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In this on-going work, we report our efforts of building App Store at OpenNEX, which aims to provide a data analytics software search engine to help researchers find reusable software components to facilitate the development of their own algorithms.
During and after flooding events, mapping the extent of floodwaters aids in the distribution of resources, recovery efforts, and damage assessment practices. Development of a land cover classification system focused on mapping inundation after major hurricane events using synthetic aperture radar (SAR) data could allow for the production of near-real-time inundation mapping, enabling government and emergency response entities to get a preliminary idea of a developing situation. Complimentary optical and SAR images from domestic and foreign entities are brought together through activations of the International Charter: Space and Major Disasters to support response efforts, from true-color, near-infrared, and thermal remote sensing data obtained by NASA, NOAA, and international satellites to the collection of high-resolution true color aerial photography by NOAA and the National Geodetic Survey. In response to Hurricane Florence of 2018, NASA JPL collected numerous swaths of quad-pol L-band SAR data with the Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) instrument observing the record-setting river stages across North and South Carolina. The resulting fully-polarized SAR images allow for mapping of inundation extent at a high spatial resolution with a unique advantage over optical imaging stemming from the sensor’s ability to penetrate cloud cover and dense vegetation. In this study, true-color NOAA aerial and commercial satellite imagery are used in conjunction with four UAVSAR data swaths centered on the Lumberton and Cape Fear River basins in southeastern North Carolina to develop a Random Forest classification model focused on mapping open water and floodwater otherwise obscured by vegetation or lingering cloud cover. Ancillary building footprint, transportation route, and population data will also be incorporated into the classification scheme to estimate the societal impacts of flooding based on the proximity of features to detected inundation. Preliminary results from the Hurricane Florence case study will be discussed in addition to the limitations of available validation data for assessment of the classifier’s accuracy.
Since its founding in 1992, Global Science & Technology, Inc. (GST), of Greenbelt, Maryland, has been developing technologies and providing services in support of NASA scientific research. GST specialties include scientific analysis, science data and information systems, data visualization, communications, networking and Web technologies, computer science, and software system engineering. As a longtime contractor to Goddard Space Flight Center s Earth Science Directorate, GST scientific, engineering, and information technology staff have extensive qualifications with the synthesis of satellite, in situ, and Earth science data for weather- and climate-related projects. GST s experience in this arena is end-to-end, from building satellite ground receiving systems and science data systems, to product generation and research and analysis.
Automated classification of remote sensing data is an integral tool for earth scientists, and deep learning has proven very successful at solving such problems. However, building deep learning models to process the data requires expert knowledge of machine learning. We introduce DELTA, a software toolkit to bridge this technical gap and make deep learning easily accessible to earth scientists. Visual feature engineering is a critical part of the machine learning lifecycle, and hence is a key area that will be automated by DELTA. Hand-engineered features can perform well, but require a cross functional team with expertise in both machine learning and the specific problem domain, which is costly in both researcher time and labor. The problem is more acute with multispectral satellite imagery, which requires considerable computational resources to process. In order to automate the feature learning process, a neural architecture search samples the space of asymmetric and symmetric autoencoders using evolutionary algorithms. Since denoising autoencoders have been shown to perform well for feature learning, the autoencoders are trained on various levels of noise and the features generated by the best performing autoencoders evaluated according to their performance on image classification tasks. The resulting features are demonstrated to be effective for Landsat-8 flood mapping, as well as benchmark datasets CIFAR10 and SVHN.
The graphical intelligence and assistance capabilities of a human-computer interface for the Test, Control, and Monitor System at Kennedy Space Center are explored. The report focuses on how a particular commercial off-the-shelf graphical software package, Data Views, can be used to produce tools that build widgets such as menus, text panels, graphs, icons, windows, and ultimately complete interfaces for monitoring data from an application; controlling an application by providing input data to it; and testing an application by both monitoring and controlling it. A complete set of tools for building interfaces is described in a manual for the TCMS toolkit. Simple tools create primitive widgets such as lines, rectangles and text strings. Intermediate level tools create pictographs from primitive widgets, and connect processes to either text strings or pictographs. Other tools create input objects; Data Views supports output objects directly, thus output objects are not considered. Finally, a set of utilities for executing, monitoring use, editing, and displaying the content of interfaces is included in the toolkit.
