Products

As MLIA addresses our many investigations and provided solutions, we endeavor to create reusable, generalizable technology that may be applied to follow-on maturation or transferred to other projects. MLIA also invests in internal technology that speeds and makes robust our development and analysis process. These are MLIA’s Products that we maintain, mature, leverage on new tasks, and share with other research groups to enrich the development of JPL’s data-driven analysis capabilities.

Please visit our public GitHub to see a full collection of our open sourced software packages.

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  Autonomous Exploration for Gathering Increased Science (AEGIS)

  The AEGIS intelligent targeting system allows a Mars rover to detect, prioritize, select, and observe scientific targets on its own, without waiting for input from humans on Earth. AEGIS collects an image with a camera aboard the rover, analyzes the image to identify geological targets, chooses the target that best matches the parameters specified by operators, and then points a remote-sensing instrument at the selected target(s), all autonomously. First deployed on the MER rover Opportunity, AEGIS is currently used on the MSL mission to select targets for the Curiosity rover’s ChemCam instrument, a remote laser-induced breakdown spectrometer. While the majority of targets are still selected by humans, AEGIS allows the rover to continue its geochemical survey during periods when Earth cannot be in the loop, and has contributed measurements on hundreds of targets, and become a routine part of how we explore Mars.




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  Basic Instrumentation Tool for Fault Localized Injection of Probabilistic SEUs (BITFLIPS)

  BITFLIPS is a software simulator for injecting single event upsets (SEUs) into a running computer program. The software is written as a plugin extension module of the open source Valgrind debugging and profiling tool. BITFLIPS can inject SEUs into any program which can be run on the Linux operating system, without modifying the original program or requiring access to the program source code. If access to the original program source code is available, BITFLIPS can offer fine-grained control over exactly when and which areas of memory (program variables) may be subjected to SEUs. SEU injection rate is controlled by specifying either a fault rate based on memory size and radiation exposure time in terms of SEUs per kilobyte per instruction. BITFLIPS also has the capability to log each SEU it injects and, if program source code is available, report the magnitude of the SEU on the program variable.




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  COmplex Data EXplorer (CODEX)

  Modern science datasets may have 500+ simultaneous measurements at each of millions of time samples. Scientists would often like to look through the record and discover not only expected trends but ones they did not initially guess. The optimal environment for this rapid exploration large data would be one where visualizations were clear, interactive, and responsive, permitting the investigator to “play” with the data and gain rapid insight, falsify hypothesis, and make discoveries. Machine Learning has proven invaluable in providing some of these key data insights, but to do so in a statistically robust and reliable manner requires a data science professional and a lot of custom Python code, losing any sense of interaction and play. CODEX addresses these concerns by providing a desktop-like environment with standard scientific graph types that are robust to rapid, powerful exploration.




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  Data Ordering Genetic Optimization (DOGO)

  DOGO was developed on the Orbiting Carbon Observatory 2 (OCO-2) mission to identify features that predict when the received CO2 goes awry. It was later adopted to advise science users on the trustability of individual observations in the mission record via the Warn Level formula: a simple prescription that assigns each observation an integer from 0 to 9 estimating how much confounding forces are present. DOGO's greatest significance is its ability to predict data trust without the need for ground truth data, instead using a series of expert-derived sanity checks on the data to narrow down on the best and worse behaved observations. DOGO is currently applying to AMMOS for implementation funds to spread its technology to other future missions.




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  Distributed Optimization of Machine Learning Incorporating Nested Evaluation (DOMINE)

  DOMINE is a software tool that enables large-scale exploration and tuning of competing machine learning technologies in a fully robust, cross-validated, and distributed sense including feature selection. DOMINE asks users to specific their desired datasets, labels, and classifiers to enable a full and efficient diagnosis which technologies, hyperparameter settings, and feature settings perform best. Our system, by design, incorporates nested cross validation to ensure publication-quality results with statistically robust, defensible decisions for model optimality. All of this is accomplished on top of a client-server interface that allows for large-scale, distributed computation for quicker results.




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  Science Yield improvemeNt via Onboard Prioritization and Summary of Information System (SYNOPSIS)

  SYNOPSIS is a framework for downlink prioritization that uses onboard estimates of science utility along with data product similarity assessments to select a downlink ordering that prioritizes products with high science utility while also capturing data with diverse characteristics to enable serendipitous discovery of novel or unexpected phenomena. SYNOPSIS will also account for user-specified dependencies that positively or negatively affect the utility of observations across instruments, such as observations of the same target from multiple instruments that provide complementary information.




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  Tuning Optimizing Genetic Algorithm (TOGA)

  The Tuning Optimizing Genetic Algorithm (TOGA) was developed on the Ocean Worlds Life Surveyor project to perform black-box optimization on software with large numbers of tunable parameters. In addition to featuring highly configurable and transparent mutators, crossover functions, population controls, and performance metrics, TOGA is able to take full advantage of high performance distributed computing with its client-server architecture for fast convergence. TOGA provides optimization for both classifier selection and hyperparameter tuning, in addition to supporting sensitivity studies and tradeoff analysis between configuration options.





Data Sets

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  Barefoot Rover Terrain Data

  This archive contains test data of the Barefoot Rover wheel on multiple materials. The Barefoot Rover wheel was used to demonstrate an instrumented wheel concept which utilizes a 2D pressure grid, an electrochemical impedance spectroscopy (EIS) sensor and machine learning (ML) to extract meaningful metrics from the interaction between the wheel and surface terrain. These include continuous slip/skid estimation, balance, and sharpness for engineering applications. Estimates of surface hydration, texture, terrain patterns, and regolith physical properties such as cohesion and angle of internal friction are additionally calculated for science applications. Through in-situ sensing, these metrics can be calculated in near real time and made available to onboard science and engineering autonomy applications.




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  HiRISE Classified Landmarks

  This data set contains landmarks which were detected and extracted from HiRISE browse images. For each original landmark, we cropped a square bounding box that includes the full extent of the landmark plus a 30-pixel margin to left, right, top and bottom. Each cropped landmark was resized to 227x227 pixels.




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  Mars Target Encyclopedia Annotated Documents (LPSC Abstracts)

  This data set contains annotated text versions of 2-page abstracts published at the Lunar and Planetary Science Conference in 2015 and 2016.




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  Mars Target Encyclopedia Database

  The Mars Target Encyclopedia (MTE) contains information about Mars mission surface targets that have appeared in scientific publications and captures information and excerpts in a searchable database.




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  Mastcam Novelty Detection Image Data Set

  These datasets were used for experiments in the paper:
  Kerner, H. R., Wellington, D. F., Wagstaff, K. L., Bell III, J. F., Ben Amor, H. Novelty Detection for Multispectral Images with Application to Planetary Exploration. In Proceedings of Innovative Applications in Artificial Intelligence (IAAI/AAAI), 2019.




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  MSL Classified Images

  This data set consists of images spanning 24 classes that were collected by the Mars Science Laboratory (MSL, Curosity) rover by three instruments (Mastcam Right eye, Mastcam Left eye, and MAHLI). These images are the "browse" version of each original data product, not full resolution. They are roughly 256x256 pixels each.




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  Planetary Body Plumes (Annotated)

  This data set was compiled to aid in evaluating methods for automated analysis of images to detect planetary bodies and limbs. It contains manually generated labels for 308 NASA images of planets and moons. The labels annotate the location of the limb (edge) of the body and plumes emitted by the body, if any. "Plume" in this context refers to any bright material emitted from the body, such as icy plumes from Enceladus or volcanic plumes from Io. 112 of the labeled images contain plumes.