Legacy missions such as Voyager, Galileo, Cassini, and New Horizons have provided a treasure trove of historic and irreplaceable data about the outer planets. Making these datasets available to future researchers in the most accessible and analysis-ready manner possible is important to preserving their value. Our project aims to make these datasets easier and more efficient to use by solving two tedious and time-consuming tasks that must be performed by most researchers: image navigation and geometric metadata generation.

A prerequisite for planetary image analysis is knowledge of what each image pixel represents. Typically, this is done using the SPICE toolkit along with a set of "kernel" files that define aspects of the planetary system and spacecraft geometry as a function of time. Unfortunately, spacecraft kernels as supplied by mission teams commonly have errors on the order of dozens to hundreds of pixels, making them of limited value. Researchers must fix these errors by performing their own image navigation using the SPICE toolkit and identifiable reference points such as stars, bodies, or ring features to determine accurate spacecraft pointing. After navigation is complete, the SPICE toolkit can be used to provide geometric information about each image pixel, such as lighting and observation geometry, to enable further scientific exploration.

While the SPICE toolkit has extraordinary capabilities, it also has a steep learning curve. We are eliminating the need for scientists to perform these steps themselves by 1) navigating each image with a precision approaching one image pixel and creating new SPICE kernels containing the improved pointing, 2) creating a collection of summary images, which provide visual context for each navigated image, and 3) creating a comprehensive set of backplanes that describe the geometric content of each pixel within each image. A backplane is an array with the same dimensions as the source image, containing a calculated quantity that is associated with each pixel's location. The backplanes we produce for each image include, for each body in the field of view, latitude, longitude, and surface resolution, and for the ring plane, radius, longitude, and resolution; we also include incidence, emission, and phase angles for both. These pre-computed backplanes can entirely eliminate the need for many scientists to learn the SPICE toolkit. All of our products will be archived with NASA's PDS.

Our navigation and backplane generation techniques [1,2] have already been proven in several research contexts, including Saturn's F Ring [3,4] and Titan's cloud layers [5]. Over the next two years, we would like to partner with additional researchers to help us beta test our results so that we can provide the greatest long-term benefit possible to the research community. If you are interested, please contact us.

[1] French et al. 2014, DPS 46, #422.01; [2] French et al. 2016, DPS 48, #121.14; [3] Lessard et al. 2022, DPS 54, #317.01; [4] French et al. 2024, DPS 56, #204.03; [5] Hanson et al. 2025, Geophys Res Lett, 52, e2024GL113415 (also abstract this conference).

How to cite: French, R., Showalter, M., and Spitale, J.: Pre-Computed Navigation and Backplanes in Support of Legacy Outer Planet Missions, EPSC-DPS Joint Meeting 2025, Helsinki, Finland, 7–13 Sep 2025, EPSC-DPS2025-382, https://doi.org/10.5194/epsc-dps2025-382, 2025.

We are developing a suite of tools for the visualization and analysis of exploration data from small celestial bodies with irregular shapes [1-8]. These tools represent the shapes of such bodies using polygon models and visualize them with 3D computer graphics. In addition, they can visualize various types of geospatial information associated with the shape models. We refer to this concept as 3D-GIS [7-8]. AiGIS and PyAiGIS are tools developed based on this concept. This paper reports the current development status of these two tools.

AiGIS is the first product in the software series based on the 3D-GIS concept. Written in Java, it is a standalone application that runs on major platforms including Windows, macOS, and Linux. It enables even beginners to easily visualize data and perform basic analyses (Fig. 1). AiGIS has been publicly available on our website for over five years[5-6] (AiGIS project site: https://arcspace.jp/aigis:top). While its core functionality is nearly complete, the software continues to receive updates to support new datasets and to fix bugs identified during use. The latest release was published in April, 2025.

Fig. 1. AiGIS screenshot. Shape and geographic information of the asteroid Ryugu are visualized.

PyAiGIS is a newly launched project aimed at developing an entirely new 3G-GIS environment based on Python and Jupyter Notebook [1-4]. Leveraging Python’s rich ecosystem of libraries, PyAiGIS is designed to enable interactive data manipulation, analysis, and visualization within the Jupyter Notebook environment (PyAiGIS project site: https://arcspace.jp/aigis2:top).