The Comprehensive Assessment of Models and Events using Library Tools (CAMEL) framework leverages existing Community Coordinated Modeling Center services: Run on Request post processing tools that generate model time series outputs and the new Community Coordinated Modeling Center Metadata Registry that describes simulation runs using Space Physics Archive Search and Extract metadata. The new CAMEL visualization tool compares the modeled time series with observational data and computes a suite of skill scores such as Prediction Efficiency, Root Mean Square Error, and Symmetric Signed Percentage Bias. Model data pairs used for skill calculations are obtained considering a user selected maximum difference between the time of observation and the nearest model output. The system renders available data for all locations and time periods selected using interactive visualizations that allow the user to zoom, pan, and pick data values along traces. Skill scores are reported for each selected event or aggregated over all events for all participating model runs. Separately, scores are reported for all locations (satellites or stations) and for each location individually. We are building on past experiences with model data comparisons of magnetosphere and ionosphere model outputs from GEM2008, GEMCEDAR Electrodynamics Thermosphere Ionosphere, and the SWPC Operational Space Weather Model challenges. The CAMEL visualization tool is demonstrated using three validation studies: (a) Wang Sheeley Arge heliosphere simulations compared against OMNI solar wind data, (b) ground magnetic perturbations from several magnetosphere and ionosphere electrodynamics models as observed by magnetometers, and (c) electron fluxes from several ring current simulations compared to Radiation Belt Storm Probes Helium Oxygen Proton Electron instrument measurements, integrated over different energy ranges.
This study presents initial results of a newly developed simulation environment intendedto explore and assess future Space Traffic Management (STM) scenarios. The number ofnew Resident Space Objects (RSOs) in near-Earth orbit is expected to increase significantlywith the expected future deployment of a number of large constellations. These futurescenarios involve the addition of many tens of thousands of new RSOs, making the analysisinto their impact on collision risk extend beyond what traditional data-mining of present-dayconjunction data can reliably predict. To address this, a robust simulation environment wasdeveloped that implements a full force-model for orbit propagation, and computes continuousall-on-all conjunction statistics for arbitrarily large catalog sizes and simulation timeframes.Collision avoidance and station-keeping maneuvers can be optionally implemented based onconfigurable user inputs including physical characteristics and spacecraft meta-data (e.g.,commercial/government, owner country, etc.). Constellation build-out and de-orbit scenarioswere also implemented and modeled based on real-data analysis. Validation of the simulationresults was a critical component of the simulation development, and was done using the currentcatalog with comparisons against both public and internal NASA data-sets. The comparisonsdemonstrate that, with the appropriate settings, representative levels of conjunction ratesand probabilities can be obtained, providing confidence that the simulation tool can generatemeaningful outcomes for test scenarios. As an initial demonstration of the tool’s capabilities,year-long simulations with station-keeping were conducted to examine conjunction historiesusing both the current object catalog (5800 active satellites) and a hypothetical 60,000 objectscenario involving five potential large constellations. Output metrics include the number ofconjunction events, estimates of collision consequence (fragmentation), delta-V maneuver costs,and the probability of at least one collision occurring. The results highlight the potential thatthe simulation tool has for incorporating and running performance comparisons between, e.g.,various sets of maneuver guidelines, industry norms, and definitions of risk, with the overallobjective of providing actionable data to STM policy makers. The presentation will providean overview of the simulation development and validation efforts, as well as a discussion ofobservations gathered from the initial simulations performed.
