2016 QUARTER 02

A B C D E F G H I K L M N O P R S T U V W
GD10-1 - Nature of aerial image 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
GD11-1 - Nature of multispectral image data
  • Explain the concepts of spatial resolution, radiometric resolution, and spectral sensitivity
  • Draw and explain a diagram that depicts the bands in the electromagnetic spectrum at which Earth’s atmosphere is sufficiently transparent to allow high-altitude remote sensing 
  • Illustrate the spectral response curves for basic environmental features (e.g., vegetation, concrete, bare soil)
  • Describe an application that requires integration of remotely sensed data with GIS and/or GPS data
  • Explain the concept of “data fusion” in relation to remote sensing applications in GIS&T
  • Draw and explain a diagram that depicts the key bands of the electromagnetic spectrum in relation to the magnitude of electromagnetic energy emitted and/or reflected by the Sun and Earth across the spectrum
AM4-3 - 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
AM11-1 - 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
GC2-9 - Neural network schemes
  • Appraise the relative value of neural networks or alternative inductive machine learning methods, such as decision trees or genetic classifiers, in a hypothetical or real case
  • Evaluate the success of neural network schemes
  • Implement a neural network classification scheme for a complex data set
GC2-3 - 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