and (6) education, training opportunities, and materials. Evidence was given that remote sensing technology is being increasingly utilized. Therefore, it was concluded that a second stage of remote sensing technology should be developed.

Landgrebe, D. A.↗

NASA NTRS1977

The transfer of remote sensing technology in the developing nations: An observation

The cooperation and assistance of industrialized nations and the United Nations and its agencies in promoting the transfer of remote sensing technology in developing nations was discussed. Training programs, workshops, and seminars as well as on-going globally scattered demonstration projects were evaluated and it was suggested that emphasis should shift from centralized training to scheduled regional training programs, resulting in larger local participation and on-the-spot application of the technology to solve local problems.

Abiodun, A. A.↗

NASA NTRS1982

LANDSAT technology transfer to the private and public sectors through community colleges and other locally available institutions, phase 2 program

A program established by NASA with the Environmental Research Institute of Michigan (ERIM) applies a network where the major participants are NASA, universities or research institutes, community colleges, and local private and public organizations. Local users are given an opportunity to obtain "hands on" training in LANDSAT data analysis and Geographic Information System (GIS) techniques using a desk top, interactive remote analysis station (RAS). The RAS communicates with a central computing facility via telephone line, and provides for generation of land use and land suitability maps and other data products via remote command. During the period from 22 September 1980 - 6 March 1982, 15 workshops and other training activities were successfully conducted throughout Michigan providing hands on training on the RAS terminals for 250 or more people and user awareness activities such as exhibits and demonstrations for 2,000 or more participants.

Rogers, R. H.↗

NASA NTRS1995

Accelerating Commercial Remote Sensing

Through the Visiting Investigator Program (VIP) at Stennis Space Center, Community Coffee was able to use satellites to forecast coffee crops in Guatemala. Using satellite imagery, the company can produce detailed maps that separate coffee cropland from wild vegetation and show information on the health of specific crops. The data can control coffee prices and eventually may be used to optimize application of fertilizers, pesticides and irrigation. This would result in maximal crop yields, minimal pollution and lower production costs. VIP is a mechanism involving NASA funding designed to accelerate the growth of commercial remote sensing by promoting general awareness and basic training in the technology.

Source record↗

NASA NTRS2005

Continued Astrometric Follow-up Of Near-Earth Objects

As the grant periods overlapped, some of this information below will also be present on the previous final report. During the period May 1 2004 to April 30 2005, approximately 100 NEOs fainter than V = 20 were observed on separate nights from the 1.2-m telescope at Mt. Hopkins. Additionally, a few comets were targeted, including astrometric support of the Deep Impact mission by observing comet P/Tempel 1. Kyle Smalley was again employed as an independent contractor, and he was trained in use of the telescope, performed several remote observing runs on his own, and has now begun critical software support of the observing program. Code to automatically operate the telescope, given a target list, is approximately 90% done. During the first observing run scheduled in late September or early October, this code will be tested at on the telescope. It is probable that the 1.2m telescope will be run automatically all night without any interruption from the observer for anything during this time. Additional work on selecting which NEO targets to observe is progressing, with a beta-release of a simple target selection web page. Additionally, two-night objects with the potential of being NEOs have been extracted on a routine basis during this last grant cycle. These will also be added to a web page to facilitate additional astrometric follow-up.

Spahr, Timothy↗

NASA NTRS2009

Cold Season QPF: Sensitivities to Snow Parameterizations and Comparisons to NASA CloudSat Observations

As operational numerical weather prediction is performed at increasingly finer spatial resolution, precipitation traditionally represented by sub-grid scale parameterization schemes is now being calculated explicitly through the use of single- or multi-moment, bulk water microphysics schemes. As computational resources grow, the real-time application of these schemes is becoming available to a broader audience, ranging from national meteorological centers to their component forecast offices. A need for improved quantitative precipitation forecasts has been highlighted by the United States Weather Research Program, which advised that gains in forecasting skill will draw upon improved simulations of clouds and cloud microphysical processes. Investments in space-borne remote sensing have produced the NASA A-Train of polar orbiting satellites, specially equipped to observe and catalog cloud properties. The NASA CloudSat instrument, a recent addition to the A-Train and the first 94 GHz radar system operated in space, provides a unique opportunity to compare observed cloud profiles to their modeled counterparts. Comparisons are available through the use of a radiative transfer model (QuickBeam), which simulates 94 GHz radar returns based on the microphysics of cloudy model profiles and the prescribed characteristics of their constituent hydrometeor classes. CloudSat observations of snowfall are presented for a case in the central United States, with comparisons made to precipitating clouds as simulated by the Weather Research and Forecasting Model and the Goddard single-moment microphysics scheme. An additional forecast cycle is performed with a temperature-based parameterization of the snow distribution slope parameter, with comparisons to CloudSat observations provided through the QuickBeam simulator.

