Search NASA⌕ Search

SEARCH · Search NASA

Results for “map segmentation”

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 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
NASA NTRS2010

Technology Advancement of the Visible Nulling Coronagraph

The critical high contrast imaging technology for the Extrasolar Planetary Imaging Coronagraph (EPIC) mission concept is the visible nulling coronagraph (VNC). EPIC would be capable of imaging jovian planets, dust/debris disks, and potentially super-Earths and contribute to answering how bright the debris disks are for candidate stars. The contrast requirement for EPIC is 10(exp 9) contrast at 125 milli-arseconds inner working angle. To advance the VNC technology NASA/Goddard Space Flight Center, in collaboration with Lockheed-Martin, previously developed a vacuum VNC testbed, and achieved narrowband and broadband suppression of the core of the Airy disk. Recently our group was awarded a NASA Technology Development for Exoplanet Missions to achieve two milestones: (i) 10(exp 8) contrast in narrowband light, and, (ii) 10(ecp 9) contrast in broader band light; one milestone per year, and both at 2 Lambda/D inner working angle. These will be achieved with our 2nd generation testbed known as the visible nulling testbed (VNT). It contains a MEMS based hex-packed segmented deformable mirror known as the multiple mirror array (MMA) and coherent fiber bundle, i.e. a spatial filter array (SFA). The MMA is in one interferometric arm and works to set the wavefront differences between the arms to zero. Each of the MMA segments is optically mapped to a single mode fiber of the SFA, and the SFA passively cleans the sub-aperture wavefront error leaving only piston, tip and tilt error to be controlled. The piston degree of freedom on each segment is used to correct the wavefront errors, while the tip/tilt is used to simultaneously correct the amplitude errors. Thus the VNT controls both amplitude and wavefront errors with a single MMA in closed-loop in a vacuum tank at approx.20 Hz. Herein we will discuss our ongoing progress with the VNT.

Lyon, Richard G.↗
DOE OSTI2024

Automated Assembly Progress Monitoring in Modular Construction Factories Using Computer Vision-Based Instance Segmentation

Modular construction has recently gained interest as a transformative construction method. In this method, a large portion of the construction is performed inside factories, where processes are fast-paced and interdependent; therefore, any deviation from the schedule can delay the production. Such deviations are frequent in modular factories due to the labor-intensive nature of the tasks. This propagation of delays can be mitigated by continuously monitoring each process; however, current manual monitoring methods are laborious, and recently proposed contact sensor-based methods are intrusive to the work. In addition, recent computer vision-based monitoring methods inside factories are limited to detection algorithms that fail to provide the pixel-level accuracy required for assembly progress monitoring in highly occluded factory scenes, and they require a large number of manual annotations. Therefore, this paper proposes a method to monitor the installation of subassemblies in modular construction factories using mask R-CNN instance segmentation and improves the data efficiency of the model using a copy-paste augmentation method. This method was validated on the CCTV videos captured from a modular construction factory in the US, resulting in a 9% mAP improvement in segmentation.

computer vision↗
DOE OSTI2021

Quantifying the unknown impact of segmentation uncertainty on image-based simulations

Abstract Image-based simulation, the use of 3D images to calculate physical quantities, relies on image segmentation for geometry creation. However, this process introduces image segmentation uncertainty because different segmentation tools (both manual and machine-learning-based) will each produce a unique and valid segmentation. First, we demonstrate that these variations propagate into the physics simulations, compromising the resulting physics quantities. Second, we propose a general framework for rapidly quantifying segmentation uncertainty. Through the creation and sampling of segmentation uncertainty probability maps, we systematically and objectively create uncertainty distributions of the physics quantities. We show that physics quantity uncertainty distributions can follow a Normal distribution, but, in more complicated physics simulations, the resulting uncertainty distribution can be surprisingly nontrivial. We establish that bounding segmentation uncertainty can fail in these nontrivial situations. While our work does not eliminate segmentation uncertainty, it improves simulation credibility by making visible the previously unrecognized segmentation uncertainty plaguing image-based simulation.