The system is built on PyVista, a powerful data visualization library [9]. PyVista serves as a Python interface for the Visualization Toolkit (VTK), an open-source software system widely used for manipulating and rendering polygon-based data [10]. PyVista provides access to VTK’s advanced visualization capabilities through a simplified and user-friendly interface, and allows for seamless handling of spatially referenced datasets. PyVista and VTK support the import of three-dimensional models in the Wavefront Object (OBJ) file format, which is the standard format used in AiGIS and similar tools for storing shape data of small bodies.

In AiGIS, geographic information is organized as a list of values corresponding to each plate of a three-dimensional polygonal shape model. While PyVista mesh objects can associate data directly with plates and vertices, PyAiGIS adopts a different design in which geographic data are managed separately using a Pandas DataFrame object. Pandas is a widely used Python library that provides robust data structures and flexible operations for handling numerical tables and time series [11]. Its capabilities for data selection, extraction, substitution, and statistical analysis make it particularly suitable for managing geospatial information.

To manage ancillary data and perform space geometry computations, PyAiGIS makes use of SpiceyPy [12], a Python wrapper for the SPICE toolkit developed by NASA’s Navigation and Ancillary Information Facility (NAIF). Although alternative libraries exist, SpiceyPy is chosen for its reliable and well-designed implementation, which ensures accurate and efficient handling of space geometry relevant to small body exploration data.

As outlined in our previous reports [1-4], the development of PyAiGIS follows a two-stage plan. The first stage involves experimental development through the creation of example Python scripts, while the second stage will focus on the practical implementation of reusable Python functions and data structures. The project is currently in the first stage, with ongoing efforts directed toward expanding the analysis and visualization capabilities of the PyAiGIS environment. The outcomes of this stage are being shared through the project’s GitHub repository (https://github.com/AiGIS-PyAiGIS/PyAiGIS).

The repository contains example code and documentation that demonstrate basic operations such as visualizing shape models and geographic data, extracting geospatial information stored in Pandas DataFrames, and generating and displaying latitude and longitude grids. It also provides example scripts for drawing mapping elements, such as lines and circles, at arbitrary locations on the surface of a shape model, as well as for overlaying global map images (Fig. 2). Furthermore, the repository offers guidance on selecting suitable visualization methods available in PyVista, depending on the user’s computing environment and intended use. Some procedures have already been implemented as Python functions, which are expected to serve as a foundation for the second stage of PyAiGIS development.

Fig. 2. PyAiGIS screenshots. Surface slope with color (A), geopotential height with contours (B), slope direction vectors (C), global mosaic image map (D), and line and circle drawing (E) are visualized on the asteroid Ryugu.

Acknowledgements: We thank T. Endo and T. Nagayoshi, who contributed to this work as an undergraduate/master’s student in our laboratory, for their efforts during the initial phase of the project. This project was supported by MEXT Promotion of Distinctive Joint Research Center Program and Promotion of Distinctive Joint Usage/Research Center Program (Grant Number: JPMXP0619217839, JPMXP0622717003 and JPMXP072383045) and supported by the JAXA Hayabusa2# International Visibility Enhancement Project.

References: [1] Hirata, N. et al. (2025) LPS LVI, Abstract #1873. [2] Hirata N. and Nagayoshi, T. (2024) LPS LV, Abstract #1877. [3] Nagayoshi, T. and Hirata, N. (2023) AGU Fall Meeting, P30F-3207. [4] Nagayoshi, T. and Hirata, N. (2022) AGU Fall Meeting, P25F-2187. [5] Hirata, N. et al. (2019) LPS L, Abstract #2347. [6] Hirata, N. et al. (2018) LPS XLIX, Abstract #1849. [7] Hirata, N. et al. (2008) LPS XXXIX, Abstract #1584. [8] Fujii, Y. et al. (2007) LPS XXXVIII, Abstract #1521. [9] PyVista, https://docs.pyvista.org/ [10] VTK, https://vtk.org/ [11] Pandas, https://pandas.pydata. org/ [12] Annex et al., (2020) Journal of Open Source Software, 5(46), 2050.

How to cite: Hirata, N.: AiGIS and PyAiGIS: Tools for Geographic Visualization and Analysis for Irregular-shaped Small Bodies, EPSC-DPS Joint Meeting 2025, Helsinki, Finland, 7–13 Sep 2025, EPSC-DPS2025-515, https://doi.org/10.5194/epsc-dps2025-515, 2025.

While obtaining a DOI for your data set is a good first step, most destinations are more than a single step away. There are various reasons to tag data with DOIs – citability, findability, etc., but the minimum metadata requirements for obtaining a DOI do not adequately support most of them.

