COSMIC: Content-based Onboard Summarization to Monitor Infrequent Change

Doran, Gary; Lu, Steven; Liukis, M.; Mandrake, Lukas; Rebbapragada, Umaa; Wagstaff, Kiri L.; Young, Jimmie; Langert, Erik; Braunegg, Anneliese; Horton, Paul; Jeong, Daniel; Trockman, Asher

doi:10.1109/aero47225.2020.9172337

Cited by 8 publications

(4 citation statements)

References 19 publications

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“…For ex-situ methods, machine learning can help scientists analyze data and notify noteworthy findings. JPL scientists [17] built an impact crater classifier to analyze images captured by the Martian Reconnaissance Orbiter. Dundar et al [19] applied machine learning algorithms to discover less common minerals and search for aqueous mineral residue.…”

Section: Machine Learning In Mars Explorationmentioning

confidence: 99%

Semi-Supervised Learning for Mars Imagery Classification and Segmentation

Wang¹,

Lin²,

Fan³

et al. 2022

Preprint

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With the progress of Mars exploration, numerous Mars image data are collected and need to be analyzed. However, due to the imbalance and distortion of Martian data, the performance of existing computer vision models is unsatisfactory. In this paper, we introduce a semi-supervised framework for machine vision on Mars and try to resolve two specific tasks: classification and segmentation.Contrastive learning is a powerful representation learning technique. However, there is too much information overlap between Martian data samples, leading to a contradiction between contrastive learning and Martian data. Our key idea is to reconcile this contradiction with the help of annotations and further take advantage of unlabeled data to improve performance. For classification, we propose to ignore inner-class pairs on labeled data as well as neglect negative pairs on unlabeled data, forming supervised inter-class contrastive learning and unsupervised similarity learning. For segmentation, we extend supervised inter-class contrastive learning into an element-wise mode and use online pseudo labels for supervision on unlabeled areas. Experimental results show that our learning strategies can improve the classification and segmentation models by a large margin and outperform state-of-the-art approaches.

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Section: Machine Learning In Mars Explorationmentioning

confidence: 99%

Semi-Supervised Learning for Mars Imagery Classification and Segmentation

Wang¹,

Lin²,

Fan³

et al. 2022

Preprint

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“…The Capturing Onboard Summarization to Monitor Image Change (COSMIC) project is currently in development to automatically analyze data onboard Mars spacecraft and notify scientists when anything noteworthy occurs or changes in order to eliminate bandwidth limitations at large distances [31]. As a part of COSMIC, JPL scientists created an automatic impact crater classifier, which analyzes images captured by the Martian Reconnaissance Orbiter (MRO) to discover impact craters on the surface of Mars.…”

Section: Ii1 Cosmic Discovering Of Mars Impact Cratersmentioning

confidence: 99%

“…Before utilizing machine learning techniques for crater discovery, crater discovery initially resulted from time-consuming and labor-intensive manual human analysis of MRO imagery of the Martian surface. The utilization of machine learning methods for analyzing MRO-captured imagery to discover impact craters resulted in easier discovery of smaller impact craters, less wasted time manually analyzing images, and an increase in crater discoveries [31].…”

Section: Ii1 Cosmic Discovering Of Mars Impact Cratersmentioning

confidence: 99%

Machine Learning for Mars Exploration

Momennasab¹

2021

Preprint

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Risk to human astronauts and interplanetary distance causing slow and limited communication drives scientists to pursue an autonomous approach to exploring distant planets, such as Mars. A portion of exploration of Mars has been conducted through the autonomous collection and analysis of Martian data by spacecraft such as the Mars rovers and the Mars Express Orbiter. The autonomy used on these Mars exploration spacecraft and on Earth to analyze data collected by these vehicles mainly consist of machine learning, a field of artificial intelligence where algorithms collect data and self-improve with the data. Additional applications of machine learning techniques for Mars exploration have potential to resolve communication limitations and human risks of interplanetary exploration. In addition, analyzing Mars data with machine learning has the potential to provide a greater understanding of Mars in numerous domains such as its climate, atmosphere, and potential future habitation. To explore further utilizations of machine learning techniques for Mars exploration, this paper will first summarize the general features and phenomena of Mars to provide a general overview of the planet, elaborate upon uncertainties of Mars that would be beneficial to explore and understand, summarize every current or previous usage of machine learning techniques in the exploration of Mars, explore implementations of machine learning that will be utilized in future Mars exploration missions, and explore machine learning techniques used in Earthly domains to provide solutions to the previously described uncertainties of Mars.

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“…[3], and execution [4], selection of scientific targets [5], and on-board data summarization and compression [6] are being developed for future space missions. These autonomy technologies hold promise to enable missions that cannot be achieved with traditional ground-in-the-loop operations cycles due to communication constraints, such as high latency and limited bandwidth, combined with dynamic environmental conditions or limited mission lifetime.…”

Section: Introductionmentioning

confidence: 99%

Operations for Autonomous Spacecraft

Castano,

Vaquero,

Rossi

et al. 2021

Preprint

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Onboard autonomy technologies such as planning and scheduling, identification of scientific targets, and content-based data summarization, will lead to exciting new space science missions. However, the challenge of operating missions with such onboard autonomous capabilities has not been studied to a level of detail sufficient for consideration in mission concepts. These autonomy capabilities will require changes to current operations processes, practices, and tools. We have developed a case study to assess the changes needed to enable operators and scientists to operate an autonomous spacecraft by facilitating a common model between the ground personnel and the onboard algorithms. We assess the new operations tools and workflows necessary to enable operators and scientists to convey their desired intent to the spacecraft, and to be able to reconstruct and explain the decisions made onboard and the state of the spacecraft. Mock-ups of these tools were used in a user study to understand the effectiveness of the processes and tools in enabling a shared framework of understanding, and in the ability of the operators and scientists to effectively achieve mission science objectives.

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COSMIC: Content-based Onboard Summarization to Monitor Infrequent Change

Cited by 8 publications

References 19 publications

Semi-Supervised Learning for Mars Imagery Classification and Segmentation

Semi-Supervised Learning for Mars Imagery Classification and Segmentation

Machine Learning for Mars Exploration

Operations for Autonomous Spacecraft

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