Retrieving Aerosol Optical Depth and High Spatial Resolution Ocean Surface Wind Speed From CALIPSO: A Neural Network Approach
This study uses ocean surface wind speed derived from microwave radiometer to train spacebased lidar measurements to measure both wind speed and aerosol optical depth using neural…
Severe Storm Prediction via Overshooting Cloud Top and Above Anvil Cirrus Plume Image Recognition from Satellite Imager Data
Overshooting Cloud Tops (OTs) Above-Anvil Cirrus Plumes (AACPs) are indicators of especially intense thunderstorm updrafts that are precursors to severe weather including damaging…
Aero-Engines AI - a machine-learning app for aircraft engine system-performance prediction
Aero-Engines AI is a Windows app that deploys machine-learning analytics to predict aircraft engine performance.
Using Natural Language Processing to Help Automate the Standardization of PI Variable Names from ICARTT Files
Variables names of data collected from suborbital missions need mapping to the corresponding standard variable names for consistency in labelling the available science data, enhan…
Airplane detection
Deep learning-based airplane detection from high-resolution satellite imagery
Automated Dust detection in satellite imagery
Application of machine learning to the problem of night-time dust detection with a simple random forest (RF) model using Geostationary Operational Environmental Satellite-16 (GOES…
Deep Learning-based Hurricane Intensity Estimator
A web-based situational awareness tool that uses deep learning on satellite images to objectively estimate windspeed of a hurricane
GCMD Keyword Recommender (GKR)
Natural Language Processing-based science keyword suggestion tool
Marine debris detection using deep learning and high resolution satellite images
Floating marine debris is a global pollution problem which threatens marine and human life and leads to the loss of biodiversity. Large swaths of marine debris are also navigation…
Near-Reality Slosh Model using AIML
Liquid slosh for aircraft and spacecraft is a highly challenging topic; traditional slosh modeling & simulation approaches are very computationally expensive. This project seeks t…
Phenomena Detection Portal
A system that detects Earth science phenomena from the image archives using deep learning to provide efficient search and discovery of Earth science events
Pixel-Level Smoke Detection Model with Deep Learning
Automated, deep learning based detection model capable of identifying smoke plumes from shortwave reflectance for the Geostationary Operational Environmental Satellite R series of…
Predicting streamflow with deep learning
Uses a long short-term memory model to predict streamflow at USGS gauges sites
Ship detection
Deep learning-based ship detection from high-resolution satellite imagery
Sinatra Software for Anomaly Detection
Flexible software framework that analyzes various input sources of data and detects anomalies. Automates neural network model creation and tuning.
INtelligent StennIs Gas House Technology (INSIGHT)
INSIGHT is an operational system that performs autonomous Integrated System Health Management (ISHM) and autonomous operations of the Nitrogen System of the High Pressure Gas Faci…
NASA Platform for Autonomous Systems (NPAS)
The NASA Platform for Autonomous Systems (NPAS) enables implementation of "thinking" systems, and in particular of "thinking" autonomous systems. A broad range of systems can be m…
Autonomous WAiting Room Evaluation (AWARE)
Using an existing security camera and YOLO Machine Learning model to detect and count number of people waiting for service at Langley's Badge & Pass Office. When a predetermined t…
Graph Neural Networks for Airfoil Performance Prediction
We are investigating the use of Graph Convolutional Neural Networks to learn relationship between airfoil coordinates and predict the performance for aerodynamics analysis. Inputs…
Pre-trained microscopy image neural network encoders
Convolutional Neural Network encoders were trained on over 100,000 microscopy images of materials.
Surrogate Models for Efficient Multiscale Modeling of Composite Materials
A custom neural network architecture containing graph convolutional network (GCN) and long short-term memory (LSTM) layers was trained as a computationally efficient surrogate for…
Federated Learning Using In-Space Data (FLUID )
Enables neural net models to be trained using a combination of terrestrial data and space-borne data without the need to downlink or uplink data for consolidation and training in…
Mapping Digital Infrastructure (MADI)
The Mapping Digital Infrastructure (MADI) is a series of natural-language processing based tools that are designed to supplement the human-in-the-loop data exploration for CAS.
Use of AI for UAV power train monitoring
Use of PyTorch feedforward neural networks to monitor the health of electronic speed controllers and motors.