Published September 21, 2023 | Version 0.0.1
Software Open

Python code for the manuscript "Automatic characterization of boulders on planetary surfaces from high-resolution satellite images"

  • 1. Stanford University, University of Oslo
  • 2. Stanford University
  • 3. Ponoma University
  • 4. Arizona State University
  • 5. Medvedev Consulting
  • 6. Technion
  • 7. University of Oslo
  • 8. Sun-Yat-Sen University

Description

Python codes for the manuscript "Automatic characterization of boulders on planetary surfaces from high-resolution satellite images" at the moment of publication. For more information and continuously up-to-date versions of those repositories, please refer to https://github.com/astroNils. 

The archive includes three GitHub repositories associated with the manuscript: rastertools, shptools and MLtools.

 

 

Files

MLtools-v0.0.0.1.zip

Files (16.7 MB)

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md5:366893a17eca3a85853fa2b9b20fd8e7
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md5:30fe22a9f69e1fd2bde3a3141231e51b
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Additional details

Funding

European Commission
BOULDERING – A Deep Learning approach for boulder detection –The key to understand planetary surfaces evolution and their crater statistics-based ages 101030364