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All sourcesNASA NTRSDOE OSTI
NASA NTRS2022

Exploring Applications of Machine Learning for Wildfire Monitoring and Detection using Unmanned Aerial Vehicles

Wildfires are increasing in frequency and severity around the world, including the United States. The losses caused by wildfires could be mitigated if high-risk areas, hotspots, and flare-ups could be monitored continuously, such as through the use of Unmanned Aerial Vehicles (UAVs). This paper documents exploratory efforts using machine learning to determine efficient flight paths for UAVs and to detect wildfires using image classification. On path planning, three machine learning techniques—Genetic Algorithm, Simulated Annealing, and Dynamic Programming—were explored. Genetic Algorithm was found to be an effective approach for path planning for wildfire monitoring and surveillance by UAVs. For a scenario of 25 locations in a circular arrangement, the algorithm was able to return the optimal path. The accuracy and execution time was found to be sensitive to the algorithm hyperparameters selected, which was especially evident in scenarios with hundreds or thousands of locations. Simulated Annealing was also found to be an effective approach for UAV path planning, with a major benefit of avoiding getting trapped in local minima and being straightforward to implement. Like Genetic Algorithm, the performance of Simulated Annealing was also found to be sensitive to the algorithm hyperparameters selected. By comparison, Dynamic Programming guarantees optimality for any number of locations, but it was found to be less practical in terms of execution time for scenarios with more than about a couple dozen locations. On wildfire detection, image classification using deep learning with a convolutional neural network was explored. Transfer learning was found to be a useful technique to efficiently train deep learning models. Also, it was determined that GPU processing can increase training speed by an order of magnitude, which enables significantly faster development. For a validation test set of 500 images, there were only two false negatives and zero false positives. These results demonstrate that detecting wildfires in static cameras using machine learning is feasible and establish a baseline for using images captured by UAVs in flight for wildfire detection.

Wildfire management↗
NASA NTRSDate not supplied

Root Cause Analysis of the Data Refinement Process – Medical Conditions Capability Resource Tables

The medical system for spaceflight thus far has been designed to support missions in low earth orbit (LEO). Crew capabilities are limited and heavily dependent on the team of medical support staff at Mission Control Center (MCC) to guide diagnosis and management. However, missions to the Moon and Mars will suffer from several constraints that will make this ground support focused approach to care ineffective. In order to update and modify medical system design, NASA has relied on Probabilistic Risk Assessment (PRA) modeling to mitigate medical risk through trade space analysis. Specifically, capability resource tables (CRT’s) were developed to create a dataset of resources required to manage a list of accepted medical conditions significant in exploration spaceflight. With 120 conditions, this dataset contained hundreds of capabilities and thousands of resources with tens of thousands of cells of data. Initially these tables were built in excel for high throughput during development, but ultimately had to be transferred, managed, and modified into the Evidence Library database for modeling purposes. The process of collating and reviewing the Evidence Library revealed numerous errors in the dataset that had to be corrected through iterative changes. Several error types emerged during this process and can be broken into specific classifications defined as “input”, “transcription”, “structural”, “branching”, and “information”. In reviewing these error types through the root cause analysis (RCA) approach, we were able to identify the contributors to these errors which included single data review points, changing product end goals, limited software selection, time constraints and several others. By reviewing and evaluating the underlying causes we can provide possible system improvements that can be implemented for current and future data management in PRA model inputs.

A. Anderson↗
NASA NTRS2005

A Review of Diagnostic Techniques for ISHM Applications

System diagnosis is an integral part of any Integrated System Health Management application. Diagnostic applications make use of system information from the design phase, such as safety and mission assurance analysis, failure modes and effects analysis, hazards analysis, functional models, fault propagation models, and testability analysis. In modern process control and equipment monitoring systems, topological and analytic , models of the nominal system, derived from design documents, are also employed for fault isolation and identification. Depending on the complexity of the monitored signals from the physical system, diagnostic applications may involve straightforward trending and feature extraction techniques to retrieve the parameters of importance from the sensor streams. They also may involve very complex analysis routines, such as signal processing, learning or classification methods to derive the parameters of importance to diagnosis. The process that is used to diagnose anomalous conditions from monitored system signals varies widely across the different approaches to system diagnosis. Rule-based expert systems, case-based reasoning systems, model-based reasoning systems, learning systems, and probabilistic reasoning systems are examples of the many diverse approaches ta diagnostic reasoning. Many engineering disciplines have specific approaches to modeling, monitoring and diagnosing anomalous conditions. Therefore, there is no "one-size-fits-all" approach to building diagnostic and health monitoring capabilities for a system. For instance, the conventional approaches to diagnosing failures in rotorcraft applications are very different from those used in communications systems. Further, online and offline automated diagnostic applications are integrated into an operations framework with flight crews, flight controllers and maintenance teams. While the emphasis of this paper is automation of health management functions, striking the correct balance between automated and human-performed tasks is a vital concern.

