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Dramatic improvements in sensor and image acquisition technology have created a demand for automated tools that can aid in the analysis of large image databases. We describe the development of JARtool, a trainable software system that learns to recognize volcanoes in a large data set of Venusian imagery. A machine learning approach is used because it is(More)
Global analysis of NASA Magellan image data indicates that a major concentration of volcanic centers covering approximately 40 percent of the surface of Venus occurs between the Beta, Atla, and Themis regiones. Associated with this enhanced concentration are geological characteristics commonly interpreted as rifting and mantle upwelling. Interconnected low(More)
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