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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Videos, photos, and AI-derived grain size data associated with “High-throughput AI Video Surveys Enable Reproducible Multiscale Sediment Size Mapping, with Implications for Hydrobiogeochemical Parameterization”

NOTE: The manuscript associated with this data package is currently in review. The data may be revised based on reviewer feedback. Upon manuscript acceptance, this data package will be updated with the final dataset and additional metadata. This data package is associated with the manuscript “High-throughput AI Video Surveys Enable Reproducible Multiscale Sediment Size Mapping, with Implications for Hydrobiogeochemical Parameterization” under review. This data package includes five data types: 1) raw photos and videos from drone survey and walking smartphone surveys; 2) images derived from raw videos; 3) manual labeling of reference scales; 4) metadata for all images and photo resolution derived from artificial intelligence (AI) models or manual labels, 5) grain size data obtained from AI models for all photos, 6) metadata and grain size data after quality control, 7) summaries of sample efficiency for all data, and 8) computational fluid dynamics (CFD) data used to support hydro-biogeochemical (HBGC) parameter estimation. Such data is used to 1) demonstrate significant improvements in accuracy, efficiency, and quality control for grain size data collection with the help of AI models, 2) study the spatial heterogeneity of grain size and observation reproducibility based on tens of thousands of data points generated by the AI models, and 3) evaluate the impacts of grain size heterogeneity on key HBGC parameters across sediment-to-reach and hourly-to-yearly scales. In particular, the data package contains 116 folders and 179696 files. The files include 41 videos in .mov format, 64047 photos in .jpg format, 13541 video-derived photos in .png format, 12747 segmentation mask data in .tif format, 12747 segmentation data in .json format, 24771 .csv files that with metadata and grain size for each individual photo as well as water depth and velocity data from CFD and observation, 51791 .txt files of raw AI predicted labels, and 11 flight record data in .srt format. The summary for all metadata and grain size statistics information is included in “Scales_V3_NG.csv” and “Statistics_V3_NG.csv”. The summary for data that pass data quality control (QC) level 0-2 is included in “QCStatistics_V3_NG.csv”. The QC level 0 represents photos whose photo resolution is positive, excluding photos that miss reference scale. The QC level 1 means reference scale circularity uncertainty is less than 5% for smartphone images while representing photo resolution is larger than 0.44 mm/pixel for drone images. The QC level 2 means excluding photos whose grain number is less than 100, a minimum number of grains recommended by classic literature. The summary for each video’s name, length, frame rates, survey area, grain number, survey efficiency, etc. can be found in “QCSummary_V3_NG.csv”. The summary for site name, GPS coordinates, and number of images at each site can be found in “SitesSummary_V3_*.csv” files. Overall computational efficiency summary is reported in Table 4 of accompanying manuscript. Additionally, the nitrate concentration data used in this work was downloaded from an existing dataset published on ESS-DIVE (Boat-Dragged Sensor Hanford Reach.csv; Conner A. et al., 2020). We thank the United States Forest Service, Washington Department of Fish and Wildlife, Washington Department of Natural Resources, Cowiche Canyon Conservatory, Port of Benton, and the Confederated Tribes and Bands of the Yakama Nation for access to field locations where the data were collected. We also thank the Yakama Nation Tribal Council and Yakama Nation Fisheries for working with us to facilitate data collection and optimization of data usage according to their values and worldview.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1975

Accuracy, resolution, and cost comparisons between small format and mapping cameras for environmental mapping

Successful aerial photography depends on aerial cameras providing acceptable photographs within cost restrictions of the job. For topographic mapping where ultimate accuracy is required only large format mapping cameras will suffice. For mapping environmental patterns of vegetation, soils, or water pollution, 9-inch cameras often exceed accuracy and cost requirements, and small formats may be better. In choosing the best camera for environmental mapping, relative capabilities and costs must be understood. This study compares resolution, photo interpretation potential, metric accuracy, and cost of 9-inch, 70mm, and 35mm cameras for obtaining simultaneous color and color infrared photography for environmental mapping purposes.