Climate change has brought renewed interest in the study of plant phenology - the timing of life history events. Data on shifting phenologies with warming have accumulated rapidly, yet research has been comparatively slow to explain the diversity of phenological responses observed across latitudes, growing seasons and species. Here, we outline recent efforts to synthesize perspectives on plant phenology across the fields of ecology, climate science and evolution. We highlight three major axes that vary among these disciplines: relative focus on abiotic versus biotic drivers of phenology, on plastic versus genetic drivers of intraspecific variation, and on cross-species versus autecological approaches. Recent interdisciplinary efforts, building on data covering diverse species and climate space, have found a greater role of temperature in controlling phenology at higher latitudes and for early-flowering species in temperate systems. These efforts have also made progress in understanding the tremendous diversity of responses across species by incorporating evolutionary relatedness, and linking phenological flexibility to invasions and plant performance. Future research with a focus on data collection in areas outside the temperate mid-latitudes and across species' ranges, alongside better integration of how risk and investment shape plant phenology, offers promise for further progress.
While devoid of an active magnetic dynamo field today, Mars possesses a remanent magnetic field that may reach several thousand nanoteslas locally. The exact origin and the events that have shaped the crustal magnetization remain largely enigmatic. Three magnetic field data sets from two spacecraft collected over 13 cumulative years have sampled the Martian magnetic field over a range of altitudes from 90 up to 6,000 km: (a) Mars Global Surveyor (MGS) magnetometer (1997–2006), (b) MGS Electron Reflectometer (1999–2006), and (c) Mars Atmosphere and Volatile EvolutioN (MAVEN) magnetometer (2014 to today). In this paper we combine these complementary data sets for the first time to build a new model of the Martian internal magnetic field. This new model improves upon previous ones in several aspects: comprehensive data coverage, refined data selection scheme, modified modeling scheme, discrete-to-continuous transformation of the model, and increased model resolution. The new model has a spatial resolution of ∼160 km at the surface, corresponding to spherical harmonic degree 134. It shows small scales and well-defined features, which can now be associated with geological signatures.
Project SIREN (Search, Intercept, Retrieve, Expulsion Nuclear) was created to identify and evaluate the technologies and operational strategies needed to rendezvous with and capture aerospace radioactive materials (e.g., a distressed or spent space reactor core) before such materials can reenter the terrestrial atmosphere and then to safely move these captured materials to an acceptable space destination for proper disposal. A major component of the current project SIREN effort is the development of an interactive technology model (including a computerized data base) that explores, in building-block fashion, the interaction of the technologies and procedures needed to successfully accomplish a SIREN mission. The SIREN model will include appropriate national and international technology elements--both contemporary and projected into the next century. To obtain maximum flexibility and use, the SIREN technology data base is being programmed for use on 286-class PC's. The major technical elements for a successful SIREN mission include: ground and space-based tracking, launch vehicles of needed payload capacity, telerobotic systems, sensors, capture technologies, and space transport and disposal. However, Project SIREN also will impose specialized requirements including the use of dextrous aerospace systems capable of properly functioning in intense radiation and thermal environments.
Seasat scatterometer data over the Arabian Sea are used to build wind-stress fields during July and August 1978. They are first compared with 3-day wind analyses from ship data along the Somali coast. Seasat scatterometer specifications of 2-m/s and 20-deg accuracy are fulfilled in almost all cases. The exceptions are for winds stronger than 14 m/s, which are underestimated by the scatterometer by 15 percent. Wind stress is derived from these wind data using a bulk formula with a drag coefficient depending on the wind intensity. A successive-correction objective analysis is used to build the wind-stress field over the Arabian Sea with 2 x 2-deg and 6-day resolution. The final wind-stress fields are not significantly dependent on the objective analysis because of the dense coverage of the scatterometer. The combination of scatterometer and coastal ship data gives the best coverage to resolve monsoon wind structures even close to the coast. The final wind stress fields show wind features consistent with other monthly mean wind stress field. However, a high variability is observed on the 6-day time scale.
Attention is given to an end-to-end Space Station Data System (SSDS) architecture which is based on internationally-recommended standards developed by the Consultative Committee for Space Data Systems (CCSDS). The proposed system uses simple modular building blocks that are recursively replicated and linked to construct essentially any desired data system configuration. The SSDS concept provides for a user-transparent data transport system which is entirely independent of the characteristics of the user data being transported, and in addition, has the flexibility to accommodate mission-induced changes in data traffic. SSDS physical elements include the following: (1) on-orbit local area networks, (2) space-to-ground, ground-to-space, and space-to-space data links, and (3) ground mission support facilities containing telemetry and telecommand data handling termini and preprocessing services.
