Annie Didier (JPL) on "Incepting Interplanetary “Google Search” through Machine Learning"

Co-hosted with ML4PSP (Machine Learning for Planetary Space Physics): https://ml4psp.github.io.

Spacecraft can produce a far greater volume of data than can be downlinked. Though interplanetary communication rates have grown in orders of magnitude since early missions, they are far surpassed by the growth in data volume produced by on-board instruments. To bypass the communication bottleneck between spacecraft and ground while optimizing scientific yield, we propose the concept of ‘Interplanetary Google Search,’ a novel approach to spacecraft data retrieval inspired by the Google search engine. We envision a selective downlink capability with on-board indexing and search where scientists can query a spacecraft’s on-board database for specific, relevant information. To realize this on-board data storage and indexing vision, we must first introduce the means to extract features relevant to scientific interest from historic data payloads. The key to our approach is utilizing machine learning to extract features and summarize data. We have demonstrated this capability using image segmentation and image captioning models of MSL RGB imagery. Such methods would, for instance, enable scientists to download a full textual summary of the imagery taken by a rover and use this information to downlink data with specific features for further analysis. This concept has even more potential for a wider range of deep-space missions with more data-intensive instruments (e.g. ground-penetrating radar and hyperspectral imagers), and can be realized with the application of data science.