Clegg, R. H.↗
NASA NTRS1975

A comparison of 9-inch, 70mm, and 35mm cameras

Successful aerial photography depends on aerial cameras that provide acceptable photographs within the cost restrictions of the job. For topographic mapping where ultimate accuracy is required, only large-format mapping cameras will suffice. For mapping environmental patterns of vegetation, soils, or water pollution, 9-inch cameras often exceed accuracy and cost requirements, and small formats may be an overall better choice. In choosing the best camera for environmental mapping, relative capabilities and costs must be understood. This study compares resolution, photo interpretation potential, metric accuracy, and cost of 9-inch, 70 mm, and 35 mm cameras for obtaining simultaneous color and color-infrared photography for environmental mapping purposes.

Clegg, R. H.↗
NASA NTRS1978

Mars - Topographic control of clouds, 1907-1973

Mariner 9 high-resolution photos and topographic information were used to make a topographic analysis of 'blue' and 'red' cloud positions reported over a 66-year period. A sample of 77 'blue' cloud sites lay preferentially at the highest Martian elevations; 60% centered precisely on the seven major volcanic mountain peaks (unknown when the clouds were observed); another 16% lay on substantial slopes or contacts between cratered terrain and lower plains. The median altitude of blue cloud sites was 2.1 km above the global topographic median. These results agree with other evidence that most earth-detected blue clouds are orographic uplift clouds, composed of condensates. Over half of 131 sporadic yelowish or red clouds were associated with blue clouds or volcanoes, and thus probably did not represent dust storm phenomena. Of 88 'possible dust clouds', about two-thirds occur at borders between light and dark areas, in the light regions. These sites may have thin veneers of dust, and current depositional or denudational activity. Median altitude of 'possible dust cloud' sites was 0.5 km below the global topographic median.

Hartmann, W. K.↗
DOE OSTI2025

Photoelectrochemical Imaging of Charge Separation between MoS 2 Triangles and Insulating SiO 2 Support

The role of the insulating support in photocatalysis is poorly understood. Using high-resolution photo-scanning electrochemical microscopy (photo-SECM), we observed significant spatial charge separation in few-layer-thick molybdenum disulfide (MoS 2 ) triangles attached to a SiO 2 substrate. Spatially resolved surface photovoltage (SPV) measurements revealed that photogenerated holes migrate from MoS 2 to the SiO 2 surface and travel laterally over distances exceeding 2 μm, driven by the builtin electric field of ~1.7 kV/cm. In thicker and less uniform flakes, the charge separation is dominated by internal driving forces within MoS 2 , without significant contribution from SiO 2 . These findings underscore the importance of insulator–semiconductor interactions for effective charge separation, suggesting a new strategy for optimizing photocatalytic systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Artificial intelligence models, photos, and data associated with the manuscript “Quantifying Streambed Grain Size, Uncertainty, and Hydrobiogeochemical Parameters Using Machine Learning Model YOLO” (v2)

