Accessing and querying NASA’s Planetary Data System (PDS) can present a steep learning curve due to the complex metadata structures and large volume of filterable parameters. To address this, we developed an LLM-powered assistant to help users generate accurate filter selections for planetary datasets, with an initial focus on Mars Reconnaissance Orbiter (MRO) data. Our approach leverages Retrieval-Augmented Generation (RAG) with hybrid methods, including BM25 and dense embeddings, to interpret natural language queries and recommend relevant filters. We also introduce a similarity search function that enables users to retrieve scientifically relevant datasets based on semantically related queries or previous selections, improving the refinement and contextualization of results. In addition, we developed an image analysis module that allows users to input planetary images and returns visually and contextually similar images by using a vision encoder and a pre-indexed embedding database. This functionality is currently limited to the Mars Science Laboratory (MSL) mission, specifically imagery related to the Curiosity rover. This module also extracts available details about the input image, enhancing user understanding and supporting visual-based data discovery. For PDS3 datasets (legacy format), we implemented a script that achieves over 80% accuracy and precision in predicting appropriate filters. For PDS-4 (the modern metadata structure), we are testing a new AI assistant that leverages the PDS API client to guide users through the well-organized dropdown filters, potentially removing the need for direct URL prediction due to the improved schema. A unified GUI is also in development to integrate all assistant modules and further enhance accessibility. This system enhances usability across both metadata structures, significantly lowering the barrier for scientific data discovery and increasing the efficiency of user interaction with planetary archives.

We introduce PageLocQA, a modular retrieval-augmented generation (RAG) framework tailored for page-level source attribution in long-document question answering. Rather than relying on complex agentic controllers or top-k retrieval alone, PageLocQA adopts a lightweight strategy that combines manual tool routing with structured prompting to generate semantically accurate answers along with page citations.We evaluate PageLocQA on a curated MODTRAN QA dataset, verified by domain experts, as well as benchmark datasets such as HotpotQA, TriviaQA, and Natural Questions. The MODTRAN 6 User Manual is a comprehensive technical manual detailing atmospheric radiative transfer modeling, input parameters, and configuration workflows, making it an ideal testbed for page-grounded QA. While it does not outperform large-scale frameworks like ChatQA or SelfRAG, PageLocQA achieves competitive BERTScore results across all datasets and demonstrates superior grounding performance on MODTRAN, achieving a BERTScore of 0.871 and F1 score of 0.303.These findings underscore the practicality of lightweight RAG-based approaches in settings that require both accuracy and explainability, especially in resource-constrained or scientific QA environments.

The NASA Planetary Data System (PDS) hosts millions of images of planets, moons, and other bodies collected throughout many missions. The ever-expanding nature of data and user engagement demands an interpretable content classification system to support scientific discovery and individual curiosity. In this paper, we leverage a prototype-based architecture to enable users to understand and validate the evidence used by a classifier trained on images from the Mars Science Laboratory (MSL) Curiosity rover mission. In addition to providing explanations, we investigate the diversity and correctness of evidence used by the content-based classifier. The work presented in this paper will be deployed on the PDS Image Atlas, replacing its non-interpretable counterpart.

Deep space missions can benefit from onboard image analysis. We demonstrate deep learning inference to facilitate such analysis for future mission adoption. Traditional space flight hardware provides modest compute when compared to today's laptop and desktop computers. New generations of commercial off the shelf (COTS) processors designed for embedded applications, such as the Qualcomm Snapdragon and Movidius Myriad X, deliver significant compute in small Size Weight and Power (SWaP) packaging and offer direct hardware acceleration for deep neural networks. We deploy neural network models on these processors hosted by Hewlett Packard Enterprise's Spaceborne Computer-2 onboard the International Space Station (ISS). We benchmark a variety of algorithms trained on imagery from Earth or Mars, as well as some standard deep learning models for image classification.

Future space missions will process and analyze imagery onboard placing greater demands on flight computing. Traditional flight hardware provides modest compute, even when compared to laptop and desktop computers. A new generation of commercial off the shelf (COTS) processors, such as Qualcomm Snapdragon, deliver significant compute in small Size Weight and Power (SWaP) and offer direct hardware acceleration in the form of Graphics Processing Units (GPU) and Digital Signal Processors (DSP). We benchmark a variety of instrument processing and analysis software (including machine learned classifiers) on a Qualcomm Snapdragon SoC currently hosted by HPE’ s Spaceborne Computer-2 (SBC-2) onboard the International Space Station.

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 content-based 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.