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All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Marin County Wildland Fires: Examining Fuel Load and Land Cover Change to Inform Fire Prevention and Suppression Decisions in Marin County, CA

Heightened occurrence of severe wildfires in the Western United States is increasing the need to better understand regions of high potential wild fire severity and develop methodologies for identifying the best locations for fuels reduction and active wildfire suppression, especially in populated regions such as Marin County, California. Marin County, located in the San Francisco Bay Area, has had significant development in the wildland-urban interface and periods of highly wildfire-prone conditions. The NASA DEVELOP team collaborated with Fire Foundry, a Marin-based fire service work force development program, to develop new models to assist with fire management. Using data from Sentinel-2A, Planet Scope, ECOSTRESS, a county-wide LiDAR mapping effort, Landsat 7 Enhanced Thematic Mapper (ETM+), and Landsat 8 Operational Land Imager (OLI), the team developed several input data layers to three models evaluating wild fire severity. One model performed a suitability analysis with weights based on scientific literature, another utilized machine learning based on past fires in Marin and neighboring Sonoma County to predict the difference normalized burn ratio, and the third inputted data layers into the Flam Map tool, which outputs risk categories. The team compared model outputs and, using the best-fit model, performed fuzzy logic analysis to identify specific locations where a fire break could be constructed to interrupt the progress of an active fire. These tools were proven useful and will assist partners in preparing for and managing an active wildfire event.

Suhani Dalal↗
NASA NTRS2021

DroughtCast: A Machine Learning Forecast of the United States Drought Monitor

Drought is one of the most ecologically and economically devastating natural phenomena affecting the United States, causing the U.S. economy billions of dollars in damage, and driving widespread degradation of ecosystem health. Many drought indices are implemented to monitor the current extent and status of drought so stakeholders such as farmers and local governments can appropriately respond. Methods toforecast drought conditions weeks to months in advance are less common but would provide a more effective early warning system to enhance drought response, mitigation, and adaptation planning. To resolve this issue, we introduce DroughtCast, a machine learning framework for forecasting the United States Drought Monitor (USDM). DroughtCast operates on the knowledge that recent anomalies in hydrology and meteorology drive future changes in drought conditions. We use simulated meteorology and satellite observed soil moisture as inputs into a recurrent neural network to accurately forecast the USDM between 1 and 12 weeks into the future. Our analysis shows that precipitation, soil moisture, and temperature are the most important input variables when forecasting future drought conditions. Additionally, a case study of the 2017 Northern Plains Flash Drought shows that DroughtCast was able to forecast a very extreme drought event up to 12 weeks before its onset. Given the favorable forecasting skill of the model, DroughtCast may provide a promising tool for land managers and local governments in preparing for and mitigating the effects of drought.

Machine Learning↗
NASA NTRS2016

Data Mining and Knowledge Discover - IBM Cognitive Alternatives for NASA KSC

Skillful tools in cognitive computing to transform industries have been found favorable and profitable for different Directorates at NASA KSC. In this study is shown how cognitive computing systems can be useful for NASA when computers are trained in the same way as humans are to gain knowledge over time. Increasing knowledge through senses, learning and a summation of events is how the applications created by the firm IBM empower the artificial intelligence in a cognitive computing system. NASA has explored and applied for the last decades the artificial intelligence approach specifically with cognitive computing in few projects adopting similar models proposed by IBM Watson. However, the usage of semantic technologies by the dedicated business unit developed by IBM leads these cognitive computing applications to outperform the functionality of the inner tools and present outstanding analysis to facilitate the decision making for managers and leads in a management information system.

Cognitive computing↗
NASA NTRS2013

Climate Informatics

The impacts of present and potential future climate change will be one of the most important scientific and societal challenges in the 21st century. Given observed changes in temperature, sea ice, and sea level, improving our understanding of the climate system is an international priority. This system is characterized by complex phenomena that are imperfectly observed and even more imperfectly simulated. But with an ever-growing supply of climate data from satellites and environmental sensors, the magnitude of data and climate model output is beginning to overwhelm the relatively simple tools currently used to analyze them. A computational approach will therefore be indispensable for these analysis challenges. This chapter introduces the fledgling research discipline climate informatics: collaborations between climate scientists and machine learning researchers in order to bridge this gap between data and understanding. We hope that the study of climate informatics will accelerate discovery in answering pressing questions in climate science.