This data package is associated with the manuscript “Quantifying Streambed Grain Size, Uncertainty, and Hydrobiogeochemical Parameters Using Machine Learning Model YOLO” published in Water Resources Research (Chen et al., 2024). This data package includes the training, validation, testing, and prediction data used by the artificial intelligence (AI) model for automated grain size and hydro-biogeochemistry quantification using streambed photos. The grain size data are extracted for each photo using You Look Only Once (YOLO), a pre-trained object detection model. This data package was originally published in October 2023. It was updated August 2025 (v2; new and modified files). File and folder names were not revised to indicate changes. See the change history section in the readme for more details. Please see flmd.csv for a list of all files contained in this data package and descriptions for each. Please see dd.csv for a data dictionary that defines the column headers of .csv files in the data package. This dataset is comprised of one data folder containing (1) file-level metadata; (2) data dictionary; (3) readme; and (4) six subfolders. Subfolders 1 to 4 include the training, validation, testing, and prediction data. Subfolder 5_Summary includes the summary results of different combinations of training, validation, testing, and prediction data. Subfolder 6_SupplementalData includes additional data downloaded from public sources (Kaufman et al., 2023a; Kaufman et al., 2023b; Garefalakis et al., 2023; Mair et al., 2024; https://github.com/river-corridors-sfa/Geospatial_variables). In total, the data package includes 110 folders and 44,283 files. These files include 9,047 .jpg photos, 1 .png photo, 3 .tif photos; 26,639 photo labels and individual grain sizes and probability from AI (.txt); 8,447 grain size distribution data (.dat); and 126 CSV files for results summary, and 14 required metadata files (.xlsx). The summary CSV files contain 68 columns and approximately 2,200 rows that represent photo names, site locations, recording time, GPS coordinates, grains sizes (D10, D50, D60, and D84), number of grains, and additional hydro-biogeochemical data such as water depth, flow velocity, Manning’s coefficient, friction factor, hydraulic conductivity, permeability, streambed interstitial velocity magnitude, mass transfer rate, and nitrate uptake velocity. The photos were obtained from 75 sites in the Yakima River Basin and the Columbia River shorelines, and other associated data from samples and sensors obtained when the photos were taken are publicly available (Fulton et al. 2022; Grieger et al. 2023). All files are .csv, .txt, .dat, .jpg, or .pdf. We acknowledge the Yakama Nation as owners and caretakers of the lands where we collected some of these data. We thank the Confederated Tribes and Bands of the Yakama Nation Tribal Council and Yakama Nation Fisheries for working with us to facilitate sample collection and optimization of data usage according to their values and worldview.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

X-ARAPUCA Photon Detectors in SBND

The Short-Baseline Near Detector (SBND) is a liquid argon time projection chamber (LArTPC) for detecting neutrinos from Fermilab's Booster Neutrino Beam. Located 110 m downstream of the beam target, SBND collects both ionization electrons and scintillation photons from particle interactions within the detector volume. The photon detection system (PDS) consists of both PMTs and X-ARAPUCAs, a novel technology featuring large area cost-effective coverage with single photo-electron resolution at cryogenic temperatures. SBND utilizes two types of X-ARAPUCAs: one sensitive to vacuum ultra-violet (VUV) scintillation light produced and one sensitive to visible light for the light reflected off TPB-coated reflective foils. SBND is the only experiment currently testing the X-ARAPUCA technology in a neutrino beam over a period of several years, and will provide critical information for deployment in future detectors like DUNE. This poster will cover the calibration and early performance of X-ARAPUCAs in SBND.

Dalager, Olivia [Fermilab]↗
NASA NTRS1975

Hydrogeological investigations in the Pampa of Argentina

In large areas of the Argentinian Pampa the salinization of ground water creates water supply difficulties. Investigations of satellite imagery (Landsat-1 and Skylab) which were based on an extensive ground survey revealed that differences in the vegetation cover are closely related to depth and salinity of ground water. Narrow elongated depressions called bajos are often the only indication of fresh ground water. They can be easily detected on the imagery. Due to their high resolution, Skylab photos even allow a quantitative estimation of fresh ground water situated below the bajos. In general however Landsat-1 imagery sufficed for evaluation. In the area of the Rio Tercero a fossil drainage pattern was discovered and in Corrientes province, soil types could be discriminated and compared to the Soil Map of the World.

Kruck, W.↗
NASA NTRS2004

A Reassessment of the Mars Ocean Hypothesis

Initial work on the identification and mapping of potential ancient shorelines on Mars was based on Viking Orbiter image data (Parker et al., 1987, 1989, 1993). The Viking Orbiters were designed to locate landing site for the two landers and were not specifically intended to map the entire planet. Fortunately, they mapped the entire planet. Unfortunately, they did so at an average resolution of greater than 200m/pixel. Higher resolution images, even mosaics of interesting regions, are available, but relatively sparse. Mapping of shorelines on Earth requires both high-resolution aerial photos or satellite images and good topographic information. Three significant sources of additional data from missions subsequent to Viking are useful for reassessing the ocean hypothesis. These are: MGS MOC images; MGS MOLA topography; Odyssey THEMIS IR and VIS images; and MER surface geology at Meridiani and Gusev. Okay, my mistake: Four.