Patterson-Hine, Ann↗
NASA NTRS2018

Automated Pneumothorax Diagnosis using Deep Neural Networks

Thoracic ultrasound can provide information leading to rapid diagnosis of pneumothorax with improved accuracy over the standard physical examination and with higher sensitivity than anteroposterior chest radiography. However, the clinical We have Furthermore, remote environments, such as the battlefield or deep-space exploration, may lack expertise for diagnosing developed an automated image interpretation pipeline for the analysis of thoracic ultrasound data and the classification of pneumothorax events to provide decision support in such situations. Our pipeline consists of image preprocessing, data augmentation, and deep learning architectures for medical diagnosis. In this work, we demonstrate that robust, accurate interpretation of chest images and video can be achieved using deep neural networks. A number of novel image processing techniques were employed to achieve this result. Affine transformations were applied for data augmentation. Hyperparameters were optimized for learning rate, dropout regularization, batch size, and epoch iteration by a sequential model-based Bayesian approach. In addition, we utilized pretrained architecturesinterpretation of a patient medical image is highly operator dependent. certain pathologies., applying transfer learning and fine-tuning techniques to fully connected layers. Our pipeline yielded binary classification validation accuracies of 98.3% for M-mode images and 99.8% with B-mode video frames.

US Army collaboration↗
NASA NTRS1974

Ultraviolet photometry from the orbiting astronomical observatory. XVI - The stellar Lyman-alpha absorption line

The stellar Lyman-alpha line at 1216 A was observed in 29 lightly reddened stars of spectral type B2.5 to B9 by a far-UV spectrophotometer on OAO-2. The equivalent widths obtained range from 15 A at type B2.5 to 65 A at type B8; in the late-B stars, the L-alpha line removes 2 to 3% of the total stellar flux. In this sampling, the strength of the L-alpha line correlates well with measures of the Balmer discontinuity and Balmer line strengths; luminosity classification does not seem to affect the line strength. The observed line widths also agree with the predictions of Mihala's grid of non-LTE model atmospheres. In some cases, the L-alpha line influences the interstellar column densities reported in the interstellar OAO-2 L-alpha survey. Hence, these data toward lightly reddened B2 and B1.5 stars should be regarded as upper limits only.

