Benchmarking Machine Learning on the Myriad X Processor Onboard the ISS
No abstract provided
Knox, Carrie↗
Engineering topics
Lu, Steven
Publications and source records attributed to Lu, Steven.
Mars Image Content Classification: Three Years of NASA Deployment and Recent Advances
The NASA Planetary Data System hosts millions of images acquired from the planet Mars. To help users quickly find images of interest, we have developed and deployed contentbased classification and search capabilities for Mars orbital and surface images. The deployed systems are publicly accessible using the PDS Image Atlas. We describe the process of training, evaluating, calibrating, and deploying updates to two CNN classifiers for images collected by Mars missions. We also report on three years of deployment including usage statistics, lessons learned, and plans for the future.
Mandrake, Lukas↗
Content-based On-board Summarization to Monitor Infrequent Change
No abstract provided
Wagstaff, Kiri↗
Efficient Active Learning for New Domains
The promise of active learning is to reduce the number of labeled examples required by supervised machine learning algorithms. The largest potential benefits lie in entirely new domains, for which no labeled examples yet exist. Yet to date, most active learning studies are retroactive and demonstrate the benefits that could have been gained if active learning had been used. What are the barriers to true adoption and utilization of active learning? We focus on two: (1) the cold start or class discovery problem, in which active learning methods may struggle to make progress with zero labeled examples, and (2) the cost of having the classifier in the loop to select the next example to be labeled. We assess different active learning approaches in the context of these two barriers and conclude with recommendations for how to employ active learning in new domains. As an example, we report on the use of active learning on a large, novel data set of Mars surface images.
Lu, Steven↗
COSMIC: Content-based Onboard Summarization to Monitor Infrequent Change
Interplanetary exploration occurs at vast distancesthat severely limit communication bandwidth to spacecraft exploringother planets. It is possible to collect much morescientific data than can ever be downlinked given current communicationcapabilities. Therefore, we are developing a systemcalled COSMIC (Content-based Onboard Summarization toMonitor Infrequent Change) that will opportunistically analyzedata onboard a Mars orbiter to alert scientists when meaningfulchanges have occurred. COSMIC will allow future spacecraftto continuously collect data to search for rare, transient phenomenasuch as fresh impacts or seasonally changing polarlandforms under a constrained downlink budget. In this paper,we describe the overall goals and architecture of COSMIC,plans to enable specific scientific studies, label acquisition toenable supervised approaches to surface landform classification,a new machine learning evaluation framework for analyzingthe trade-offs between classifier accuracy and computationalrequirements, and lessons learned about constraints that COSMICwill face operating onboard a spacecraft. In particular, wediscuss design considerations surrounding computational andstorage constraints, change detection strategies, and localizingdetected landforms of interest within a global coordinate frame.Finally, we describe challenges and open research questions thatmust be addressed prior to deploying COSMIC.
Trockman, Asher↗
Horizon Detection for Mars Surface Operations
A new image processing method for automated horizon detection is presented in this paper. This method was developed for the Mars Exploration Rovers mission’s surface operations to aid in determining orbiter visibility periods for downlink data, and is now being considered for all Mars surface missions and for a variety of applications. Starting with images received from Mars rovers or landers, the horizon pixel location is found by (1) obtaining the gradient images using the Sobel operator, (2) calculating the preliminary horizon locations by optimizing the maximum difference between sky and ground regions in the gradient domain, and (3) applying a multi-variable thresholding method. The results are analyzed using a variety of images from the Mars rovers, as an example one dataset’s results were categorized as 94.2% Good, 4.11% Okay, and 1.65% Poor. The pixel location corresponding to the found horizon in an image is converted to azimuth and elevation values using the camera model. The elevation of the terrain surrounding the rover or lander directly affects the duration orbiters are visible for downlink data volume, and when the sun is visible for image quality or solar energy consumption. Having an automated process to detect the elevation of the terrain allows the operation team to better predict the outcome of the plan, reducing heath and safety risks and allowing new operational limits to be defined.
Oij, Stephanie Lynn↗