In this presentation, we examine the DataCite metadata schema[1] – the metadata schema most data providers will be dealing with – and consider what fields have the highest return-on-investment for data providers. Specifically, we consider the case of data providers who are seeking to have their high-quality research data sets incorporated into the literature that comprises the Body of Knowledge. We’ll identify these key metadata fields, provide advice for filling them with actionable values, and highlight the benefits that come with consistent, quality metadata.

[1] DataCite Metadata Schema 4.6 (https://doi.org/10.14454/mzv1-5b55)

How to cite: Raugh, A., Cecconi, B., D'Abrusco, R., Henneken, E., Muench, A., and Landais, G.: Making Your DOI Work for You, EPSC-DPS Joint Meeting 2025, Helsinki, Finland, 7–13 Sep 2025, EPSC-DPS2025-987, https://doi.org/10.5194/epsc-dps2025-987, 2025.

Introduction: Ejecta layering is an important characteristic of the airless lunar surface, and ejecta emplacement contributes much in the surface modification and regolith evolution [1, 2]. Next lunar missions, especially under the Artemis project [3], and the CP-22 mission with the PROSPECT payload onboard [4] will have various interactions with the regolith. including sampling and geotechnical processing, thus information on the structure of the regolith is useful both for scientific, In Situ Resource Utilization (ISRU) and safety aspects. Especially small craters have been poorly covered and understood in these aspects [5] as well as for next landing site candidates [6]. I present a new method of ejecta thickness estimation and introduce the test version of a web based application, which calculates the thickness, volume and stratigraphy of the ejecta for a chosen location on the lunar surface and estimates their source craters.

Data processing and visualization: Based on several measurements on the general topographic radial profile of ejecta plus bedrock uplift around differently sized craters, i used LOLA Digital Elevation model with spatial resolution of 60 m/px (for crates with diameter < 2 km) and WAC Digital Elevation Model with spatial resolution of 100 m/px (for craters with diameter > 2 km) to define a mathematical formula to model the observed crater ejecta topography. 

Figure 1. Digital Elevation Model (first column) and optical images (LRO Narrow Angle Camera Mosaic and Wide Angle Camera Mosaic, second column) from examples of different crater size categories and the ejecta profiles (third column) of the three craters form each categories (blue lines) and the modelled ejecta thickness (red lines).

Having an ejecta estimation formula, in the next stage i have developed the online application, where clients can query information about the ejecta thickness for the selected point in the lunar surface. 

Figure 2. Used techniques and frameworks for the application.

For the calculation and visualization purposes two types of data storage are necessary. The position and size information about lunar craters are the key of the calculations: i used the Robbins crater database [7], that contains more than 1.2 million craters larger than 1km. PosGIS extension of PostgreSQL [8] was implemented to store these data. Navigation and localization on the lunar surface is important for the proper site selection: Geoserver [9] is designed to store spatial datasets, i added the IAU planetary CRS extension for the appropriate coordinate handling of the LROC WAC global mosaic with 100 m/px resolution.

I used JavaScript based Leaflet.js [10] for the site selection: the client has to click on the displayed lunar surface and jQuery [11] gives the coordinates of the chosen point to the Python based Flask [12] application. This application connects to the PostGIS database and extract the thickness data with an sql selection. After the calculation the backend converts the data to JSON (for displaying) and CSV (for downloading) and transfer back to the browser with MIME (Multipurpose Internet Mail Extensions).

Figure 3. Visualization of the source craters and their ejecta thickness at the selected location (centre) displayed in the Leaflet map. 

The Leaflet window displays the result: the craters that provides ejecta to the selected point with opacity related to the thickness. Blue popups also appear at the center of the craters and contain information about position, distance from the selected point, diameter and provided ejecta.

Future development: I am planning the further development of this application with some other important features. Age estimation (fist relative, then absolute where possible) of the source craters is the next stage. After the age information will be available, i can create layer sequence stratigraphy for the selected location. In the near future i will release this open source application to the planetary scientist community.   

Acknowledgement

This work was supported by the LUNGISTRAT project of ESA (4000146132), formerly the Ministry of National Economy and Trade, recently the Ministry of National Economic.

References

[1] Takano et al. 2020. Experimental study on thermal properties of high porosity particles for understanding physical properties of Phobos surface. In: JpGU-AGU Joint Meeting, PPS08-P01.

[2] Kobayashi et al. 2023. Laboratory measurements show temperature-dependent permittivity of lunar regolith simulants. Earth Planets and Space 75:1, 8.

[3] Moriarty and Petro 2024. Journal of Geophysical Research: Planets, Volume 129, Issue 4, article id. e2023JE008266.