Climate change↗
NASA NTRS2020

High Resolution Global Coupled Chemistry-Meteorology Simulations Using the NASA GEOS Composition Forecast System, GEOS-CF

We will give an overview of the NASA Global Earth Observing System Composition Forecast system (GEOS-CF), a high-resolution (0.25 degree) global composition model developed by the NASA Global Modeling and Assimilation Office (GMAO). This system combines the GEOS weather and aerosol model with the GEOS-Chem chemistry module (version 12) to provide a holistic view of atmospheric composition that captures a wide range of air pollutants such as ozone, nitrogen oxides, volatile organic compounds, and fine particulate matter. The spatial resolution of 0.25 degrees (approx. 25 km) is fine enough to resolve local features such as nighttime ozone titration previously resolved only by urban or regional models. Furthermore, since there are no boundary conditions for a global model, the GEOS-CF captures large-scale processes such as long-range transport of air pollutants from forest fires. Comparisons against surface observations highlight the model’s overall capability to reproduce the diurnal variability of air pollutants under a variety of meteorological conditions. In addition, we show how machine learning techniques can be used to correct for sub-grid variability, which further improves model estimates at a given surface observation site. The GEOS-CF system offers a new tool for scientists and the public health community alike and is being developed jointly with several government and non-profit partners. As an example, we will show the use of GEOS-CF during the Satellite Coastal and Oceanic Atmospheric Pollution Experiment (SCOAPE). The campaign, conducted in collaboration between NASA and the Bureau of Ocean Energy Management (BOEM), aims to investigate the response of onshore air quality to Outer Continental Shelf (OCS) oil and gas exploration, development and production. Detailed gas-phase chemistry, as provided by GEOS-CF, is critical to understand the formation of air pollution related to hydrocarbon emissions from offshore oil and gas activities. The accuracy of GEOS-CF can be further improved by incorporating detailed offshore emissions compiled by BOEM.

Knowland, K. Emma↗
NASA NTRSDate not supplied

Kaona: Deep Searching and Curating Data from Aviation Safety Reporting Systems

Context: Several works in the literature have examined how safety narrative databases can be leveraged to share lessons learned. However, less attention has been given to augmenting existing processes for mining these safety reporting system databases. Aim: In this work, we introduce Kaona: An interface that weaves machine learning in existing aviation safety database mining activities. Method: We provide a use case of search, curation and newsletter writing to showcase how Kaona features build on existing processes and on its own to enhance information retrieval, curation and synthesis of narratives. Results: We created two instances of Kaona internally for evaluation, one using publicly available NASA’s ASRS narratives and another using publicly available C3RS narratives. Data ranged from 1998 to 2024. Conclusion: Our tool provides a new way to explore safety narratives, serving to re-imagine how text databases can benefit of novel information retrieval mechanisms in the era of large language models.

ASRS↗
NASA NTRSDate not supplied

Kaona: Deep Searching and Curating Aviation Safety Reporting Systems

Context: Several works in the literature have examined how safety narrative databases can be leveraged to share lessons learned. However, less attention has been given in augmenting existing processes of safety reporting systems. Aim: In this work, we introduce Kaona: An interface that weaves machine learning in existing aviation safety reporting systems activities. Method: We provide a use case of search, curation and newsletter writing to showcase how Kaona features build on existing processes and on its own to enhance information retrieval, curation and synthesis of narratives. Results: We created two instances of Kaona internally for evaluation, one using all public NASA's ASRS narratives and another using all public C3RS narratives. Data ranged from 1998 to 2024. Conclusion: Our tool provides a new way to explore safety narratives, serving to re-imagine how text databases can benefit of novel information retrieval mechanisms in the era of large language models.

asrs↗
NASA NTRSDate not supplied

Kaona: Deep Searching and Curating Safety Reporting Systems

Context: Several works in the literature have examined how safety narrative databases can be leveraged to share lessons learned. However, less attention has been given in augmenting existing processes of safety reporting systems. Aim: In this work, we introduce Kaona: An interface that weaves machine learning in existing aviation safety reporting systems activities. Method: We provide a use case of search, curation and newsletter writing to showcase how Kaona features build on existing processes and on its own to enhance information retrieval, curation and synthesis of narratives. Results: We created two instances of Kaona internally for evaluation, one using all public NASA's ASRS narratives and another using all public C3RS narratives. Data ranged from 1998 to 2024. Conclusion: Our tool provides a new way to explore safety narratives, serving to re-imagine how text databases can benefit of novel information retrieval mechanisms in the era of large language models.