Savage, B. D.↗
NASA NTRS2005

Lunar and Planetary Science XXXVI, Part 19

The topics include: 1) The abundances of Iron-60 in Pyroxene Chondrules from Unequilibrated Ordinary Chondrites; 2) LL-Ordinary Chondrite Impact on the Moon: Results from the 3.9 Ga Impact Melt at the Landing Site of Appolo 17; 3) Evaluation of Chemical Methods for Projectile Identification in Terrestrial and Lunar Impactites; 4) Impact Cratering Experiments in Microgravity Environment; 5) New Achondrites with High-Calcium Pyroxene and Its implication for Igneous Differentiation of Asteroids; 6) Climate History of the Polar Regions of Mars Deduced form Geologic Mapping Results; 7) The crater Production Function for Mars: A-2 Cumulative Power-Law Slope for Pristine Craters Greater than 5 km in Diameter Based on Crater Distribution for Northern Plains Materials; 8) High Resolution Al-26 Chronology: Resolved Time Interval Between Rim and Interior of a Highly Fractionated Compact Type a CAI from Efremovka; 9) Assessing Aqueous Alteration on Mars Using Global Distributions of K and Th; 10) FeNi Metal Grains in LaPaz Mare Basalt Meteorites and Appolo 12 Basalts; 11) Unique Properties of Lunar Soil for In Situ Resource Utilization on the Moon; 12) U-Pb Systematics of Phosphates in Nakhlites; 13) Measurements of Sound Speed in Granular Materials Simulated Regolith; 14) The Effects of Oxygen, Sulphur and Silicon on the Dihedral Angles Between Fe-rich Liquid Metal and Olivine, Ringwoodite and Silicate Perovskite: Implications for Planetary Core Formation; 15) Seismic Shaking Removal of Craters 0.2-0.5 km in Diameter on Asteroid 433 Eros; 16) Focused Ion Beam Microscoopy of ALH84001 Carbonate Disks; 17) Simulating Micro-Gravity in the Laboratory; 18) Mars Atmospheric Sample Return Instrument Development; 19) Combined Remote LIBS and Raman Spectroscopy Measurements; 20) Unusual Radar Backscatter Properties Along the Northern Rim of Imbrium Basin; 21) The Mars Express/NASAS Project at JPL; 22) The Geology of the Viking 2 Lander Site Revisited; 23) An Impact Genesis for Loki Patera? 24) Mars Polar Cap Edges Tracked over 3 Full Mars Years; 25) Elemental Abundance in Presolar SiC: Comparing Grains Separated by Acid Residue and Gently Separation Procedures; 26) First Results from the Descent Imager/Spectral Radiometer (DISR) Experiment on the Huygens Entry Probe of Titan; 27) Minor Element Behavior of Pallasite Olivine: Understanding Pallasite Thermal History and Chronology; 28) Canonical Anorthite in a Grosnaja Forsterite-bearing CAI; 29) Experimental Evidence for Condensation of 'Astrophysical' Carbonate; 30) Distribution and Classification of Multiple Coronae on Venus; 31) Recognition of Rayed Craters on Mars in THEMIS Thermal Infrared Imagery: Implications for Martian Meteorite Source Regions; 32) Geochemical Modeling of Evaporites on Mars: Insight from Meridiani Planum; 33) Hadean Crustal Processes Revealed from Oxygen Isotopes and U-Th-Pb Depth Profiling of Pre-4.0 Ga Detrital Zircons from Western Australia; 34) On Modeling the Seepage of Water into the Martian Subsurface; 35) Martial Gullies and Groundwater: A Series of Unfortunate Exceptions; 36) Olivine and Carbonate Globules in ALH84001: A Terrestrial Analog, and Implications for Water on Mars; 37) A Reevaluation of Mass Movements Within the Valles Marineris Region of Mars Using MOLA and MOC Data; 38) Evidence of Hydrated 109P/Swift-Tuttle Meteoroids from Meteor Spectroscopy; 39) Cr-54 Anomalies in the Solar System: Their Extent and Origin; 40) Reevaluation of the Mn-53-Cr-53 Systematic in the Basaltic Achondrites; 41) Effective Liquid Metal-Silicate Mixing Upon Shock by Power-Law Droplet Size Scaling in Richtmyer-Meshkov Like Perturbations; 42) Post-Impact Deformation of Impact Craters: Towards a Better Understanding Through the Study of Mjolnir Crater; 43) Cutting Silica Aerogel for Particle Extraction; 44) Liquid Hydrocarbons on Titan's Surface? How Cassini ISS Observations Fit into the Story (So Far); and 45) Mesoscale Simulations of Polar Circulations: Late Spring to Late Summe

Source record↗
NASA NTRS2019

Automated Classification of Transient Contamination in Stationary Acoustic Data

An automated procedure for the classification of transient contamination of stationary acoustic data is proposed and analyzed. The procedure requires the assumption that the stationary acoustic data of interest can be modeled as a band-limited, Gaussian random process. It also requires that the transient contamination be of higher variance than the acoustic data of interest. When these assumptions are satisfied, it is a blind separation procedure, aside from the initial input specifying how to subdivide the time series of interest. No a priori threshold criterion is required. Simulation results show that for a sufficient number of blocks, the method performs well, as long as the occasional false positive or false negative is acceptable. The effectiveness of the procedure is demonstrated with an application to experimental wind tunnel acoustic test data which are contaminated by hydrodynamic gusts.

Bahr, Christopher J.↗
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 NTRSDate not supplied

