Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Ground truth”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
NASA NTRS2010

Utilization of Airborne and in Situ Data Obtained in SGP99, SMEX02, CLASIC and SMAPVEX08 Field Campaigns for SMAP Soil Moisture Algorithm Development and Validation

Field experiment data sets that include coincident remote sensing measurements and in situ sampling will be valuable in the development and validation of the soil moisture algorithms of the NASA's future SMAP (Soil Moisture Active and Passive) mission. This paper presents an overview of the field experiment data collected from SGP99, SMEX02, CLASIC and SMAPVEX08 campaigns. Common in these campaigns were observations of the airborne PALS (Passive and Active L- and S-band) instrument, which was developed to acquire radar and radiometer measurements at low frequencies. The combined set of the PALS measurements and ground truth obtained from all these campaigns was under study. The investigation shows that the data set contains a range of soil moisture values collected under a limited number of conditions. The quality of both PALS and ground truth data meets the needs of the SMAP algorithm development and validation. The data set has already made significant impact on the science behind SMAP mission. The areas where complementing of the data would be most beneficial are also discussed.

Soil moisture↗
NASA NTRS2024

Iona Ecological Conservation: Utilizing Earth Observations to Understand Landscape Patterns and Assist in Wildlife Management in Iona National Park, Angola

Following the end of the Angolan civil war in 2002, human and livestock populations have increased exponentially within Iona National Park. An ongoing drought since 2017 has brought these people and livestock into increasing competition with local wildlife for resources – highlighting a conservation challenge that will become more entrenched as the effects of anthropogenic climate change increase. In 2019, African Parks began co-managing Iona National Park in Angola with the Angolan government, hoping to enact scientifically grounded management strategies to meet this challenge. To accomplish this, African Parks needed contemporary and historic information on the spatial distribution of landcover types within Iona and adjacent areas. We constructed and applied a Random Forest classifier in Google Earth Engine to multispectral imagery gathered from Landsat 5, 7, 8 and Sentinel-1 and 2 to meet this need. Using the classifier, we generated a time-series of land cover maps between 1990–2023, from which landscape metrics and change detection analysis were calculated to show how certain habitats and formations had changed over time. The resulting maps have producer and user’s accuracies above 87% and show four broad landcover regions within the study area. Notably, we observed a decrease in the park’s diversity as per the Shannon Diversity Index – an index that considers the richness of classes, as well the evenness of their distribution. A lack of arid specific land cover indices and ground-truthed training data from earlier years limited the accuracy and resolution of our landcover maps. However, this project still demonstrates that Earth observations can be used to form the basis of conservation policy in arid environments, where ground-truth data may be difficult to obtain or non-existent.

remote sensing↗
NASA NTRS1972

Investigation of techniques for correcting ERTS data for solar and atmospheric effects

The author has identified the following significant results. The design and fabrication of five radiant power measuring instruments (RPMI) for ERTS ground truth have been completed. These instruments will be deployed in concert with ERTS overflights to obtain radiometric measurements needed to determine solar and atmospheric parameters that effect ERTS radiance measurements. With these parameters, the accuracy and capability of various procedures for transforming ERTS data into absolute target reflectance signatures will be evaluated. The RPMI is a rugged, hand-carried instrument accurately calibrated to measure both downwelling and reflected radiance within each ERTS multispectral scanner band. A foldover handle permits a quick change from wide angle global or sky irradiance measurements to narrow angle radiance measurements from sky and ground targets. These measurements yield ground truth site reflectance and permit calculation of additional parameters such as beam transmittance between spacecraft and ground, and path radiance (path reflectance).

Rogers, R. H.↗
DOE OSTI2023

MnEdgeNet for accurate decomposition of mixed oxidation states for Mn XAS and EELS L2,3 edges without reference and calibration

