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Large datasets of sub-meter aerial imagery represented as orthophoto mosaics are widely available today, and these data sets may hold a great deal of untapped information. This imagery has a

Large datasets of sub-meter aerial imagery represented as orthophoto mosaics are widely available today, and these data sets may hold a great deal of untapped information. This imagery has a potential to locate several types of features; for example, forests, parking lots, airports, residential areas, or freeways in the imagery. However, the appearances of these things vary based on many things including the time that the image is captured, the sensor settings, processing done to rectify the image, and the geographical and cultural context of the region captured by the image.

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    Date Created
    • 2016
    Resource Type
  • Text
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    Note
    • Partial requirement for: M.S., Arizona State University, 2016
      Note type
      thesis
    • Includes bibliographical references (pages 35-40)
      Note type
      bibliography
    • Field of study: Computer science

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    by Nagesh Kumar Uba

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