asrs↗
NASA NTRSDate not supplied

Machine Learning for NASA Advanced Information Systems

NASA's Advanced Information Systems Technology (AIST) Program is one of several Technology programs managed by the Earth Science Technology Office (ESTO) in the Earth Science Division (ESD). The AIST Program focuses on advanced information systems and novel computer science technologies that will be needed by NASA Earth Science in the next 5 to 10 years. The three main thrusts of the AIST Program deal with Novel Observing Strategies (NOS), Analytic Collaborative Frameworks (ACF) and Earth System Digital Twins (ESDT). For all these thrusts, Machine Learning (ML) is increasingly being used in multiple aspects of Earth science systems, e.g., for onboard autonomy and decision making, for the analysis of massive and diverse datasets as well as more recently for developing surrogate models that will represent one of the main components of future Digital Twins of the Earth. Particularly, ESDT technologies developed by the AIST Program will allow to develop integrated Earth Science frameworks that will mirror the Earth with state-of-the-art models (Earth system models and others), timely and relevant observations, and analytic tools. These information systems will be used for supporting near- and long-term science and policy decisions. ESDT frameworks will build on previously developed AIST capabilities and technologies to integrate interconnected models with continuous streams of observations, data analytics, data assimilation, simulations, advanced visualizations and the ability to conduct "what-if" scenarios. This talk will describe the three thrusts of the AIST Program with a special focus on Machine Learning and how it is being used at all steps of the Earth Science data lifecycle.

Mathematical and Computer Sciences (General)↗
NASA NTRSDate not supplied

Tracking the Hunga Tonga-Hunga Ha’apai Eruption Stratospheric Aerosol and Trace Gas Plumes Using Machine Learning

On January 15, 2022, the Hunga Tonga-Hunga Ha’apai (hereafter, Hunga Tonga) submarine volcano had an explosive eruption that thrusted ash, gases, and water vapor through the troposphere into the stratosphere and mesosphere. Previous studies manually tracked the aerosol and trace gas plumes over time across different positions in the southern hemisphere. Using data retrieved from low earth orbiting satellite instruments (e.g., OMPS, OMI, and CALIPSO), this research demonstrates how open-source machine learning (ML) models, like Meta’s Segment Anything Model (SAM), with prompt engineering can perform automatic plume tracking following the Hunga Tonga eruption. This extensible methodology, and modular data processing and modeling pipeline using NASA Earthdata and Openscapes, establishes a framework for systematically and rapidly studying extreme events, including volcanic eruptions and large-scale wildfires. By combining advanced machine learning techniques, such as SAM’s zero-shot learning, with large volumes of remote sensing data, this work demonstrates how AI and open science can accelerate research and generate actionable results. The tools and technologies presented here can help translate earth science to action from NASA’s current and future Earth observing satellite missions (e.g., the Atmosphere Observing System (AOS)), and assist researchers and stakeholders in understanding, mapping, and responding to natural disasters and extreme events in a changing world.

David M. Giles↗
NASA NTRSDate not supplied

Aero-Engines AI - A Machine-Learning App for Aircraft Engine Concepts Assessment

Effective deployment of machine-learning (ML) models could drive a high level of efficiency in aircraft engine conceptual design. Aero-Engines AI is a user-friendly app that has been created to deploy trained machine-learning (ML) models to assess aircraft engine concepts. It was created using tkinter, a GUI (graphical user interface) module that is built into the standard Python library. Employing tkinter greatly facilitates the sharing of ML application as an executable file which can be run on Windows machines (without the need to have Python or any library installed). The app gets user input for a turbofan design, preprocesses the input data, and deploys trained ML models to predict turbofan thrust specific fuel consumption (TSFC), engine weight, core size, and turbomachinery stage-counts. The ML predictive models were built by employing supervised deep-learning and K-nearest neighbor regression algorithms to study patterns in an existing open-source database of production and research turbofan engines. They were trained, cross-validated, and tested in Keras, an open-source neural networks API (application programming interface) written in Python, with TensorFlow (Google open-source artificial intelligence library) serving as the backend engine. The smooth deployment of these ML models using the app shows that Aero-Engines AI is an easy-touse and a time-saving tool for aircraft engine design-space exploration during the conceptual design stage. Current version of the app focuses on the performance prediction of conventional turbofans. However, the scope of the app can easily be expanded to include other engine types (such as turboshaft and hybrid-electric systems) after their ML models are developed. Overall, the use of a machine-learning app for aircraft engine concept assessment represents a promising area of development in aircraft engine conceptual design.

machine learning↗
NASA NTRS2022

Hybrid data-driven and model-informed online tool wear detection in milling machines