Accurate decomposition of the mixed Mn oxidation states is highly important for characterizing the electronic structures, charge transfer and redox centers for electronic, and electrocatalytic and energy storage materials that contain Mn. Electron energy loss spectroscopy (EELS) and soft X-ray absorption spectroscopy (XAS) measurements of the Mn L2,3 edges are widely used for this purpose. To date, although the measurements of the Mn L2,3 edges are straightforward given the sample is prepared properly, an accurate decomposition of the mix valence states of Mn remains non-trivial. For both EELS and XAS, 2+, 3+, and 4+ reference spectra need to be taken on the same instrument/beamline and preferably in the same experimental session because the instrumental resolution and the energy axis offset could vary from one session to another. To circumvent this hurdle, in this study, we adopted a deep learning approach and developed a calibration-free and reference-free method to decompose the oxidation state of Mn L2,3 edges for both EELS and XAS. A deep learning regression model is trained to accurately predict the composition of the mix valence state of Mn. To synthesize physics-informed and ground-truth labeled training datasets, we created a forward model that takes into account plural scattering, instrumentation broadening, noise, and energy axis offset. With that, we created a 1.2 million-spectrum database with 1-by-3 oxidation state composition ground truth vectors. The library includes a sufficient variety of data including both EELS and XAS spectra. By training on this large database, our convolutional neural network achieves 85% accuracy on the validation dataset. We tested the model and found it is robust against noise (down to PSNR of 10) and plural scattering (up to t/λ = 1). We further validated the model against spectral data that were not used in training. In particular, the model shows high accuracy and high sensitivity for the decomposition of Mn 3 O 4 , MnO, Mn 2 O 3 , and MnO 2 . The accurate decomposition of Mn 3 O 4 experimental data shows the model is quantitatively correct and can be deployed for real experimental data. Our model will not only be a valuable tool to researchers and material scientists but also can assist experienced electron microscopists and synchrotron scientists in the automated analysis of Mn L edge data.

25 ENERGY STORAGE↗
NASA NTRS1993

MAX-91: Polarimetric SAR results on Montespertoli site

The polarimetric Synthetic Aperture Radar (SAR) is a powerful sensor for high resolution ocean and land mapping and particularly for monitoring hydrological parameters in large watersheds. There is currently much research in progress to assess the SAR operational capability as well as to estimate the accuracy achievable in the measurements of geophysical parameters with the presently available airborne and spaceborne sensors. An important goal of this research is to improve our understanding of the basic mechanisms that control the interaction of electro-magnetic waves with soil and vegetation. This can be done both by developing electromagnetic models and by analyzing statistical relations between backscattering and ground truth data. A systematic investigation, which aims at a better understanding of the information obtainable from the multi-frequency polarimetric SAR to be used in agro-hydrology, is in progress by our groups within the framework of SIR-C/X-SAR Project and has achieved a most significant milestone with the NASA/JPL Aircraft Campaign named MAC-91. Indeed this experiment allowed us to collect a large and meaningful data set including multi-temporal multi-frequency polarimetric SAR measurements and ground truth. This paper presents some significant results obtained over an agricultural flat area within the Montespertoli site, where intensive ground measurements were carried out. The results are critically discussed with special regard to the information associated with polarimetric data.

Baronti, S.↗
NASA NTRS1984

A statistical evaluation and comparison of VISSR Atmospheric Sounder (VAS) data and corresponding rawinsonde measurements

Three mesoscale sounding data sets from the VISSR Atmospheric Sounder (VAS) produced using different retrieval techniques were evaluated of corresponding ground truth rawinsonde data for 6-7 March 1982. Mean, standard deviations, and RMS differences between the satellite and rawinsonde parameters were calculated over gridded fields in central Texas and Oklahoma. Large differences exist between each satellite data set and the ground truth data. Biases in the satellite temperature and moisture profiles seem extremely dependent upon the three dimensional structure of the atmosphere and range from 1 deg to 3 deg C for temperature and 3 deg to 6 deg C for dewpoint temperature. Atmospheric gradients of basic and derived parameters determined from the VAS data sets produced an adequate representation of the mesoscale environment but their magnitudes were often reduced by 30 to 50%.

Source record↗
NASA NTRS2022

Using commercial high-resolution satellite imagery to monitor a nuisance macroalga in the largest marine protected area in the U.S.A.

Satellite imagery is a useful tool for monitoring and mapping the distribution of invasive or nuisance algal species on coral reefs over the temporal and spatial scales needed for ecosystem management. Visual inspections of high-resolution satellite imagery were used to detect the newly discovered nuisance alga, Chondria tumulosa , at Manawai (Pearl and Hermes Atoll) in combination with ground-truthing surveys. Low-albedo (“dark”) survey sites on spur habitats were associated with mean C. tumulosa cover seven times higher than adjacent high-albedo (“light”) sites. There was an inverse relationship between C. tumulosa percent cover at ground-truthing sites and mean reflectance values. Archival satellite imagery showed that areas of high C. tumulosa cover (i.e., dark patches) were not evident on or before 2015 on the northeast backreef. This case study is the first to apply satellite imagery to target a nuisance red macroalgal bloom on a coral reef. The ability to use satellites for the detection of nuisance or invasive benthic species, such as expansive mats of C. tumulosa , provides managers with a valuable tool, especially in remote regions.

