• Two Data Types

  • Vector Data
  • Raster/imagary Data

• Georeferencing

• On-screen Digitizing: Creating Vector Data

• A raster consists of a matrix of pixels (or cells) organized into rows and columns (or a grid) in which each pixel contains a value representing information.
• Raster data are collected by aircraft, drones, satellites, ground and water-based sensors, digital pictures, and scanned maps.
• Also includes aerial photographs and satellite imagery. File types: geoTIFF, TIFF, JPG, PNG, GIF, BMP, and other imagery file formats.

• Two types of raster data: continuous and discrete
• Three types of raster datasets: thematic data, spectral data, and pictures (imagery).

• Rasters are well suited for representing data that changes continuously across a landscape (surface).
• They provide a method of storing the continuity as a surface. They also provide a regularly spaced representation of surfaces.

• Rasters representing thematic data can be derived from analyzing other data.
• A common analysis application is classifying a satellite image into land-cover categories.

• A common use of image data in a GIS is as an image background for other feature layers.
• For example, orthoimages that are displayed under other GIS layers allow map users to confirm that map layers are spatially aligned and represent real objects, as well as provide additional contextual information.

• A simple data structure
• A format for advanced spatial and statistical analysis
• Represent continuous image data, surfaces, and perform scientific analysis
• Speed and Storage and Collect
• Perform fast overlays with complex datasets

• Spatial inaccuracies exist due to the limits imposed by the raster dataset cell dimensions
• Loss of geometric precision accompanies restructuring data to a regularly spaced raster-cell boundary.
• Raster datasets can be very large. Resolution increases as the size of the pixel decreases.

• Each pixel has a value.
• Pixel values can be either positive or negative, integer, or floating point.
• The area (or surface) represented by each pixel consists of the same width and height and is an equal portion of the entire surface represented by the image.

• The pixel size determines how coarse or fine the patterns or objects in the image appear.

• The location of each pixel is defined by the row or column where it is located in the raster matrix.

• The extent of an image is defined by the top, bottom, left, and right coordinates of the rectangular area covered by the image.

• A coordinate system
• A reference coordinate or x,y location (typically the upper left or lower left corner of the image)
• A pixel size
• The count of rows and columns

• When you georeference your raster data, you define its location using map coordinates and assign the coordinate system of the map frame.

• Georeferencing raster data allows it to be viewed, queried, and analyzed with your other geographic data.

• Georeference your raster dataset using existing spatial data (target data), such as a vector feature class, that resides in the desired map coordinate system.

• The process involves identifying a series of ground control points that link locations on the raster dataset with locations in the target data

• If possible, you should spread out the links over the entire raster dataset rather than concentrating them in one area - adding more links will not necessarily yield a better registration.

• Generally, the greater the overlap between the raster dataset and target data, the better the alignment results

• The polynomial transformation uses a polynomial built on control points and a least-squares fitting (LSF) algorithm. It is optimized for global accuracy but does not guarantee local accuracy. [1]
• The adjust transformation optimizes for both global LSF and local accuracy. It is built on an algorithm that combines a polynomial transformation and triangulated irregular network (TIN) interpolation techniques. [3]

• The similarity transformation is a first order transformation which tries to preserve the shape of the original raster. [3]
• The projective transformation can warp lines so that they remain straight. The projective transformation is especially useful for oblique imagery, scanned maps, and for some imagery products such as Landsat and Digital Globe. [4]
• The spline transformation is a true rubber sheeting method and optimizes for local accuracy but not global accuracy. [10]

• A zero-order polynomial is used to shift your data.

• The first-order polynomial transformation is commonly used to georeference an image. Use a first-order or affine transformation to shift, scale, and rotate a raster dataset.

• By receiving signals from navigational satellites – GPS (x, y coordinates)
• By “Digitizing” – Tracing lines from maps and drawings
• By Scanning maps into the computer and registering the information to the correct locations
• Automated vectorization – converts a raster file into vector objects(ArcScan)
• Creating data from pre-existing data: Geoprocessing
• Creating data from attribute information: Geocoding/Address matching
• Through “heads up” (on-screen) digitizing - Create vectors from raster layers (DOQs, aerials, scanned images) directly off a computer screen

Overview of georeferencing