Aqueous alteration of Cb-type asteroid Ryugu

The samples from the asteroid Ryugu, which returned to Earth in the winter of 2020 [1], were stored under pure Nitrogen conditions at the Extraterrestrial Materials Curation Center of ISAS. After basic description [2-3], they were distributed to six initial analysis teams in Japan in June 2021. The initial analysis "Stone" team, which was responsible for the analysis of coarse particles (>1 mm in size), consisted of about 150 scientists from Japan and abroad, and conducted a series of analyses We analyzed sixteen coarse Ryugu particles 1 ~ 8 mm in size: six from the 1st touch-down site and ten from the 2nd touch-down site. Individual coarse samples were cut by Xe-FIB or wire-saw to expose particular objects or textures to be exposed on the surface based on 3D structure and element distribution obtained by synchrotron X-ray CT analysis and XRF analysis, respectively. FE-SEM/EDS and FE-EPMA/WDS analysis were made on all polished sections. TEM observation of many FIB sections was made to see microstructures and to compare with carbonaceous chondrites. Many other analyses were carried out to characterize mineralogical and compositional properties and summary of the analysis procedures is described in [4]. X-ray CT analysis showed that all sixteen Ryugu particles are composed of fine-grained material, with no chondrules and CAIs larger than 100 μm in size. FE-EPMA observation showed that Ryugu samples are breccias, consisting of many small rock fragments of different compositions, lithologies, and histories. The most common lithology includes Mg-rich saponite and serpentine, dolomite, magnesite, hydroxyapatite, pyrrhotite, and magnetite as main constituents. The mineralogy of this major lithology supports the classification of Ryugu samples as CI chondrites, which experienced extensive aqueous alteration in Ryugu’s parent asteroid. Based on the obtained mineralogical properties, we performed chemical modeling of aqueous alteration of Ryugu’s parent asteroid [5]. Numerical simulations [6-8] to reproduce thermal history and impact-induced destruction of the Ryugu’s parent asteroid were also carried out using mineralogical and physical properties of the Ryugu samples. In the talk, I would like to explain the conditions of aqueous alteration occurred in the parent asteroid of Ryugu in order to show how the water in the asteroid changed the mineralogy and chemistry of the nebular dust.

T. Nakamura↗
NASA NTRS2001

AVIRIS Land-Surface Mapping in Support of the Boreal Ecosystem-Atmosphere Study (BOREAS)

A key scientific objective of the original Boreal Ecosystem-Atmospheric Study (BOREAS) field campaign (1993-1996) was to obtain the baseline data required for modeling and predicting fluxes of energy, mass, and trace gases in the boreal forest biome. These data sets are necessary to determine the sensitivity of the boreal forest biome to potential climatic changes and potential biophysical feedbacks on climate. A considerable volume of remotely-sensed and supporting field data were acquired by numerous researchers to meet this objective. By design, remote sensing and modeling were considered critical components for scaling efforts, extending point measurements from flux towers and field sites over larger spatial and longer temporal scales. A major focus of the BOREAS follow-on program is concerned with integrating the diverse remotely sensed and ground-based data sets to address specific questions such as carbon dynamics at local to regional scales. The Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) has the potential of contributing to BOREAS through: (1) accurate retrieved apparent surface reflectance; (2) improved landcover classification; and (3) direct assessment of biochemical/biophysical information such as canopy liquid water and chlorophyll concentration through pigment fits. In this paper, we present initial products for major flux tower sites including: (1) surface reflectance of dominant cover types; (2) a land-cover classification developed using spectral mixture analysis (SMA) and Multiple Endmember Spectral Mixture Analysis (MESMA); and (3) liquid water maps. Our goal is to compare these land-cover maps to existing maps and to incorporate AVIRIS image products into models of photosynthetic flux.

Roberts, Dar A.↗
NASA NTRS2024

XLSSC 122 Caught in the Act of Growing Up: Spatially Resolved SZ Observations of a z=1.98 Galaxy Cluster

Context. How protoclusters evolved from sparse galaxy overdensities to mature galaxy clusters is still not well understood. In this context, detecting and characterizing the hot intracluster medium (ICM) at high redshifts (z ∼ 2) is key to understanding how the continuous accretion from the filamentary large-scale structure and the mergers along it impact the first phases of cluster formation. Aims. We study the dynamical state and morphology of the z = 1:98 galaxy cluster XLSSC 122 with high-resolution observations (≈5″) of the ICM through the Sunyaev–Zeldovich (SZ) effect. XLSSC 122 is the highest redshift optically confirmed galaxy cluster found in an unbiased, widefield survey. Methods. Via Bayesian forward modeling, we mapped the ICM on scales from the virial radius down to the core of the cluster. To constrain such a broad range of spatial scales, we employed a new technique that jointly forward-models parametric descriptions of the pressure distribution to interferometric ACA and ALMA observations and multiband imaging data from ACT. Results. We detect the SZ effect with 11σ significance in the ALMA+ACA observations and find a flattened inner pressure profile that is consistent with a noncool core classification with a significance of ≥3σ. In contrast to the previous works, we find better agreement between the SZ effect signal and the X-ray emission as well as the cluster member distribution. Further, XLSSC 122 exhibits an excess of SZ flux in the south of the cluster where no X-ray emission is detected. By reconstructing the interferometric observations and modeling in the uv-plane, we obtain a tentative detection of an infalling group or filamentary-like structure in the southeast that is believed to boost and heat up the ICM while the density of the gas is still low. In addition to characterizing the dynamical state of the cluster, we provide an improved SZ mass estimate M500,c = 1.66−0.20+0.23 × 1014 M⊙ . Conclusions. Altogether, the observations indicate that we see XLSSC 122 in a dynamic phase of cluster formation while a large reservoir of gas is already thermalized.

large-scale structure of Universe↗
NASA NTRS1972

Microwave emission characteristics of sea ice

A general classification is presented for sea ice brightness temperatures with categories of high and low emission, corresponding to young and weathered sea ice, respectively. A sea ice emission model was developed which allows variations of ice salinity and temperature in directions perpendicular to the ice surface.