An algorithm for progressive classification of data, analogous to progressive rendering of images, makes it possible to compromise between speed and accuracy. This algorithm uses support vector machines (SVMs) to classify data. An SVM is a machine learning algorithm that builds a mathematical model of the desired classification concept by identifying the critical data points, called support vectors. Coarse approximations to the concept require only a few support vectors, while precise, highly accurate models require far more support vectors. Once the model has been constructed, the SVM can be applied to new observations. The cost of classifying a new observation is proportional to the number of support vectors in the model. When computational resources are limited, an SVM of the appropriate complexity can be produced. However, if the constraints are not known when the model is constructed, or if they can change over time, a method for adaptively responding to the current resource constraints is required. This capability is particularly relevant for spacecraft (or any other real-time systems) that perform onboard data analysis. The new algorithm enables the fast, interactive application of an SVM classifier to a new set of data. The classification process achieved by this algorithm is characterized as progressive because a coarse approximation to the true classification is generated rapidly and thereafter iteratively refined. The algorithm uses two SVMs: (1) a fast, approximate one and (2) slow, highly accurate one. New data are initially classified by the fast SVM, producing a baseline approximate classification. For each classified data point, the algorithm calculates a confidence index that indicates the likelihood that it was classified correctly in the first pass. Next, the data points are sorted by their confidence indices and progressively reclassified by the slower, more accurate SVM, starting with the items most likely to be incorrectly classified. The user can halt this reclassification process at any point, thereby obtaining the best possible result for a given amount of computation time. Alternatively, the results can be displayed as they are generated, providing the user with real-time feedback about the current accuracy of classification.
Concepts of sonic-boom pressure loading of building structures and the associated responses are reviewed, and results of pertinent theoretical and experimental research programs are summarized. The significance of sonic-boom load time histories, including waveshape effects, are illustrated with the aid of simple structural elements such as beams and plates. Also included are discussions of the significance of such other phenomena as three-dimensional loading effects, air cavity coupling, multimodal responses, and structural nonlinearities. Measured deflection, acceleration, and strain data from laboratory models and full-scale building tests are summarized, and these data are compared, where possible, with predicted values. Damage complaint and claim experience due both to controlled and uncontrolled supersonic flights over communities are summarized with particular reference to residential, commercial, and historic buildings. Sonic-boom-induced building responses are compared with those from other impulsive loadings due to natural and cultural events and from laboratory simulation tests.
The importance of ensuring preservation and useful access to the unique science potential of past, present and future NASA solar and space physics (i.e. heliophysics) data has been recognized since the inception of NASA but remains challenging. In this talk, I will briefly review the history of this topic and and then discuss the present NASA model for heliophysics science data management, including key current resources for finding and using data projects like the Space Physics Data Facility. I will highlight expected future directions, building on working elements of the present program and exploiting new technology, to further improve the data environment, address existing issues and anticipate emerging challenges.
In this paper a new image processing technique for flash LIDAR data is presented as a potential tool to enable safe and precise spacecraft landings in future robotic or crewed lunar and planetary missions. Flash LIDARs can generate, in real-time, range data that can be interpreted as a 3-dimensional (3-D) image and transformed into a corresponding digital elevation map (DEM). The NASA Autonomous Landing and Hazard Avoidance (ALHAT) project is capitalizing on this new technology by developing, testing and analyzing flash LIDARs to detect hazardous terrain features such as craters, rocks, and slopes during the descent phase of spacecraft landings. Using a flash LIDAR for this application looks very promising, however through theoretical and simulation analysis the ALHAT team has determined that a single frame, or mosaic, of flash LIDAR data may not be sufficient to build a landing site DEM with acceptable spatial resolution, precision, size, or for a mosaic, in time, to meet current system requirements. One way to overcome this potential limitation is by enhancing the flash LIDAR output images. We propose a new super-resolution algorithm applicable to flash LIDAR range data that will create a DEM with sufficient accuracy, precision and size to meet current ALHAT requirements. The performance of our super-resolution algorithm is analyzed by processing data generated during a series of simulation runs by a high fidelity model of a flash LIDAR imaging a high resolution synthetic lunar elevation map. The flash LIDAR model is attached to a simulated spacecraft by a gimbal that points the LIDAR to a target landing site. For each simulation run, a sequence of flash LIDAR frames is recorded and processed as the spacecraft descends toward the landing site. Each run has a different trajectory profile with varying LIDAR look angles of the terrain. We process the output LIDAR frames using our SR algorithm and the results show that the achieved level of accuracy and precision of the SR generated landing site DEM is more than adequate for detecting hazardous terrain features and identifying safe areas.