[4] Heather et al. 2024. The ESA PROSPECT Payload for CP22: Science Activities and Operations Planning. 55th LPSC, No. 3040, id.1085

[5] Kereszturi and Steinmann 2019. Terra-mare comparison of small young craters on the Moon. Icarus 322, 54-68.

[6] Leone et al. 2023. Sverdrup-Henson crater: a candidate location for the first lunar South Pole settlement. iScience 9;26(10):107853.

[7] Robbins 2018. A New Global Database of Lunar Impact Craters >1–2 km: 1. Crater Locations and Sizes, Comparisons With Published Databases, and Global Analysis. . Journal of Geophysical Research: Planets, Volume 124, Issue 4. 871-892.

[8] PostgreSQL, https://www.postgresql.org/

[9] Geoserver, https://geoserver.org/

[10] Leaflet.js, https://leafletjs.com/

[11] jQuery, https://jquery.com/

[12] Flask, https://flask.palletsprojects.com/en/stable/

How to cite: Tomka, R. K.: Open source web based lunar ejecta thickness and layering calculator application, EPSC-DPS Joint Meeting 2025, Helsinki, Finland, 7–13 Sep 2025, EPSC-DPS2025-1204, https://doi.org/10.5194/epsc-dps2025-1204, 2025.

The Galileo NIMS data set

Between 1995 and 2003, the NASA Galileo spacecraft explored the Jupiter system, collecting invaluable data on the planet and its moons. Among many instruments, the spacecraft was equipped with the Near-Infrared Mapping Spectrometer instrument (NIMS), a complex imaging spectrometer operating from 0.7 to 5.2 microns with 17 detectors [1]. Galileo NIMS data are archived in the PDS as so-called “tubes” and “g-cubes” (or mosaics). Due to the varying distance and observing geometry of each targeted flyby in the Jupiter system, together with the own instrument operational settings and health, the dataset is very heterogenous in spatial, spectral, and angular resolution. Nonetheless, the Galileo/NIMS data represent one of the most valuable resource to model and map the surface properties (composition, grain size, roughness, phase function) of Jupiter's moons, which are the prime targets of the Europa Clipper [2] and ESA JUICE [3] missions in the decade.

The NIMS database framework

We converted the Galileo/NIMS calibrated g-cube dataset publicly available in the PDS Imaging Node (as g-cubes) into a relational database, which allows to quickly select and extract radiance factors (I/F), radiance (I), geometry data, and metadata from the entire NIMS data set. The data cover the NIMS observations of Jupiter, Io, Europa, Ganymede, and Callisto. Due to the heterogenous calibration of the data set, calibration information and calibration data available from the g-cubes labels are also stored in the database. The smallest element in the database is a spectrum (i.e. one pixel). Using SQL queries on the database, and criteria defined on the pixel viewing geometry (e.g. incidence, emission, phase, and azimuth) and the geographic pixel location (latitudes and longitudes on a given target), phase curves and/or collections of spectra can be easily retrieved from regions of interest. Individual g-cubes data can also be retrieved.

Future steps

We are currently integrating this framework within the ESA DataLabs compute platform, which provides a JupyterLab-based environment from which users will be able to easily access the database using Python notebooks. Future updates will incorporate the NIMS tubes data to the existing g-cubes in the database. Following the recent efforts from the scientific community to reanalyse and recalibrate the NIMS data, more recent recalibrated NIMS data sets [e.g. 4, 5] may be incorporated to the framework.

References  [1] Carlson et al., Space Science Reviews, 60, 457-502, 1992 ; [2] Howell and Pappalardo, Nat Commun 11, 1311, 2020 ; [3] Grasset et al., Plan Spac Sci 78, 1-21, 2013; [4] Malaska et al., 2023a, PDART program, DOI:10.17189/4sq6-x165 ; [5] Malaska et al., 2023b, PDART program, DOI:10.17189/4sz4-5024 

How to cite: Cornet, T., Cruz Mermy, G., Andrieu, F., Belgacem, I., and Schmidt, F.: Maximising the science return of the Galileo/NIMS dataset using a pixel-based database framework, EPSC-DPS Joint Meeting 2025, Helsinki, Finland, 7–13 Sep 2025, EPSC-DPS2025-1836, https://doi.org/10.5194/epsc-dps2025-1836, 2025.

