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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 37 records · Page 2

Individual Motorist Data - Ohio EV Ownership Trends

The individual motorist dataset contains data and analysis of consumer electric vehicle (EV) ownership trends in rural Appalachian Ohio in comparison with statewide trends. The data span four years, from Q1 2020 to Q2 2023 (partial). They are sourced from the Ohio Bureau of Motor Vehicles registration records and contain detail on drivetrain type (battery-electric vehicle [BEV] or plug-in hybrid electric vehicle [PHEV]); specific vehicle make and model; and registration location at county, city, and ZIP code levels of spatial resolution. Registration data are analyzed at the county level against such indicators as median income, poverty status, urban-rural status, and density of public charging infrastructure. In addition to tabular data, a GIS shapefile with many analysis fields joined is included.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Integrated System for Autonomous and Adaptive Caretaking (ISAAC): Phase 1 Low-Fidelity Demo

This presentation describes the ISAAC phase 1 low-fidelity demonstration results. The demonstration satisfied the milestone from the Gateway-ISAAC Memorandum of Understanding to "Demonstrate spatial and logical data registration between robotics and spacecraft". It integrated many new ISAAC components, including a spatially linked model, Astrobee multi-sensor mapping, and an integrated data interface. It advanced ISAAC key performance parameters related to mapping, in a lab setting. Areas for phase 1 forward work include: improve maturity toward the high-fidelity demo on the ISS; demonstrate mapping with more sensor modalities; expand initial anomaly detection implementation into a flexible framework with multiple detection algorithms for different tasks; begin open source software release process for releasable ISAAC components.

robotics↗

Data system considerations for remote sensing

The availability of data obtained with the aid of Landsat and other remote sensing satellites provides potentially the possibility to study problems on a global scale. Difficulties arise, however, in connection with the diversity of data formats, archival conditions, and the need for data registration. Systems are being planned to test the ability to reduce some of these difficulties. Details regarding the given situation are examined, taking into account global problems which are being considered. These problems are related to the global carbon dioxide cycle, and the biogeochemical cycle. Attention is given to pilot data systems, considerations for spatial data, data format considerations, data quality considerations, aspects of data base design, and system aids.

Billingsley, F. C.↗

A Stochastic Framework for Estimating Load Profiles at EV Fast Charging Stations

This paper formulates a methodology for estimating the average daily load profiles of EV fast charging stations over a planning horizon of five to ten years. The developed methodology uses historic vehicle registration data, state-level EV adoption targets, seasonal driving patterns, local demographics, competition, and traffic volume information to predict average station usage. Through Monte Carlo simulations, an average daily load profile is obtained for each month in the planning horizon, and prediction uncertainty is quantified. The proposed framework will facilitate the accurate estimation of energy and demand costs incurred by the charging station over the planning period, thereby informing return-on-investment calculations.

Biswas, Shuchismita↗

Study of software application of airborne laser doppler system for severe storms measurement

Significant considerations are described for performing a Severe Storms Measurement program in real time. Particular emphasis is placed on the sizing and timing requirements for a minicomputer-based system. Analyses of several factors which could impact the effectiveness of the system are presented. The analyses encompass the problems of data acquisition, data storage, data registration, correlation, and flow field computation, and error induced by aircraft motion, moment estimation, and pulse integration.

Alley, P. L.↗

Flame Characterization Using a Tunable Solid-State Laser with Direct UV Pumping

Tunable solid-state lasers with direct UV pumping, based on d-f transitions of rare earth ions incorporated in wide band-gap dielectric crystals, are reliable sources of laser radiation that are suitable for excitation of combustion-related free radicals. We have employed such a laser for analytical flame characterization utilizing Laser-Induced Fluorescence (LIF) techniques. LIF spectra of alkane-air flames (used for studying combustion processes under normal and microgravity conditions) excited in the region of the A-X (0,0) OH-absorption band have been recorded and found to be both temperature-sensitive and positionally-sensitive. In addition, also clearly noticeable was the sensitivity of the spectra to the specific wavelength used for data registration. The LiCAF:Ce laser shows good prospects for being able to cover the spectral region between 280 and 340 nm and therefore be used excitation of combustion-intermediates such as the hydroxyl OH, methoxy CH30 and methylthio CH3S radicals.

Kamal, Mohammed M.↗

Using WorldView-2 Imagery to Track Flooding in Thailand in a Multi-Asset Sensorweb

For the flooding seasons of 2011-2012 multiple space assets were used in a "sensorweb" to track major flooding in Thailand. Worldview-2 multispectral data was used in this effort and provided extremely high spatial resolution (2m / pixel) multispectral (8 bands at 0.45-1.05 micrometer spectra) data from which mostly automated workflows derived surface water extent and volumetric water information for use by a range of NGO and national authorities. We first describe how Worldview-2 and its data was integrated into the overall flood tracking sensorweb. We next describe the use of Support Vector Machine learning techniques that were used to derive surface water extent classifiers. Then we describe the fusion of surface water extent and digital elevation map (DEM) data to derive volumetric water calculations. Finally we discuss key future work such as speeding up the workflows and automating the data registration process (the only portion of the workflow requiring human input).

surface water extent↗

Automated Registration of Vector Data to Overhead Imagery

The availability of open source, remote sensing-derived vector data has increased exponentially in recent years. Unfortunately, these vector data are rarely made available with the corresponding source images from which objects were extracted. As such, these derived datasets are commonly used in combination with target images which differ from the source. Satellite viewing geometry can cause objects, extracted from a source image, to appear shifted when overlaid with a target image. Whether for purely cartographic purposes, reusability of preexisting training labels, or any spatial analysis where spatial correspondence between vector and image are required, the following paragraphs outline a method to address this challenge through the automated registration of vector data to overhead imagery, including existing literature regarding this challenge, followed by a case study of Sioux Falls, South Dakota.