The work implied in the NASA Applied Science Program requires a delicate balancing act for the those doing it. At the implementation level there are multiple tensions intrinsic to the program. For example each application of an existing product to a decision support process requires deep knowledge about the data and deep knowledge about the decision making process. It is highly probable no one person has this range of knowledge. Otherwise the decision making process would already be using the data. Therefore, a team is required. But building a team usually requires time, especially across agencies. Yet the program mandates efforts of relatively short duration. Further, those who know the data are scientists, which makes them essential to the program. But scientists are evaluated on their publication record. Anything which diverts a scientist from the research for his next publication is an anathema to him and potential death to their career. Trying to get another agency to use NASA data does not strike most scientists as material inherently suitable for publication. Also, NASA wishes to rapidly implement often substantial changes to another agency's process. For many reasons, such as budget and program constraints, speed is important. But the owner of a decision making process is tightly constrained, usually by law, regulation, organization and custom. Changes when made are slow, cautious, even hesitant, and always done according a process specific to the situation. To manage this work MSFC must balance these and other tensions. Some things we have relatively little control over, such as budget. These we try to handle by structural techniques. For example by insisting all of our people work on multiple projects simultaneously we inherently have diversification of funding for all of our people. In many cases we explicitly use some elements of tension to be productive. For example the need for the scientists to constantly publish is motivation to keep tasks short and to the immediate point. As another example, the DSS agency and it personnel are treated as integral to the effort at every step. Thus, their ways of doing things, their problems and their assets become part of the solution. MSFC also manages the Applied Science work' within a strategic framework.' First, the scientists are necessarily at the core of all of this work as well as all of the work within the larger' organization. We therefore strive to keep a roughly 50-50 balance between the work done on tasks funded directly by the research side of NASA's Science Mission Directorate and funding from the applied side. Done at both the organization level and the level of the individual, this keeps the scientist both happy and productive over the long term. We also try diligently to remove as much burden from the scientist as practical by employing people such as the authors and others to do tasks not requiring; scientific knowledge. We also have designed our effort to take full use of external partners. We actively seek and support, including fund, people from multiple organizations to join us as committed collaborators. In this we use today's money and today's problem to help us diversify and strengthen for tomorrow. MSFC also considers the Applied Science work holistically. Each element is viewed as a step in a larger process. At a management level we can chose to emphasize or encourage certain areas which service long term goals. Thus, if we think work in a particular area should be developed, we can start with the smaller, less costly elements and grow.
Under NASA’s Applied Sciences, the NASA DEVELOP Program is centered on dual capacity building. Projects bring together partners and participants to learn how Earth observation data can enhance environmental decision-making practices and inform action. During their time with DEVELOP, participants build technical skills around remotely sensed data and professional skills through interactions with team members, science advisors, and partners. To further refine the participant experience, DEVELOP explores which technical and professional skills individuals report improving during the 10-week program. An analysis of self-reported assessments from 2018 to 2021 included projects that were conducted in-person and virtually. Participants consistently indicated a strong improvement in technical and professional skills yet there was clear variation between in-person and virtual opportunities. When comparing between 2019 and 2021, self-reporting showed a higher percentage of participants reporting an increase in networking in the virtual environment and an increase in building professional relationships with project partners virtually. This analysis suggests that professional opportunities available in the virtual environment may in fact bolster early career scientists’ networking abilities. This presentation will identify DEVELOP’s approach to monitoring and evaluation, highlight interesting trends introduced by the pandemic, and lessons learned.