Parker, T. J.↗
NASA NTRS2021

NASA-NOAA Feasibility Report

The western stock of Steller sea lions and the Aleutian stock of harbor seals both experienced dramatic declines in abundance during the past few decades, particularly in the central and western Aleutian Islands. Both stocks have continued to decline in recent years, thus are a high priority for routine monitoring. NOAA’s Marine Mammal laboratory (MML) has conducted aerial surveys of marine mammals throughout coastal Alaska to monitor trends in population abundance and distribution for more than 20 years. These surveys have typically been conducted from a twinengine aircraft, such as a DHC-6 Twin Otter, operated by NOAA pilots and crewmembers. Aerial surveys in the Aleutian Islands present significant challenges and higher risks due to their remoteness, rugged terrain, and extreme weather. To evaluate the feasibility of transitioning from manned to unmanned aerial surveys in this region, thereby reducing risks to NOAA personnel and high-value aircraft, we propose to conduct test survey flights in the Near Islands of Alaska using a medium-sized, fixed-wing UAV (NASA’s SIERRA-B aircraft) with a nadir-aimed imaging payload. Survey flights are planned to be conducted during daylight hours on days with VFR conditions and less than ~20 knot winds at altitudes of 800-1200 feet. Survey imagery, including thermal video and high-resolution color photos, would be collected at known Steller sea lion and harbor seal sites along the coastlines of Shemya, Nizki, Alaid, Agattu, and Attu Islands. The planning team will request approval and support from the US Air Force to conduct this project at Eareckson Air Station (Shemya Island, AK) during the month of September 2022 when weather conditions in the western Aleutian Islands are expected to be optimal.

NOAA↗
NASA NTRS2012

Generating Ground Reference Data for a Global Impervious Surface Survey

We are engaged in a project to produce a 30m impervious cover data set of the entire Earth for the years 2000 and 2010 based on the Landsat Global Land Survey (GLS) data set. The GLS data from Landsat provide an unprecedented opportunity to map global urbanization at this resolution for the first time, with unprecedented detail and accuracy. Moreover, the spatial resolution of Landsat is absolutely essential to accurately resolve urban targets such as buildings, roads and parking lots. Finally, with GLS data available for the 1975, 1990, 2000, and 2005 time periods, and soon for the 2010 period, the land cover/use changes due to urbanization can now be quantified at this spatial scale as well. Our approach works across spatial scales using very high spatial resolution commercial satellite data to both produce and evaluate continental scale products at the 30m spatial resolution of Landsat data. We are developing continental scale training data at 1m or so resolution and aggregating these to 30m for training a regression tree algorithm. Because the quality of the input training data are critical, we have developed an interactive software tool, called HSegLearn, to facilitate the photo-interpretation of high resolution imagery data, such as Quickbird or Ikonos data, into an impervious versus non-impervious map. Previous work has shown that photo-interpretation of high resolution data at 1 meter resolution will generate an accurate 30m resolution ground reference when coarsened to that resolution. Since this process can be very time consuming when using standard clustering classification algorithms, we are looking at image segmentation as a potential avenue to not only improve the training process but also provide a semi-automated approach for generating the ground reference data. HSegLearn takes as its input a hierarchical set of image segmentations produced by the HSeg image segmentation program [1, 2]. HSegLearn lets an analyst specify pixel locations as being either positive or negative examples, and displays a classification of the study area based on these examples. For our study, the positive examples are examples of impervious surfaces and negative examples are examples of non-impervious surfaces. HSegLearn searches the hierarchical segmentation from HSeg for the coarsest level of segmentation at which selected positive example locations do not conflict with negative example locations and labels the image accordingly. The negative example regions are always defined at the finest level of segmentation detail. The resulting classification map can be then further edited at a region object level using the previously developed HSegViewer tool [3]. After providing an overview of the HSeg image segmentation program, we provide a detailed description of the HSegLearn software tool. We then give examples of using HSegLearn to generate ground reference data and conclude with comments on the effectiveness of the HSegLearn tool.