Invasive↗
NASA NTRS1976

Spectral reflectance signatures of coastal pollutants

The author has identified the following significant results. Remote sensing from satellites and aircraft was combined with ground truth collected from ships in an attempt to classify coastal and estuarine water types by their spectral reflectance signatures. During ocean waste disposal operations 40 miles off the Delaware coast, water samples were collected and spectroradiometric measurements conducted in the field to provide ground truth for aircraft and LANDSAT overpasses with multispectral scanners.

Klemas, V.↗
DOE OSTI2021

Haze Mitigation in High-Resolution Satellite Imagery using Enhanced Style-Transfer Neural Network and Normalization Across Multiple GPUs

Despite recent advances in deep learning approaches, haze mitigation in large satellite images is still a challenging problem. Due to amorphous nature of haze, object detection or image segmentation approaches are not applicable. Also it is practically infeasible to obtain ground truths for training. Bounded memory capacity of GPUs is another constraint that limits the size of image to be processed. In this paper, we propose a style transfer based neural network approach to mitigate haze in a large overhead imagery. The network is trained without paired ground truths; further, perception loss is added to restore vivid colors, enhance contrast and minimize artifacts. The paper also illustrates our use of multiple GPUs in a collective way to produce a single coherent clear image where each GPU dehazes different portions of a large hazy image.

Park, Byung↗
NASA NTRS1987

Earth science research

The analysis of ground-truth data from the boreal forest plots in the Superior National Forest, Minnesota, was completed. Development of statistical methods was completed for dimension analysis (equations to estimate the biomass of trees from measurements of diameter and height). The dimension-analysis equations were applied to the data obtained from ground-truth plots, to estimate the biomass. Classification and analyses of remote sensing images of the Superior National Forest were done as a test of the technique to determine forest biomass and ecological state by remote sensing. Data was archived on diskette and tape and transferred to UCSB to be used in subsequent research.

Botkin, Daniel B.↗
NASA NTRS1990

Flight data acquisition methodology for validation of passive ranging algorithms for obstacle avoidance

The automation of low-altitude rotorcraft flight depends on the ability to detect, locate, and navigate around obstacles lying in the rotorcraft's intended flightpath. Computer vision techniques provide a passive method of obstacle detection and range estimation, for obstacle avoidance. Several algorithms based on computer vision methods have been developed for this purpose using laboratory data; however, further development and validation of candidate algorithms require data collected from rotorcraft flight. A data base containing low-altitude imagery augmented with the rotorcraft and sensor parameters required for passive range estimation is not readily available. Here, the emphasis is on the methodology used to develop such a data base from flight-test data consisting of imagery, rotorcraft and sensor parameters, and ground-truth range measurements. As part of the data preparation, a technique for obtaining the sensor calibration parameters is described. The data base will enable the further development of algorithms for computer vision-based obstacle detection and passive range estimation, as well as provide a benchmark for verification of range estimates against ground-truth measurements.