Molthan, A. L.↗

NASA NTRS2013

NASA Technology Evaluation for Environmental Risk Mitigation Remediation Technology Collaboration Development

NASA is committed to finding solutions to agency cleanup problems that are better, cheaper, and more effective than the status quo. Unfortunately, some potential solutions involve innovative technologies for which NASA remediation managers may not have a high level of understanding or confidence. Since 2004, NASA's Stennis Space Center (SSC) in Mississippi has been pumping groundwater contaminated with trichloroethylene (TCE) and other halogenated volatile organic compounds (HVOC) from their cleanup location designated "Area G" through extraction wells to an aboveground treatment system. Over time, however, the effectiveness of this treatment strategy has diminished and an alternative approach is needed. In 2012, professionals from NASA's Principal Center for Technology Evaluation for Environmental Risk Mitigation (TEERM) introduced SSC managers to an innovative technology for enhancing the performance of SSC's existing pump and treat system. The technology, generally referred to as in situ chemical oxidation (ISCO), involves slowly and continuously injecting a strong but safe chemical oxidant into the groundwater. Treatment is enhanced by a "surfactant-type effect" which causes residual contamination from saturated soil to be released into the dissolved-phase where it can be readily oxidized. Any dissolved-phase contamination that was not oxidized can be collected by the extraction well network and treated aboveground. SSC was not familiar with the technology so to increase their confidence, TEERM identified a contractor who was willing to demonstrate their product and process at a significantly reduced price. An initial, small-scale demonstration of ISCO began at sse in March 2012 and completed in August 2012. This successful demonstration was followed by three larger-scale ISCO demonstrations between August and December 2012. The contractor's innovative Continuous Injection System (CIS) incorporated "green" and sustainable technologies and practices. A slow injection rate was maintained autonomously by the CIS, eliminating the need for multiple mobilizations of personnel and powered equipment. The CIS was calibrated to deliver only as much reagent as the formation would accept without "short circuiting", minimizing material waste. Public utility water pressure was used to mix, dilute, and inject the reagent. NASA personnel were trained to operate and maintain the system and remote monitoring and injection control capabilities were developed, further reducing the need for contractor mobilizations to the site. An integrated solar photovoltaic panel was used to power the control valves and web monitoring telemetry. SSC provided hydrogen peroxide for the process using their existing supplier for the aboveground pump and treat systems. The only consumable required from the contractor was their proprietary activator to catalyze SSC's peroxide.

Romeo, James↗

NASA NTRS1990

Telepresence and Space Station Freedom workstation operations

The Space Station Freedom workstation system is a distributed network of computer based workstations that provides the man-machine interfaces for controlling space station systems. This includes control of external manipulator, robotic and free flyer devices by crewmembers in the space station's pressurized shirt-sleeve environment. These remotely controlled devices help minimize the requirement for costly crew extravehicular activity (EVA) time for such tasks as station assembly and payload support. Direct window views may be used for controlling some of the systems, but many activities will be remote or require levels of detail not possible by direct observation. Since controlling remote devices becomes more difficult when direct views are inadequate or unavailable, many performance enhancing techniques have been considered for representing information about remote activities to the operator. Described here are the telepresence techniques under consideration to support operations and training. This includes video enhancements (e.g., graphic and text overlays and stereo viewing), machine vision systems, remote activity animation, and force reflection representation.

Jensen, Dean G.↗

NASA NTRSDate not supplied

25 Years of CALIPSO

Selected for development in 1998 and launched together with CloudSat in 2006, the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations(CALIPSO) mission terminated science operations in the summer of 2023 aftercompleting17 years of on-orbit observations. As one of NASA’s Earth System Science Pathfinder missions, CALIPSO was truly a pathfinder. CALIPSO observations have provided a new perspective on clouds and aerosol and have not only met but far exceeded the original objectives of the mission. Many unanticipated findings and data applications have been discovered along the way. Fly-ing with many other remote sensing instruments, as part of the A-train constellation, stimulated the discovery of numerous retrieval synergies between lidar and other sensors. This paper describes how the CALIPSO mission came to be, discusses some of the early choices made by the CALIPSO team that shaped the mission, and some of the challenges facing the team in developing the first-ever global climatologies of aerosol and cloud based on lidar observations.

Space lidar↗

DOE OSTI2024

MDLoader: A Hybrid Model-Driven Data Loader for Distributed Graph Neural Network Training

Scalable data management is essential for processing large scientific dataset on HPC platforms for distributed deep learning. In-memory distributed storage is preferred for its speed, enabling rapid, random, and frequent data access required by stochastic optimizers. Processes use one-sided or collective communication to fetch remote data, with optimal performance depending on (i) dataset characteristics, (ii) training scale, and (iii) interconnection network. Empirical analysis shows collective communication excels with larger mini-batch sizes and/or fewer processes, whereas one-sided communication outperforms at larger scales. We propose MDLoader, a hybrid in-memory data loader for distributed graph neural network training. MDLoader features a model-driven performance estimator that dynamically selects between one-sided and collective communication at the beginning of training using Tree of Parzen Estimators (TPE). Evaluations on NERSC Perlmutter and OLCF Summit show MDLoader outperforms single-backend loaders by up to 2.83 × and predicts the suitable communication method with 96.3% (Perlmutter) and 94.3% (Summit) success rate.

Bae, Jonghyun↗

DOE OSTI2022

A Weakly-Supervised, Multitask Deep Learning Framework for Shadow Mitigation in Remote Sensing Imagery

We propose a weakly-supervised, multitask framework for training a convolutional neural network to solve the problem of cloud shadow mitigation given only cloud and shadow masks as labels. The network minimizes the Wasserstein distance between shadows and their proximal sunlit neighborhoods, generating a supervisory signal directly from within the input image. We extract further utility from the shadow mask through multitask learning by introducing an auxiliary task of shadow segmentation. Our approach is advantageous since it performs mitigation in an end-to-end framework which requires only a shadowed image for inference. We apply this process to the Landsat 8 OLI SPARCS validation data set and demonstrate plausible results.

Couwenhoven, Scott↗

DOE OSTI2020

Framework For Spatial Agricultural Crop Yield Prediction Model Development

This framework was developed to provide data preprocessing for spatiotemporal agricultural yield data and remote sensing data for modelling using artificial neural networks (ANNs) to predict subfield crop yield estimates. The software includes methods to train, validate, and test ANN models. It also include methods to infer on new remote sensing data.

Griffel, LloydM.↗