Published December 21, 2024 | Version v1.2.0

Dataset for the manuscript "Attribute Recognition: A New Method for Grouping Planetary Images by Visual Characteristics, Using the Example of Mn-Rich Rocks in the Floor of Gale Crater, Mars."

  • 1. ROR icon Los Alamos National Laboratory
  • 2. ROR icon Yale University
  • 3. ROR icon University of Hawaiʻi at Mānoa
  • 4. ROR icon Northeastern University
  • 5. ROR icon Purdue University West Lafayette
  • 6. ROR icon Space Science Institute
  • 7. ROR icon Lyon 1 Université
  • 8. ROR icon United States Geological Survey
  • 9. ROR icon Deutsches Zentrum für Luft- und Raumfahrt e. V. (DLR)
  • 10. ROR icon Institut de Recherche en Astrophysique et Planétologie

Description

This dataset supports the manuscript "Attribute Recognition: A New Method for Grouping Planetary Images by Visual Characteristics, Using the Example of Mn-Rich Rocks in the Floor of Gale Crater, Mars." The dataset is contained in a single CSV file with 201 data rows (one row per NASA Curiosity rover ChemCam instrument target used in the study). The columns in this dataset include the martian solar day (sol) on which each target was imaged by ChemCam; the standoff distance from ChemCam to each target (in meters); binary columns (values are either 1 or 0, indicating presence or absence, respectively) for each of the 17 visual attributes we documented for each target image; the corresponding greyscale ChemCam RMI mosaic file location (on the Planetary Data System); and columns indicating which group each target was sorted into under each classification algorithm discussed in the text (P_{SG}: simple graph method; P_{AP}: automatic partitioning method; P_{\lambda=1.6}: community detection method with \lambda=1.6). To obtain the binary strings used for the classification algorithms, the 17 visual attribute columns can be concatenated. 

Also included is a collection of HTML files that enables easy viewing of the RMI mosaics in each cluster, using the Planetary Data System links. To use it, download the .zip file, unzip it, and open the index.html file in the browser of your choice (likely will work to simply double-click index.html)

Files

essunfeld-RMI-classification-dataset.csv

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