The Planetary Data System (PDS) Small Bodies Node (SBN) is actively working to advance the FAIR (Findable, Accessible, Interoperable, and Reusable) characteristics of our data.  While current PDS4 models are well-suited for describing observational data, there is a need to better support the discovery of these datasets, especially higher-level, or “integrated” data sets.

High-level data sets, or “integrated data” sets, are typically the result of extensive data analysis and contain some of the most science-rich information.  These products usually are produced by combining multiple observations and even can be from multiple observing platforms or missions.  They include products such as digital terrain models, mosaic images, geologic maps, spectral feature maps, thermal maps, and crater databases.  Though rich in scientific value, these products can be difficult to discover in the current PDS4 system because a lack metadata that contains many of the parameters most scientists would typically use.

To address this need, SBN is developing a new "Findability" metadata dictionary designed to enrich PDS4 labels with attributes better suited for integrated products.  Most of the metadata currently being archived for PDS4 is limited and focused on describing the data for use.  There are only a few fields that are well suited for search.  This new dictionary emphasizes more abstract and science-driven parameters.  To maximize the finability of the data, we seek to identify a wide suite of characteristics to describe the data.  Our dictionary includes 21 different parameters that reflect different ways to think about the data to expand the conceptual coverage and aid discovery.

These new metadata parameters range within four broad categories of data

1 - What science discipline, including the technique of how the data is generated

2 - Characterization of the object of the data

3 - Typical subcategories of how the data is generally thought of

4 - What is the research context (i.e. what was the goal of the research)

By enabling the addition of these keywords and categories to dataset metadata, this new dictionary makes it possible to search and filter data using criteria that align more closely with how scientists frame their research questions.

We have begun building tools and web interfaces to aid in the generation of the data.  We provide a list of options to the data providers in order to quickly classify their data with the same questions that the eventual users would be working with.  Figures 1 and 2 illustrate examples of the type of user-friendly interface that this metadata enrichment could support.

Finally and most importantly, this work directly supports FAIR principles by enabling both human users and automated tools to more effectively identify and access the data results.  Once the PDS has this enhanced suite of metadata, then a greatly expanded suite of searches can be done.  This would result in a wide range of terms not only providing results from search queries, but these terms can be used to filter the results.  This would greatly reduce the friction in locating valuable higher-level datasets, creating a PDS archive that better serves the evolving needs of the planetary science community.

How to cite: Palmer, E., Lopez, K., and Drum, M.: Enhancing Discoverability in PDS:  A New Findability-Focused Approach to PDS4 Metadata., EPSC-DPS Joint Meeting 2025, Helsinki, Finland, 7–13 Sep 2025, EPSC-DPS2025-1845, https://doi.org/10.5194/epsc-dps2025-1845, 2025.

Introduction

A new version of the NASA Planetary Data System (PDS) Planetary Plasma Interactions (PPI) Node website (https://pds-ppi.igpp.ucla.edu/) was released in April 2024. This new website is designed to make PDS/PPI data holdings more findable, accessible, and reusable. In combination with PDS4 migration efforts, which include translating the data to more interoperable formats, the new website presents a major step forward in improving the FAIRness of the PDS/PPI data archive.

PDS/PPI Archive Web Access

The Planetary Plasma Interactions Node has been part of the Planetary Data System since its formation in 1989. PPI has maintained a public webpage for most of that period. PPI data have been web accessible dating back to the mid-1990’s.

The most recent iteration of the PPI webpage was officially released in April 2024. This version is designed to take better advantage of PDS4 metadata and organizational capabilities, including enhanced metadata, logical archive browsing, and discipline level extensions to the PDS4 Information Model.

PPI Website API and Tool Support

PPI has implemented a number of multidisciplinary APIs to enhance access to its data holdings. These include instances of the Heliophysics Application Programming Interface (HAPI), and the EuroPlanet-Table Access Protocol (EPN-TAP). These APIs make PPI data readily accessible to software applications such as Autoplot and TOPCAT.

The PPI website also offers a number of internal web tools and services. These include VISTA, a web visualization utility, and CSV and VOTable download options.

Conclusion

Efforts to ensure the FAIRness of PDS/PPI’s data holdings continue. Improvements to the web interface and new features and functionality continue to be added. The parallel effort to provide versions of PPI data in more user accessible formats also continues. Users are invited to provide feedback.

How to cite: Mafi, J. N. and Moon, I. S.: Updates to the PDS/PPI Website, EPSC-DPS Joint Meeting 2025, Helsinki, Finland, 7–13 Sep 2025, EPSC-DPS2025-2089, https://doi.org/10.5194/epsc-dps2025-2089, 2025.