McKee, Jacob↗

Towards an intercomparison of automated registration algorithms for multiple source remote sensing data

The first step in the integration of multiple data is registration, either relative image-to-image registration or absolute geo-registration, to a map or a fixed coordinate system. As the need for automating registration techniques is recognized, we feel that there is a need to survey all the registration methods which may be applicable to Earth and space science problems and to evaluate their performances on a large variety of existing remote sensing data as well as on simulated data of soon-to-be-flown instruments. In this paper we will describe: 1) the operational toolbox which we are developing and which will consist in some of the most important registration techniques; and 2) the quantitative intercomparison of the different methods, which will allow a user to select the desired registration technique based on this evaluation and the visualization of the registration results.

LeMoigne, Jacqueline↗

Digital registration of topographic data and satellite MSS data for augmented spectral analysis

Results are presented for a project directed to assess the type and extent of long-term natural and man-made changes in floodplain features of a definite area in the Mississipi River Valley. 1:6000 scale maps were prepared from remotely sensed film/filter photographic coverage of the area at different times to compare changes in such features as channels, backwaters, vegetation as well as changes in the amount, location, and conditions of dredge spoil. Changes observed by this sequential photo comparison include abandonment of agricultural land followed by orderly secondary plant succession, the filling-in of shallows by sediment and the orderly succession of plants from a hydric to a more mesic environment and changes in deltas of small tributary streams in response to agricultural practices occurring at their headwaters. Plant colonization is observed on some spoil, and secondary movement of spoil is revealed in other areas.

Anuta, P. E.↗

Oceanographic satellite remote sensing: Registration, rectification, and data integration requirements

The problem of data integration in oceanography is discussed. Recommendations are made for technique development and evaluation, understanding requirements, and packaging techniques for speed, efficiency and ease of use. The primary satellite sensors of interest to oceanography are summarized. It is concluded that imaging type sensors make image processing an important tool for oceanographic studies.

Nichols, D. A.↗

A survey of automated remote sensing for agriculture

The state-of-the-art of the technology available to make remote sensing crop production estimates is reviewed with reference to several past and present research projects. In particular, attention is given to Landsat data acquisition, registration and preprocessing, data transformation, data modeling, proportion estimation, and labeling. Development stage models and crop condition models are briefly characterized, and areas where further research is needed are identified.

Hall, F. G.↗

Calibration, navigation, and registration of MAMS data for FIFE

The International Satellite Land Surface Climatology Project (ISLSCP) was conducted to study the interaction of the atmosphere with the land surface and the research problems associated with the interpretation of satellite data over the Earth's land surface. The experimental objectives of the First ISLSCP Field Experiment (FIFE) were the simultaneous acquisition of satellite, atmospheric, and surface data and to use these data to understand the processes controlling energy/mass exchange at the surface. The experiment site is a 15 x 15 km area southeast of Manhattan, Kansas, intersected by Interstate 70 and Kansas highway 177. The Konza Prairie portion is 5 x 5 km and is a controlled experiment site consisting primarily of native tall grass prairie vegetation. The remainder of the site is grazing and farm land with trees along creek beds that are scattered over the area. Airborne multispectral imagery from the Multispectral Atmospheric Mapping Sensor (MAMS) was collected over this region on two days during Intensive Field Campaign-1 (1FC-1) to study the time and space variability of remotely-sensed geophysical parameters. These datasets consist of multiple overflights covering about a 60-min period during late morning on June 4, 1987 and shortly after dark on the following day. Image data from each overpass were calibrated and Earth located with respect to each other using aircraft inertial navigation system parameters and ground control points. These were the first MAMS flights made with 10-bit thermal data.

Jedlovec, G. J.↗

Software For Tie-Point Registration Of SAR Data

SAR-REG software package registers synthetic-aperture-radar (SAR) image data to common reference frame based on manual tie-pointing. Image data can be in binary, integer, floating-point, or AIRSAR compressed format. For example, with map of soil characteristics, vegetation map, digital elevation map, or SPOT multispectral image, as long as user can generate binary image to be used by tie-pointing routine and data are available in one of the previously mentioned formats. Written in FORTRAN 77.

Rignot, Eric↗

Tools and Methods for the Registration and Fusion of Remotely Sensed Data

Tools and methods for image registration were reviewed. Methods for the registration of remotely sensed data at NASA were discussed. Image fusion techniques were reviewed. Challenges in registration of remotely sensed data were discussed. Examples of image registration and image fusion were given.

Goshtasby, Arthur Ardeshir↗

Recent Advances in Registration, Integration and Fusion of Remotely Sensed Data: Redundant Representations and Frames

In recent years, sophisticated mathematical techniques have been successfully applied to the field of remote sensing to produce significant advances in applications such as registration, integration and fusion of remotely sensed data. Registration, integration and fusion of multiple source imagery are the most important issues when dealing with Earth Science remote sensing data where information from multiple sensors, exhibiting various resolutions, must be integrated. Issues ranging from different sensor geometries, different spectral responses, differing illumination conditions, different seasons, and various amounts of noise need to be dealt with when designing an image registration, integration or fusion method. This tutorial will first define the problems and challenges associated with these applications and then will review some mathematical techniques that have been successfully utilized to solve them. In particular, we will cover topics on geometric multiscale representations, redundant representations and fusion frames, graph operators, diffusion wavelets, as well as spatial-spectral and operator-based data fusion. All the algorithms will be illustrated using remotely sensed data, with an emphasis on current and operational instruments.

Fusion↗