All Topics

A B C D E F G H I K L M N O P R S T U V W
DC-10 - Nature of aerial photograph data
  • Differentiate oblique and vertical aerial imagery
  • Describe the location and geometric characteristics of the “principal point” of an aerial image
  • Recognize the distortions and implications of relief displacement and radial distortion in an aerial image
  • Explain the phenomenon that is recorded in an aerial image
  • Compare and contrast digital and photographic imaging
  • Explain the significance of “bit depth” in aerial imaging
DC-16 - Nature of multispectral image data

A multispectral image comprises a set of co-registered images, each of which captures the spatially varying brightness of a scene in a specific spectral band, or electromagnetic wavelength region. An image is structured as a raster, or grid, of pixels. Multispectral images are used as a visual backdrop for other GIS layers, to provide information that is manually interpreted from images, or to generate automatically-derived thematic layers, for example through classification. The scale of multispectral images has spatial, spectral, radiometric and temporal components. Each component of scale has two aspects, extent (or coverage), and grain (or resolution). The brightness variations of an image are determined by factors that include (1) illumination variations and effects of the atmosphere, (2) spectral properties of materials in the scene (particularly reflectance, but also, depending on the wavelength, emittance), (3) spectral bands of the sensor, and (4) display options, such as the contrast stretch, which affect the visualization of the image. This topic review focuses primarily on optical remote sensing in the visible, near infrared and shortwave infrared parts of the electromagnetic spectrum, with an emphasis on satellite imagery.  

AM-05 - Neighborhoods
  • Discuss the role of Voronoi polygons as the dual graph of the Delaunay triangulation
  • Explain how Voronoi polygons can be used to define neighborhoods around a set of points
  • Outline methods that can be used to establish non-overlapping neighborhoods of similarity in raster datasets
  • Create proximity polygons (Thiessen/Voronoi polygons) in point datasets
  • Write algorithms to calculate neighborhood statistics (minimum, maximum, focal flow) using a moving window in raster datasets
  • Explain how the range of map algebra operations (local, focal, zonal, and global) relate to the concept of neighborhoods
FC-19 - Networks defined
  • Define different interpretations of “cost” in various routing applications
  • Describe networks that apply to specific applications or industries
  • Create a data set with network attributes and topology
  • Define the following terms pertaining to a network: Loops, multiple edges, the degree of a vertex, walk, trail, path, cycle, fundamental cycle
AM-63 - Non-linearity relationships and non-Gaussian distributions
  • Understand how some machine learning methods might be more adept at modeling or representing such distributions
  • Define non-linear and non-Gaussian distributions in a geospatial data environment
  • Exemplify non-linear and non-Gaussian distributions in a geospatial data environment