Precision machining tool wear is responsible for low product throughput and quality. Monitoring the tool wear online is vital to prevent degradation in machining quality. However, direct real-time tool wear measurement is not practical. This paper presents residual-based anomaly detection models, combining a hybrid model comprised of a physics-based model and a data-driven model (a decision tree or a neural network) to predict signals of interest (e.g., power or forces) under nominal conditions, followed by Page’s cumulative sum test for detecting tool wear on-line using the computer numerical control machine measurements. The most informative features are ranked using dynamic programming and its approximation variants from real-time measurements and machine settings, such as the width of cut, depth of cut, feed rate and spindle speed, that serve as inputs to the predictive models. The baseline nominal model is incrementally updated with experimental data via a gradient boosted adaptation model to generate the residuals that account for discrepancies between the actual machine data under normal conditions and the baseline nominal model predictions. The hybrid model is validated against 20 Mazak milling machine experimental tests and one Haas run-to-failure experiment. The proposed anomaly detector is applied to synthetic data from simulations of the physics-based model at different operating conditions, measurement noise levels, and tool wear levels, and the methods were able to achieve an overall 92% accuracy in data with 1% noise. The anomaly detection methods based on hybrid model reduced the false alarms of either the data-driven or physical-based models alone, and are found to be capable of good online detection of tool wear.

Online anomaly detection↗
NASA NTRSDate not supplied

Conducting Feasibility Studies in a Virtual World: Lessons Learned and Emerging Best Practices from the NASA DEVELOP Program

In response to new workplace realities, the NASA DEVELOP National Program pivoted from co-locating students, emerging professionals, and science advisors to bringing together virtual teams from across the United States. In its spring 2020 term, rapidly evolving circumstances required an ad-hoc roll-out of a virtual approach to complete the spring projects. Based on the experience from the spring term and a few weeks of planning, DEVELOP then conducted a fully virtual summer term with features such as 1) online collaboration tools, 2) virtual machines for analysis, and 3) streamed training offerings, including DEVELOP’s first ever program-wide Software Carpentry workshop. This full term of bringing together remote actors to select, build, and manage teams brought many challenges. Summer feedback has influenced planning for the fall 2020 term and process improvement is ongoing. This presentation will highlight lessons learned throughout this period of rapid change. Feedback from spring and summer terms and the Software Carpentry workshop will be summarized. Beyond participant impacts, there will also be discussion of effects on project results and partner experience. Final takeaways will focus on best practices that have been distilled for virtually-conducted feasibility studies.

NASA DEVELOP↗
NASA NTRSDate not supplied

Improving Sim-to-Real Transfer in Vision-Based Robot Navigation Via Instance-Level GAN-Based Data Augmentation

Achieving robust vision-based robotic tasks requires large amounts of data, which are often difficult to obtain in real-world scenarios. Simulators and synthetic data offer a cost-effective alternative, but the visual gap between simulation and reality hinders the performance of models when deployed in real-world environments. In this paper, we present a data augmentation pipeline that integrates a foundation model (Segment Anything Model) with an unsupervised image-to-image translation model (CycleGAN) for instance-level domain transfer from simulation to reality. This pipeline enables the generation of realistic labeled data from synthetic images for training supervised machine learning models in vision-based navigation tasks. We evaluate our approach on real-world data for ego-vehicle pose estimation, a critical autonomous navigation task involving the prediction of cross-track position and heading angle relative to road center line markings. The results of our tests show that our GAN-based data augmentation pipeline significantly outperforms models trained solely on simulation data or on data processed with standard image augmentation methods for sim-to-real transfer, enhancing model robustness and generalizability in real-world scenarios. Our method provides a scalable and flexible data augmentation tool for leveraging large synthetic datasets to enhance vision-based robotic navigation tasks.

artificial intelligence↗
NASA NTRS2023

Aero-Engines AI - A Machine-Learning App for Aircraft Engine Concepts Assessment

Effective deployment of machine-learning (ML) models could drive a high level of efficiency in aircraft engine conceptual design. Aero-Engines AI is a user-friendly app that has been created to deploy trained machine-learning (ML) models to assess aircraft engine concepts. It was created using tkinter, a GUI (graphical user interface) module that is built into the standard Python library. Employing tkinter greatly facilitates the sharing of ML application as an executable file which can be run on Windows machines (without the need to have Python or any library installed). The app gets user input for a turbofan design, preprocesses the input data, and deploys trained ML models to predict turbofan thrust specific fuel consumption (TSFC), engine weight, core size, and turbomachinery stage-counts. The ML predictive models were built by employing supervised deep-learning and K-nearest neighbor regression algorithms to study patterns in an existing open-source database of production and research turbofan engines. They were trained, cross-validated, and tested in Keras, an open-source neural networks API (application programming interface) written in Python, with TensorFlow (Google open-source artificial intelligence library) serving as the backend engine. The smooth deployment of these ML models using the app shows that Aero-Engines AI is an easy-touse and a time-saving tool for aircraft engine design-space exploration during the conceptual design stage. Current version of the app focuses on the performance prediction of conventional turbofans. However, the scope of the app can easily be easily expanded to include other engine types (such as turboshaft and hybrid-electric systems) after their ML models are developed. Overall, the use of a machine-learning app for aircraft engine concept assessment represents a promising area of development in aircraft engine conceptual design.