Smith, Phillip N.↗
NASA NTRS2019

Scenario Complexity for Unmanned Aircraft System Traffic

This work introduces an approach to estimate the complexity of a low-altitude air traffic scenario involving multiple UASs using mathematical programming. Given a set of multi-point UAS flight trajectories, vehicle dynamics, and a conflict resolution algorithm, an abstract model is developed such that it can be solved quickly using a mathematical programming optimization software without running high-fidelity simulations that can be computationally expensive and may not suit real-time apA quick and accurate assessment of complexity for a given traffic scenario can help plan and schedule flights to alleviate traffic bottleneck and mitigate operation risks, especially for unmanned aerial system traffic management where high traffic density or complexity is expected. This work introduces a traffic scenario complexity metric that was constructed based on the number of potential conflicts weighted by the conflict resolution cost associated. The cost associated with a conflict is calculated based on the corresponding conflict resolution maneuvers. To obtain the conflict resolution maneuvers, a MILP-based optimization was formulated with the vehicle model and conflict management parameters incorporated. To evaluate the complexity metrics, an approach of using measurements from high-fidelity simulations was proposed. The scenario complexity measurements for 920 random-generated scenarios were obtained through high-fidelity simulations and treated as the ground truth. Two statistics methods: Pearson and Alternative Conditional Expectations were applied for analysis. The results showed that the number of flights has low correlation with the scenario complexity according to the correlation coefficients calculated by both methods. The Alternative Conditional Expectations method shows that the proposed scenario complexity metric has better correlation with the ground truth than the number of potential conflicts.plications. In the abstract model, each vehicle is represented by a time-varied vector associated with position, speed, and heading information. The total extra distance that aircraft need to divert from their original routes to avoid collisions is computed and used to setup a quadratic programming formula. The metrics including the number of conflicts and extra distances travelled by all vehicles are then utilized to estimate the complexity of a given UAS flight scenario. Results and verification against high-fidelity simulations will be provided in the final draft.

traffic complexity↗
NASA NTRS1990

Flight data acquisition methodology for validation of passive ranging algorithms for obstacle avoidance

An account is given of the methodology used to develop a data base containing low altitude imagery, augmented with the rotorcraft and sensor parameters required for passive-range estimation. The bata base is predicated on flight test data encompassing imagery, rotorcraft and sensor parameters, and ground truth range measurements. Data preparation was conducted by means of a novel technique for obtaining the sensor-calibration parameters. The data-base thus obtained will facilitate further development of computer vision-based obstacle-detection and passive range-estimation algorithms, as well as furnish a benchmark for verification of range estimates against ground-truth estimates.

Smith, Phillip N.↗
DOE OSTI2024

So you think you can track?

This work introduces a multi-camera tracking dataset consisting of 234 hours of video data recorded concurrently from 234 overlapping HD cameras covering a 4.2 mile stretch of 8-10 lane interstate highway near Nashville, TN. Video is recorded in cooperation with Tennessee State Department of Transportation and its policies. The video is recorded during a period of high traffic density with 500+ objects typically visible within the scene and typical object longevities of 3-15 minutes. GPS trajectories from 270 vehicle passes through the scene are manually corrected in the video data to provide a set of ground-truth trajectories for recall-oriented tracking metrics, and object detections are provided for each camera in the scene (159 million total before cross-camera fusion). Initial benchmarking of tracking-by-detection algorithms is performed against the GPS trajectories, and a best HOTA of only 9.5% is obtained (best recall 75.9% at IOU 0.1, 47.9 average IDs per ground truth object), indicating the benchmarked trackers do not perform sufficiently well at the long temporal and spatial durations required for traffic scene understanding. Video data, scene information, and vehicle trajectories are made publicly available at i24motion.org.

42 ENGINEERING↗
DOE OSTI2023

A baseline for ensemble-based, time-resolved inflow reconstruction for a single turbine using large-eddy simulations and latent diffusion models

We are interested in reconstructing winds flowing through a turbine on a second-by-second basis over a 10 min window. Previously, we developed a machine learning algorithm that takes in a snapshot of wind speed measurements and generates ensembles of three-dimensional wind field estimates. Here, we use these estimates as initial conditions in large-eddy simulations and reconstruct atmospheric and turbine response dynamic quantities in a synthetic field campaign. In doing so, we establish a baseline for model validation that future time-aware data assimilation techniques will be compared to. In turbine-free case studies, ground truth wind speeds consistently fall within our estimated wind speed distribution for the first 100 s after the simulation start. In simulations with turbines, the wind estimates show a small bias of 0.10 m s 1 and good correlation of 0.80 during the first 100 s. During this window, our estimates of the Blade 1 bending moment and generator power typically span the ground truth, with the estimate of the former performing better overall. In summary, this approach shows promise as a stand-alone technique for reconstructing real-world inflow and turbine dynamics in 1-2 min windows and as a foundation for future time-aware data assimilation techniques.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
NASA NTRS1989

Application of Landsat Thematic Mapper data for coastal thermal plume analysis at Diablo Canyon