Edgerton, A. T.↗
NASA NTRS1984

Atmospheric effects on remote sensing of surface reflectance

This paper reviews the atmospheric effects on remote sensing of surface reflectance. The scattering and absorption of sunlight by atmospheric molecules and aerosols affects the quality of images of the surface remotely sensed from satellites and aircrafts. The concentration and characteristics of the atmospheric aerosols vary from place to place and vary with time. The effect of atmospheric aerosols on the upward radiance depends on their optical thickness, scattering phase function and absorption. These parameters result from the aerosol concentration, composition, and the relative humidity. For high resolution images the aerosol scale height is also of importance. The radiative transfer theory that predicts the atmospheric radiances for a given surface and atmosphere is a well established theory for the case of uniform surfaces (or low resolution data). Some radiative transfer models exist for nonuniform surfaces and others are being developed. Recent field experiment and laboratory simulation data confirm the need for these models and can be used for their testing. It is shown that the atmospheric effect reduces the apparent resolution of satellite imagery and causes errors in the classification of surface fields. Suggestions for correction procedures are given. Such corrections can be based on ground observations, on satellite radiances above dark areas, or on climatologic information, depending on the accuracy of the corrections needed. The chosen correction algorithm depends also on the image resolution and the specific remote sensing application.

Kaufman, Y. J.↗
NASA NTRS1987

Inlets, ducts and nozzles

The internal fluid mechanics research program in inlets, ducts, and nozzles is described. The program consists of a balanced effort between the development of computational tools and the conduct of experimental research. The experiments are designed to better understand the fluid flow physics, to develop new or improved flow models, and to provide benchmark quality data sets for validation of the computational methods. The inlet, duct, and nozzle research program is described according to three major classifications of flow phenomena: highly three-dimensional flow fields; shock-boundary layer interactions; and shear layer control. Specific examples of current and future elements of the research program are described for each of these phenomena. In particular, the highly three-dimensional flow field phenomena is highlighted by describing the computational and experiemental research program in transition ducts having a round-to-rectangular area variation. In the case of shock-boundary layer interactions, the specific details of research for normal shock-boundary layer interactions are described. For shear layer control research in vortex generators and the use of aerodynamic excitation for enhancement of the jet mixing process are described. Future research in inlets, ducts, and nozzles will include more emphasis on three-dimensional full Navier-Stokes methods and corresponding experiments designed to concentrate on the appropriate three-dimensional fluid flow physics.

Abbott, John M.↗
NASA NTRS1989

HIRIS performance study

The remote sensing system simulation is used to study a proposed sensor concept. An overview of the instrument and its parameters is presented, along with the model of the instrument as implemented in the simulation. Signal-to-noise levels of the instrument under a variety of system configurations are presented and discussed. Classification performance under these varying configurations is also shown, along with relationships between signal-to-noise ratios, feature selection, and classification performance.

Kerekes, John P.↗
NASA NTRS1990

Inlets, ducts, and nozzles

The internal fluid mechanics research program in inlets, ducts, and nozzles consists of a balanced effort between the development of computational tools (both parabolized Navier-Stokes and full Navier-Stokes) and the conduct of experimental research. The experiments are designed to better understand the fluid flow physics, to develop new or improved flow models, and to provide benchmark quality data sets for validation of the computational methods. The inlet, duct, and nozzle research program is described according to three major classifications of flow phenomena: (1) highly 3-D flow fields; (2) shock-boundary-layer interactions; and (3) shear layer control. Specific examples of current and future elements of the research program are described for each of these phenomenon. In particular, the highly 3-D flow field phenomenon is highlighted by describing the computational and experimental research program in transition ducts having a round-to-rectangular area variation. In the case of shock-boundary-layer interactions, the specific details of research for normal shock-boundary-layer interactions are described. For shear layer control, research in vortex generators and the use of aerodynamic excitation for enhancement of the jet mixing process are described.

Abbott, John M.↗