42 ENGINEERING↗
DOE OSTI2022

Testing Hidden Assumptions of Representativeness in Reach-Scale Studies of Hyporheic Exchange

Field studies of hyporheic exchange in mountain systems are often conducted using short study reaches and a limited number of observations. It is common practice to assume these study reaches represent hyporheic exchange at larger scales or different sites and to infer general relationships among potential causal mechanisms from the limited number of observations. However, these assumptions of representativeness are rarely tested. In this study, we develop numerical models from four segments of mountain streams in different geomorphologic settings and extract shorter reaches to test how representative exchange metrics are in shorter reaches compared to their reference segments. We also map the locations of the representative reaches to determine if a pattern exists based on location. Finally, we compare variance of these shorter within-site reaches to 29 additional reaches across the same basin to understand the impacts of inferring causal mechanisms, for example, the expectation that wide and narrow valley bottoms will yield different hyporheic exchange patterns. Our results show that the location and length strategy of the study reach must be considered before assuming an exchange metric to be representative of anything other than the exact segment studied. Further, it is necessary to quantify within and between site variations before making causal inferences based on observable characteristics, such as valley width or stream morphology. Our findings have implications for future field practices and how those practices are translated into models.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1982

Australia ground data collection 1981-82 crop year, volume 1

Under AgRISTARS management, ground data were collected at 20 agricultural sites within Australia during the crop year 1981-82. The data collection activity is summarized. Specifically, the following information is provided: discussion of data procedures, methods, and products; crop production results; photographs of the Australia agriculture scene, map sheets of segments, LANDSAT full frames, and aerial photographs of data collection areas; and summarizations of district agronomist reports.

Quinones, C. R.↗
NASA NTRS1988

Detecting and avoiding collisions between two robot arms in a common workspace

A novel approach to collision detection is presented and methods of avoiding collisions are discussed. Collisions are assumed to be between the wrists of two robots (which correspond to the upper three links of PUMA manipulators). A sphere model for the wrist is employed because it is rotationally invariant and computationally efficient. The collision detection algorithm involves obtaining the potential collision path segments along the straight line trajectories without considering the motion characteristics, and mapping the potential collision segment information into the time domain to obtain the space-time collisions.

Basta, Robert A.↗
NASA NTRS1993

The effect of scene content on speed, time, and distance perception

Helicopter flights performed at low-levels place high demands on pilots; they must simultaneously control the vehicle, avoid obstacles, and navigate. Therefore, pilots must correlate cues viewed in the external scene with information on map in order to maintain their geographical orientation. This is a particularly difficult task when helicopter pilots fly through visually unfamiliar terrain without highly detailed maps. As a result, pilots must often use estimates of elapsed time, distance traveled, and/or average speed in order to maintain a flight path indicated on a map during flight segments when these cues are absent. Therefore, the current study is concerned with the perception of speed, time, and distance, which we assume underlies the ability to orient oneself during these types of flight segments.

Awe, Cynthia A.↗
NASA NTRS2002

Correlations among the Optical Properties of Cirrus-Cloud Particles: Microphysical Interpretation

Cirrus measurements obtained with a ground-based polarization Raman lidar at 67.9 deg N in January 1997 reveal a strong positive correlation between the particle optical properties, specifically depolarization ratio delta(sub par) and extinction- to-backscatter (lidar) ratio S, for delta(sub par) less than approximately 40%, and an anti-correlation for delta(sub par) greater than approximately 40%. Over the length of the measurements the particle properties vary systematically. Initially, delta (sub par) approximately equals 60% and S approximately equals 10sr are observed. Then, with decreasing delta(sub par), S first increases to approximately 27sr (delta(sub par) approximately equals 40%) before decreasing to values around 10sr again (delta(sub par) approximately equals 20%). The analysis of lidar humidity and radiosonde temperature data shows that the measured optical properties stem from scattering by dry solid ice particles, while scattering by supercooled droplets, or by wetted or subliming ice particles can be excluded. For the microphysical interpretation of the lidar measurements, ray-tracing computations of particle scattering properties have been used. The comparison with the theoretical data suggests that the observed cirrus data can be interpreted in terms of size, shape, and, under the assumption that the lidar measurements of consecutive cloud segments can be mapped on the temporal development of a single cloud parcel moving along its trajectory, growth of the cirrus particles: Near the cloud top in the early stage of cirrus development, light scattering by nearly isometric particles that have the optical characteristics of hexagonal columns (short, column-like particles) is dominant. Over time the ice particles grow, and as the cloud base height extends to lower altitudes characterized by warmer temperatures they become morphologically diverse. For large S and depolarization values of approximately 40%, the scattering contributions of column- and plate-like particles are roughly the same. In the lower ranges of the cirrus clouds, light scattering is predominantly by plate-like ice particles. This interpretation assumes random orientation of the cirrus particles. Simulations with a simple model suggest, however, that the positive correlation between S and delta(sub par) which is observed for depolarization ratios less than 40% mainly at low cloud altitudes, can be alternatively explained by horizontal alignment of a fraction of the cirrus particle population.

