The Multi-Temporal Database of Planetary Image Data (Muted): Interactive Visualization of Datasets for the Moon, Mars, and Mercury

THE MULTI-TEMPORAL DATABASE OF PLANETARY IMAGE DATA (MUTED): INTERACTIVE VISUALIZATION OF DATASETS FOR THE MOON, MARS, AND MERCURY. T. Heyer 1, H. Hiesinger1, N. Schmedemann1, and W. Iqbal1, 1Institut für Planetologie, Universität Münster, Wilhelm-KlemmStr. 10, 48149 Münster, Germany (thomas.heye[email protected]e) Introduction: The Multi-Temporal Database of Planetary Image Data (MUTED) is a comprehensive web-tool to identify and access orbital images of planetary bodies, including Mars, the Moon, and Mercury. The database enables location-driven and data-driven image searches for the identification of multi-temporal images of the major space missions as a basis for diverse surface change analyses [1, 2]. MUTED is available at https:\\muted.unimuenster.de and will assist and optimize image data searches to support the analyses and understanding of short-term, long-term, and seasonal processes on planetary surfaces. In particular, images can be searched in temporal and spatial relation to other images on a global scale or for a specific region of interest. Tools and services: The multi-temporal database offers a variety of tools for selecting, visualizing, and processing planetary data. Users can explore planetary bodies such as Mars, the Moon, and Mercury using diverse global base maps, including spectral, topographic, and geologic information. In the map view, the planetary bodies are displayed as 3D spheres or in a Mercator map projection. Overlaid on these global base maps, users can visualize data from various planetary missions, either globally or within user-defined regions of interest. The data can be filtered by attributes such as acquisition date, pixel resolution, and incidence angle. Additional information, such as data acquisition time, temporal and spatial context, preview images, and links to download raw data, is available for each image. A unique feature of the database is the timeline tool, which allows users to explore the multitemporal coverage of planetary surfaces. Images within an area of interest can be displayed in chronological order, providing a quick overview of data availability and temporal context. This tool can be used, for example, to identify overlapping datasets with the shortest or longest time intervals or to examine the seasonal distribution of observations. Additionally, the database provides an advanced search function, allowing users to search across all integrated datasets. This enables users to locate specific nomenclature features, latitude-longitude coordinates, or images of various instruments. We developed a rendering tool that enables users to analyze the surfaces Mars, the Moon, and Mercury in more detail (Fig. 1). The tool loads tiled blocks of binary data from both global and regional datasets, efficiently managing large-scale planetary datasets by breaking them into smaller, manageable components. This approach minimizes memory consumption and accelerates data processing, making it suitable for real-time rendering in standard web browsers. Users can apply various color scales and dynamically stretch the data as required. Furthermore, datasets can be filtered using adjustable minimum and maximum thresholds, allowing users to highlight/hide specific data ranges. This function can be used to visualize both topographical data and spectral data. For example, areas of the same altitude can be identified using global elevation models. Fig. 1. The MUTED web interface showing the global LOLA DEM [3] with elevations scaled from -4000 m (blue) to 1000 m (red). White footprints indicate LROC NAC [4] stereo image coverage, highlighting areas for generating high-resolution DEM availability. Users can modify the transparency of individual layers and thus combine different layers for a detailed surface analysis. A newly implemented screenshot feature allows users to capture their current visualization as a PNG image. This screenshot can optionally include a scale bar and north arrow. Inegrated machine-learning based datasets: In addition to the global basemaps and instrument footprints, we have integrated a variety of global datasets generated using machine learning techniques [5-12]. These datasets provide a fast and efficient overview of the global distribution of specific features and landforms. Moreover, these features can be further analyzed by utilizing image data of different instruments. The integrated datasets include global catalogs for craters [5, 6], boulders [7], granular flows [8], rockfalls [9], chloride deposits [10], and pitted cones [11, 12] for Mars or the Moon. For each feature, additional metadata is provided, including geographic coordinates, feature diameter, the ID of the images in which they were detected, and preview images of the detections (Fig. 2), depending on the dataset. Furthermore, for some features, datasets from multiple authors have been integrated, enabling direct comparison and crossvalidation of results. Scientific Applications: MUTED has been utilized in various recent planetary studies of Mars and the Moon [13-25]. In particular, the database has been used to study recent changes on the martian surface, focusing on active landforms such as slope streaks [13-15] and gullies [17, 18]. Furthermore, the database has been used for data selection as a basis for diverse surface analyses, such as geological mapping [19-21] and crater counting [22] on the Moon and Mars. Upcoming datasets: Due to continuous data acquisition by spacecrafts, the amount of planetary image data is steadily increasing and enables further comprehensive analyses of planetary surface changes. Furthermore, the flexible structure of MUTED allows for a fast integration of upcoming datasets. Upcoming data from BepiColombo [23, 24], Europe’s first mission to Mercury will be integrated. Acknowledgements: This research has been funded and supported by the German Aerospace Agency (DLR) (Grant # 50 OO 2102). References: [1] Heyer et al. (2018) PSS, 159, 56-65. [2] Erkeling et al. (2016) PSS, 125, 43-61. [3] Smith et al. (2010) SSR, 150, 209. [4] Robinson et al. (2010) SSR, 150, 81. [5] Salamunićcar et al. (2012) PSS, 60, 1. [6] Wang et al. (2021) JGR, 126, 9. [7] Rüsch and Bickel (2023) PSJ, 4, 126. [8] Bickel et al. (2022) GRL, 49, 12. [9] Bickel et al. (2020) Nat. Com., 11, 2862. [10] Bickel et al. (2024) Nature Sci. Data, 11, 845. [11] Mills et al. (2024) Icarus, 418, 116145. [12] Chen et al. (2024) Nature Sci. Data, 11, 942. [13] Heyer et al. (2019) Icarus, 323, 76-86. [14] Heyer et al. (2020) Icarus, 351, 113951. [15] Heyer et al. (2024) Icarus, 423, 116270. [16] Vijayan et al. (2021) GRL, 49, 1. [17] Raack et al. (2020) Icarus, 350, 113899. [18] Sinha et al. (2020) Icarus, 352, 113979. [19] Iqbal et al. (2023) Icarus, 406, 115732. [20] Wueller et al. (2024) JGR, 49, 1. [21] Wueller et al. (2025) Icarus, 426, 116373. [22] Oetting et al. (2023) JGR, 128, 10. [23] Benkhoff et al. (2010) PSS, 58, 1-2, 2-20. [24] Hiesinger et al. (2010) PSS, 58, 1-2, 144-165. Fig. 2. The MUTED web interface showing a catalog of fragmented rocks [10] (cyan dots) on top of the LROC WAC mosaic [4] for an area at the Apollo 15 landing site. Detections based on LROC NAC [4] image data are presented in the preview panel on the left side.