Applications in GIS Module 3

This weeks lab was formatted differently then previous modules. The assignment required that four Esri learning courses be completed with minimum of 80% on each of the final quizzes. The courses required were as follows: Introduction to 3D Visualization, Performing Line of Sight Analysis, Performing Viewshed Analysis in ArcGIS Pro, Sharing 3D Content Using Scene Layer Packages. The overall theme in this module was the use of 3D layers within a local or global scene. In manipulating 3D data the most used tool was Feature To 3D By Attribute, which converted 2D data to 3D using a specific attribute. For our purposes the Z attribute was used to determine the height of the 3D symbol. The Create 3D Object Scene Layer Package tool was used to package and export the scene for publishing. For Line of Sight analysis Construct Sight Lines was used to draw lines from an observer to the target feature. These lines were then run through the Line of Sight tool to determine which positions on the target feature were visible from the observation point. For viewshed analysis the attribute table was modified with offset, azimuth, and radius data then ran through the Viewshed tool to determine what terrain points were visible from the observation point. The height points were manipulated and it was ran again to achieve the desired results.

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Module 5 - Unsupervised and Supervised Image Classification

This weeks module focused on classifying images using multispectral signatures. Above you can see the completed classified land cover of Germantown, Maryland. To create this image above signatures were collected that correlated to each required feature. Then bands were chosen (R:4 G:6 B:5) that contained the largest separation amongst features to minimize spectral confusion. In the above image roads and urban areas were often confused leading to a much larger area of roads than actually exist. The inset map contains a classification distance map which displays the distance each cell is (spectrally) from the sample points with brighter pixels being further than darker pixels. This indicates that brighter areas have a higher chance of error.

Lab 3: M 1.3 Assessment

Lab 2: M1.2 Standards

The task for this module was to perform an accuracy assessment on two versions of the same road network in Albuquerque. One was mapped by the city of Albuquerque (yellow) and one by Street Map USA (red). In order to perform the accuracy assessment twenty points per network needed created at matching intersections on each network. Then a reference point was digitized based on satellite imagery to represent the "actual" location for each intersection. XY coordinates were assigned to all sixty of the created points and the values were exported to an excel spreadsheet. From there the RMSE for each of the networks were calculated using the reference points. With this the accuracy assessment was completed resulting in the following: ABQ_Streets Positional Accuracy: Tested 14.84 feet horizontal accuracy at 95% confidence level. StreetMapUSA Positional Accuracy: Tested 158.56 feet horizontal accuracy at 95% confidence level.

GIS Portfolio - Michael Malone

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