machine learning↗
NASA NTRS2022

A Framework for Deep Learning Emulation of Numerical Models With a Case Study in Satellite Remote Sensing

Numerical models based on physics represent the state of the art in Earth system modeling and comprise our best tools for generating insights and predictions. Despite rapid growth in computational power, the perceived need for higher model resolutions overwhelms the latest generation computers, reducing the ability of modelers to generate simulations for understanding parameter sensitivities and characterizing variability and uncertainty. Thus, surrogate models are often developed to capture the essential attributes of the full-blown numerical models. Recent successes of machine learning methods, especially deep learning (DL), across many disciplines offer the possibility that complex nonlinear connectionist representations may be able to capture the underlying complex structures and nonlinear processes in Earth systems. A difficult test for DL-based emulation, which refers to function approximation of numerical models, is to understand whether they can be comparable to traditional forms of surrogate models in terms of computational efficiency while simultaneously reproducing model results in a credible manner. A DL emulation that passes this test may be expected to perform even better than simple models with respect to capturing complex processes and spatiotemporal dependencies. Here, we examine, with a case study in satellite-based remote sensing, the hypothesis that DL approaches can credibly represent the simulations from a surrogate model with comparable computational efficiency. Our results are encouraging in that the DL emulation reproduces the results with acceptable accuracy and often even faster performance. We discuss the broader implications of our results in light of the pace of improvements in high-performance implementations of DL and the growing desire for higher resolution simulations in the Earth sciences.

Bayesian Deep Learning↗
NASA NTRS2019

Artificial Intelligence: Powering Human Exploration of the Moon and Mars

Artificial Intelligence (AI) is a growing field of computa- tional science techniques designed to mimic functions per- formed by people. Advancements in autonomy will depend on a portfolio of AI technologies. Automated planning and scheduling is a venerable field of study in AI, and is needed for a variety of mission planning functions. Plan execution technology is less well studied, but important for auton- omy and robotics. Specialized forms of automated reason- ing and machine learning are key technologies to enable fault management. Over the past decade, the NASA Au- tonomous Systems and Operations (ASO) project has devel- oped and demonstrated numerous autonomy enabling tech- nologies employing AI techniques. Our work has employed AI in three distinct ways to enable autonomous mission op- erations capabilities. Crew Autonomy gives astronauts tools to assist in the performance of each of these mission oper-ations functions. Vehicle System Management uses AI tech- niques to turn the astronaut's spacecraft into a robot, allow- ing it to operate when astronauts are not present, or to reduce astronaut workload. AI technology also enables Autonomous Robots as crew assistants or proxies when the crew are not present. When these capabilities are used to enable astro- nauts to operate autonomously, they must be integrated with user interfaces, introducing numerous human factors con- siderations; when these capabilities are used to enable vehi- cle system management, they must be integrated with flight software, and run on embedded processors under the control of real-time operating systems.We first describe human spaceflight mission operations capabilities. The remainder of the paper will describe the ASO project, and the development and demonstration per- formed by ASO since 2011. We will describe the AI tech- niques behind each of these demonstrations, which include a variety of symbolic automated reasoning and machine learn- ing based approaches. Finally, we conclude with an assess- ment of future development needs for AI to enable NASA's future Exploration missions.

Mission Operations↗
NASA NTRS2007

The Business Case for Automated Software Engineering

Adoption of advanced automated SE (ASE) tools would be more favored if a business case could be made that these tools are more valuable than alternate methods. In theory, software prediction models can be used to make that case. In practice, this is complicated by the 'local tuning' problem. Normally. predictors for software effort and defects and threat use local data to tune their predictions. Such local tuning data is often unavailable. This paper shows that assessing the relative merits of different SE methods need not require precise local tunings. STAR 1 is a simulated annealer plus a Bayesian post-processor that explores the space of possible local tunings within software prediction models. STAR 1 ranks project decisions by their effects on effort and defects and threats. In experiments with NASA systems. STARI found one project where ASE were essential for minimizing effort/ defect/ threats; and another project were ASE tools were merely optional.

model evaluation↗