Reichardt, J.↗
NASA NTRS2013

Metric Learning to Enhance Hyperspectral Image Segmentation

Unsupervised hyperspectral image segmentation can reveal spatial trends that show the physical structure of the scene to an analyst. They highlight borders and reveal areas of homogeneity and change. Segmentations are independently helpful for object recognition, and assist with automated production of symbolic maps. Additionally, a good segmentation can dramatically reduce the number of effective spectra in an image, enabling analyses that would otherwise be computationally prohibitive. Specifically, using an over-segmentation of the image instead of individual pixels can reduce noise and potentially improve the results of statistical post-analysis. In this innovation, a metric learning approach is presented to improve the performance of unsupervised hyperspectral image segmentation. The prototype demonstrations attempt a superpixel segmentation in which the image is conservatively over-segmented; that is, the single surface features may be split into multiple segments, but each individual segment, or superpixel, is ensured to have homogenous mineralogy.

Thompson, David R.↗
DOE OSTI2019

Segmentation and Comparative Modeling in an 8.6-Å Cryo-EM Map of the Singapore Grouper Iridovirus

SGIV, or Singapore grouper iridovirus, is a large double-stranded DNA virus, reaching a diameter of 220 nm and packaging a genome of 140 kb. We present a 3D cryoelectron microscopy (cryo-EM) icosahedral reconstruction of SGIV determined at 8.6-Å resolution. It reveals several layers including a T = 247 icosahedral outer coat, anchor proteins, a lipid bilayer, and the encapsidated DNA. A new segmentation tool, iSeg, was applied to extract these layers from the reconstructed map. The outer coat was further segmented into major and minor capsid proteins. None of the proteins extracted by segmentation have known atomic structures. Here, we generated models for the major coat protein using three comparative modeling tools, and evaluated each model using the cryo-EM map. Our analysis reveals a new architecture in the Iridoviridae family of viruses. It shares similarities with others in the same family, e.g., Chilo iridescent virus, but also shows new features of the major and minor capsid proteins.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1981

Wheat cultivation: Identification and estimation of areas using LANDSAT data

The feasibility of using automatically processed multispectral data obtained from LANDSAT to identify wheat and estimate the areas planted with this grain was investigated. Three 20 km by 40 km segments in a wheat growing region of Rio Grande do Sul were aerially photographed using type 2443 Aerochrome film. Three maps corresponding to each segment were obtained from the analysis of the photographs which identified wheat, barley, fallow land, prepared soil, forests, and reforested land. Using basic information about the fields and maps made from the photographed areas, an automatic classification of wheat was made using MSS data from two different periods: July to September and July to October 1979. Results show that orbital data is not only useful in characterizing the growth of wheat, but also provides information of the intensity and extent of adverse climate which affects cultivation. The temporal and spatial characteristics of LANDSAR data are also demonstrated.

Dejesusparada, N.↗
NASA NTRS1974

Geologic and mineral and water resources investigations in western Colorado, using Skylab EREP data

The author has identified the following significant results. Discovery of three major north-trending, throughgoing faults in the Front Range, previously mapped only as isolated segments, demonstrates the utility of space photography and may lead to reinterpretation of the Front Range tectonic style. Faulting and alteration appear to be the most useful indicators of mineralization in central Colorado. These phenomena appear on Skylab photography as tonal lineaments and color anomalies. Twenty-three lineaments have been mapped in the San Juan Mountains, the longest of which is 156 km long. Twelve lineaments intersect or are tangent to calderas. Intrusive domes are aligned along lineaments, but calderas appear to occur at the intersections of major lineaments. Lineaments can be recognized on some EREP passes but not on other passes over the same area. The difference is attributed to solar elevation effects. Bedding attitudes can be photogeologically estimated down to surprisingly low dips, on the order of + or - 1-2 deg, and attitudes can be subdivided easily into quantitative groups. The primary application of Skylab photography to geologic mapping in montane areas is clearly limited to regional mapping at scales smaller than 1:24,000.

Lee, K.↗
DOE OSTI2024

Characterizing Mesoscale Cellular Convection in Marine Cold Air Outbreaks With a Machine Learning Approach

Abstract During marine cold‐air outbreaks (MCAOs), when cold polar air moves over warmer ocean, a well‐recognized cloud pattern develops, with open or closed mesoscale cellular convection (MCC) at larger fetch over open water. The Cold‐Air Outbreaks in the Marine Boundary Layer Experiment provided a comprehensive set of ground‐based in situ and remote sensing observations of MCAOs at a coastal location in northern Norway. MCAO periods that unambiguously exhibit open or closed MCC are determined. Individual cells observed with a profiling Ka‐band radar are identified using a watershed segmentation method. Using self‐organizing maps (SOMs), these cells are then objectively classified based on the variability in their vertical structure. The SOM nodes contain some information about the location of the cell transect relative to the center of the MCC. This adds classification noise, requiring numerous cell transects to isolate cell dynamical information. The SOM‐based classification shows that comparatively intense convection occurs only in open MCC. This convection undergoes an apparent lifecycle. Developing cells are associated with stronger updrafts, large spectrum width, larger amounts of liquid water, lower surface precipitation rates, and lower cloud tops than mature and weakening cells. The weakening of these cells is associated with the development of precipitation‐induced cold pools. The SOM classification also reveals less intense convection, with a similar lifecycle. More stratiform vertical cloud structures with weak vertical motions are common during closed MCC periods and are separated into precipitating and non‐precipitating stratiform cores. Convection is observed only occasionally in the closed MCC environment.

