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Effective classification for crater detection: A case study on Mars.

, , , , , , , , and . IEEE ICCI, page 688-695. IEEE Computer Society, (2010)

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Global assessment and mapping of changes in mesoscale landscapes: 1992-2015., , and . Int. J. Appl. Earth Obs. Geoinformation, (2019)An efficient approach to external cluster assessment with an application to martian topography., , and . Data Min. Knowl. Discov., 14 (1): 1-23 (2007)Semi-supervised based active class selection for automatic identification of sub-kilometer craters., , and . ISPA, page 615-620. IEEE, (2011)Machine Learning for Automatic Mapping of Planetary Surfaces., , and . AAAI, page 1807-1812. AAAI Press, (2007)Towards machine ecoregionalization of Earth's landmass using pattern segmentation method., and . Int. J. Appl. Earth Obs. Geoinformation, (2018)Spatial approach to analyzing dynamics of racial diversity in large U.S. cities: 1990-2000-2010., and . Comput. Environ. Urban Syst., (2018)Digital topography models for Martian surfaces., and . IEEE Geosci. Remote. Sens. Lett., 2 (3): 260-264 (2005)Pattern-based identification and mapping of landscape types using multi-thematic data., and . Int. J. Geogr. Inf. Sci., 35 (8): 1634-1649 (2021)Automatic Recognition of Landforms on Mars Using Terrain Segmentation and Classification., , and . Discovery Science, volume 4265 of Lecture Notes in Computer Science, page 255-266. Springer, (2006)Automatic detection of craters in planetary images: an embedded framework using feature selection and boosting., , , , , , and . CIKM, page 749-758. ACM, (2010)