The possibility of using Landsat Thematic Mapper (TM) thermal data to derive absolute temperature distributions in coastal waters that receive cooling effluent from a power plant is demonstrated. Landsat TM band 6 (thermal) data acquired on June 18, 1986, for the Diablo Canyon power plant in California were compared to ground truth temperatures measured at the same time. Higher-resolution band 5 (reflectance) data were used to locate power plant discharge and intake positions and identify locations of thermal pixels containing only water, no land. Local radiosonde measurements, used in LOWTRAN 6 adjustments for atmospheric effects, produced corrected ocean surface radiances that, when converted to temperatures, gave values within approximately 0.6 C of ground truth. A contour plot was produced that compared power plant plume temperatures with those of the ocean and coastal environment. It is concluded that Landsat can provide good estimates of absolute temperatures of the coastal power plant thermal plume. Moreover, quantitative information on ambient ocean surface temperature conditions (e.g., upwelling) may enhance interpretation of numerical model prediction.

Gibbons, D. E.↗
NASA NTRS2011

Effective Albedo of Vegetated Terrain at L-Band

This paper derives an explicit expression for an effective albedo of vegetated terrain from the zero- and multiple- order radiative transfer (RT) model comparison. The formulation establishes a direct physical link between the effective vegetation parameterization and the theoretical description of absorption and scattering within the canopy. The paper will present an evaluation of the derived albedo for corn canopies with data taken during an experiment at Alabama A&M Winfield A. Thomas Agricultural Research Station near Huntsville, Alabama in June, 1998. The test site consisted of two 50-m x 60-m plots - one with a bare surface and the other with grass cover - and four 30-m x 50-m plots of corn at different planting densities. One corn field was planted at a full density of 9.5 plants/sq m while the others were planted at 1/3, 1/2 and 2/3 of the full density. The fields were observed with a truck-mounted L-band radiometer at incident angle of 15 degree for the period of two weeks. Soil moisture (SM) changed daily due to irrigation and natural rainfall. Variations in gravimetric SM from 18 % to 34 % were seen during this period. Ground truth data, including careful characterization of the corn size and orientation statistics, and its dielectric, was also collected and used to simulate the effective albedo for the vegetation. The single-scattering albedo is defined as the fractional power scattered from individual vegetation constituents with respect to canopy extinction. It represents single-scattering properties of vegetation elements only, and is independent of ground properties. The values of the albedo get higher when there is dense vegetation (i.e. forest, mature corn, etc.) with scatterers, such as branches and trunks (or stalks in the case of corn), which are large with respect to the wavelength. This large albedo leads to a reduction in brightness temperature in the zero-order RT solution (known as tau-omega model). Higher-order multiple-scattering RT solutions are required for proper representation of scattering within vegetation. In this paper, an expression for an effective albedo for the whole canopy including the ground is derived for use in the zero-order RT model-based SM retrieval. This effective albedo takes into account of all the processes taking place within the canopy, including multiple-scattering. This new formulation will be presented and its importance for microwave SM retrieval will be evaluated for corn canopies in conjunction with the detailed ground truth data obtained during the experiment at Alabama in 1998. Emphasis will be placed on examining how the radiometer response to SM is modified by the corn canopy scattering under different field conditions. A semi-empirical parameterization of the effective albedo will be investigated through analysis of SM and vegetation water content effects on the effective albedo.

Kurum, Mehmet↗
NASA NTRSDate not supplied

Informing NLP Learning Tasks by Tracking User Features: An ASRS Use Case using Kaona

There has been growing interest in utilizing natural language processing (NLP) algorithms in Aviation Safety. This interest has extended to leveraging the decades of records publicly available on the Aviation Safety Reporting System (ASRS). While related literature has given more emphasis in lessons learned from the narratives, our prior work has focused on using NLP to support narrative search in the ASRS. Specifically, we evaluated if the use of alternative search mechanisms to keyword search, such as the retrieval of related narratives even without matching keywords could improve narrative discovery. A difficulty in experimenting alternative search mechanisms in any information retrieval task is the lack of ground truth. To address this limitation, we propose Kaona, a lightweight interface which enables the prototyping of alternative search retrieval tasks, by tracking user experience both explicitly (user-specified feedback), or implicitly (user navigation through interface affordances). Differently from distracting requests for feedback during user navigation, Kaona collects explicit feedback from users by mapping them to affordances which support the user workflow, while obtaining ground truth information for learning tasks.

human-computer-interaction↗