Tilton, James C.↗
NASA NTRS1971

A search for life on earth at 100 meter resolution.

Study of several thousand photos indicating that about 0.01 of Gemini and Apollo photographs of the earth at 100 m resolution reveal signs of life - rectangular arrays due to human agricultural and urban territoriality, roads, canals, jet contrails, and industrial pollution. Potential false positives - e.g., dunes, sand bars, jetstream clouds - abound. A curve is derived for the detectivity of contemporary life on earth, in a plot of ground resolution versus global coverage. A comparable biology on Mars would not have been detected by all observations of Mars through Mariner 7.

Sagan, C.↗
NASA NTRS2018

Earth Science Engagement Through Art and Astronaut Photography

For over five decades of manned spaceflight missions, NASA astronauts have taken extraordinary photographs of Earth's surface and dynamic processes. Humans on board the International Space Station (ISS) have a unique platform to perform Earth observations at various viewing angles, seasons, and times of day. Astronaut photos taken from the ISS comprise a true‐color (RGB) dataset taken with multiple handheld digital cameras and lens types (prior to 2004, film cameras were in use). Earth observations through astronaut photography are an important and unique remote sensing method when monitoring natural disasters, urban growth, and environmental changes. While astronaut imagery can be used for earth science research, there is also an artistic aspect to the photography that fascinates a wide global population. A broader public audience can be introduced to earth science through high resolution, Earth art photos taken from the perspective of an astronaut. The Crew Earth Observations (CEO) Facility within the Earth Science and Remote Sensing Unit at NASA's Johnson Space Center supports the acquisition, analysis, and curation of astronaut photography of Earth's surface and atmosphere. CEO's website, the Gateway to Astronaut Photography of Earth (eol.jsc.nasa.gov), provides free public access to view, search, and download over three million images taken by astronauts throughout all of NASA's crewed spaceflight history, with an emphasis on current ISS imagery. The CEO Facility actively curates a digital collection of exceptional Earth art astronaut photos used for public engagement. Our new Downloadable Earth Art page focuses on broad earth science topics including: mountains, water, clouds, agriculture, as well as an "abstract" category. This continuously‐updated collection is comprised of freely accessible and high‐quality downloadable materials, such as single‐ and dual‐screen digital wallpapers. All Earth Art materials are presented with science‐based information that complements the artistic qualities of the imagery, and facilitate connections between general audiences and earth science from the International Space Station.

Meado, Andrea L.↗
NASA NTRS1982

Geographic science

The use of multispectral imaging systems in land use, geomorphological, and cartographic applications is examined. Spatial, spectral, and geometric resolution requirements for photo interpretation and multispectral pattern recognition are discussed. The potential contributions of these systems in specific experiments is also included.

Bryant, N.↗
NASA NTRS1991

Normal-incidence soft X-ray telescopes

Photos obtained during 5 min of observation time from the flight of a 25-cm-diameter normal-incidence soft-X-ray (63.5 A) telescope on September 11, 1989, are analyzed, and the data are compared to the results expected from tests of the mirror surfaces. These tests cover a range of spatial periods from 25 cm to 1 A. The photos demonstrate a resolution close to the photon shot-noise limit and a reduction in the scattering of the multilayer mirror compared to a single surface for scattering angles above 1 arcmin, corrresponding to surface irregularities with spatial periods below 10 microns. These results are used to predict the possible performance of future telescopes. Sounding rocket observations might be able to reach a resolution around 0.1 arcsec.