Meteorology & Atmospheric Sciences↗
DOE OSTI2025

Magnetic mesh generation and field line reconstruction for scrape-off layer and divertor modeling in stellarators

The design of divertor targets and baffles for optimal heat and particle exhaust from magnetically confined fusion plasmas requires a combination of fast, low-fidelity models (such as EMC3-Lite (Feng 2022 Plasma Phys. Control. Fusion 64 125012)) for scoping studies and high-fidelity ones (such as EMC3-EIRENE (Feng et al 2014 Contrib. Plasma Phys. 54 426–31)) for verification. Both of those approaches benefit from a magnetic flux tube mesh for fast interpolation and mapping of field line segments (Feng et al 2005 Phys. Plasmas 12 052505). A new automated mesh generator for unstructured quadrilateral flux tubes with adaptive refinement is presented and integrated into FLARE (Frerichs 2024 Nucl. Fusion 64 106034). For HSX with an extended first wall, it is found that several layers of flux tubes can span the entire half field period before splitting is required. This is an advantage over the traditional setup of the EMC3-EIRENE mesh where careful construction of several sub- domains is required already for the much tighter present first wall. In particular, there is no longer the need to manually construct a suitable outer boundary for the mesh. The divide and conquer paradigm with unstructured mesh layout offers a powerful alternative for fast head load approximation that is suitable for integration into optimization workflows. Further examples for W7-X and CTH demonstrate the versatile application range.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Flexible allocation of energy storage in power grids

Methods and apparatus provide flexible allocation and regulation of scheduled energy transfers between energy storage devices (“batteries”) and a power grid. Piecewise mappings having at least one sloping segment enable gradual variations in scheduled energy transfers as cleared values of a medium of energy exchange deviate from predicted values of the medium of energy exchange. Thereby deviations from a battery's predicted energy transfer schedule can be reduced, and overall smoother operation of a power grid can be achieved. Two sloping linear segments can be separated by a dead band, a portion of the mapping in which the scheduled energy transfer amount is invariant. A dead zone can increase the likelihood of a battery meeting its predicted schedule.

Bhattarai, Bishnu P.↗
NASA NTRS2016

Persistence Mapping Using EUV Solar Imager Data

We describe a simple image processing technique that is useful for the visualization and depiction of gradually evolving or intermittent structures in solar physics extreme-ultraviolet imagery. The technique is an application of image segmentation, which we call "Persistence Mapping," to isolate extreme values in a data set, and is particularly useful for the problem of capturing phenomena that are evolving in both space and time. While integration or "time-lapse" imaging uses the full sample (of size N ), Persistence Mapping rejects (N - 1)/N of the data set and identifies the most relevant 1/N values using the following rule: if a pixel reaches an extreme value, it retains that value until that value is exceeded. The simplest examples isolate minima and maxima, but any quantile or statistic can be used. This paper demonstrates how the technique has been used to extract the dynamics in long-term evolution of comet tails, erupting material, and EUV dimming regions.

Thompson, B. J.↗
NASA NTRS2022

Bayesian Deep Learning for Segmentation for Autonomous Safe Planetary Landing

Hazard detection is critical for enabling autonomous landing on planetary surfaces. Current state-of-the-art methods leverage traditional computer vision approaches to automate the identification of safe terrain from input digital elevation models (DEMs). However, performance for these methods can degrade for input DEMs with increased sensor noise. In the last decade, deep learning techniques have been developed for various applications. Nevertheless, their applicability to safety-critical space missions has often been limited due to concerns regarding their outputs’ reliability. In response to these limitations, this paper proposes an application of the Bayesian deep learning segmentation method for hazard detection. The developed approach enables reliable, safe landing site detection by i) generating simultaneously a safety prediction map and its uncertainty map via Bayesian deep learning and semantic segmentation, and ii) using the uncertainty map to filter out the uncertain pixels in the prediction map so that the safe site identification is performed only based on the certain pixels (i.e., pixels for which the model is certain about its safety prediction). Experiments are presented with simulated data based on a Mars HiRISE digital terrain model by varying uncertainty threshold and noise levels to demonstrate the performance of the proposed approach.

Kento Tomita↗
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.↗