Spiller, Eberhard↗
NASA NTRS1970

A search for life on earth at 100 meter resolution

A study of several thousand photos indicated approximately 1% of Gemini and Apollo photographs of the earth at 100 m resolution revealed signs of life; rectangular arrays due to human agricultural and urban territoriality, roads, canals, jet contrails, and industrial pollution. Potential false positives such as dunes, sand bars, and jet stream clouds abound. A curve was derived for the detectivity of contemporary life on earth, in a plot of ground resolution versus global coverage. A comparable biology on Mars would not have been detected by all observations of Mars through Mariner 7. Forthcoming Mars orbiter and lander imaging experiments hold significant promise of detecting life on Mars of contemporary terrestrial extent and advancement, should such life exist.

Sagan, C.↗
NASA NTRS2018

Public Engagement of Earth Science and Art Using Astronaut Photography

For over fifty years of crewed spaceflight missions, NASA astronauts have taken remarkable photographs of the Earth that offer various perspectives for viewing dynamic Earth processes. The International Space Station (ISS) today provides a unique platform to view and capture imagery of Earth at a variety of viewing angles, seasons, and times of day. Astronaut photos comprise a variable true-color (RGB - Red Green Blue) dataset taken with multiple handheld digital (and historically, film) cameras and lens types that differ from nadir-looking satellite-based remote sensing instruments with fixed temporal and spatial resolutions. The Crew Earth Observations (CEO) Facility within the Earth Science and Remote Sensing Unit at NASA’s Johnson Space Center supports the acquisition, analysis, and curation of astronaut photography of Earth’s surface and atmosphere. CEO’s website, the Gateway to Astronaut Photography of Earth (eol.jsc.nasa.gov), provides free public access to view, search, and download over three million images taken by astronauts from Mercury, Gemini, and Apollo missions to current ISS missions. Handheld images taken by astronauts from the ISS are used for scientific research and public engagement in STEM (Science, Technology, Engineering, Mathematics) fields, particularly earth science. While segments of the public are actively interested in STEM fields, there may be groups who are underexposed to and disinterested in earth science or are unaware of astronaut photography from the ISS. A broader public audience can be introduced to earth science, STEM fields, and ISS research through artistic or abstract photos of the Earth. Wide angle pictures taken of Earth have identifiable landforms and bodies of water, however zoomed in, very high resolution photos without obvious geographic reference can attract new viewing audiences on a purely aesthetic basis. The CEO Facility actively curates a collection of exceptional astronaut photos of Earth as art used to reach a wider population through increased digital products and outreach events. This continuously-updated collection feeds development of freely accessible and high-quality downloadable materials including scientific articles, videos, computer wallpapers, and social media content. These materials are presented with science-based information that augments the artistic qualities of the imagery, and facilitate connection between art-engaged or non-STEM audiences to the ISS research platform and the need for future innovative STEM research.

Meado, Andrea L.↗
NASA NTRS1982

Familiarization with LANDSAT imagery

Learning objectives of the activities provided include: (1) reading the annotation of a LANDSAT image; (2) becoming acquainted with the characteristics of 1:1,000,000 scale transparencies and prints of MSS images; (3) noting the general information visible in LANDSAT photo products; (4) observing changes of appearance of any ground feature or class in the black and white images made from the four MSS bands and the characteristic color of each class in color composites; (5) determining the degree to which a LANDSAT image meets map accuracy standards and can be fitted to map projections; (6) assessing the effects of LANDSAT enlargements and scale changes and of the limitations of satellite resolution relative to aerial photos; (7) observing the influence of time of acquisition (season) on a scene; (8) getting a feel for image quality as dependent on processing and photoreproduction; (9) appreciating the characteristics of the RBV and thermal band imagery obtained from LANDSAT-3; and (10) becoming familiar with certain attributes of adjacent LANDSAT images which permit them to be joined in mosaics and to be viewed in stereo.

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