Introduction
Evaluating the quality and accuracy of geospatial data is one of the most important topics among geospatial data users. Geospatial data are used for diverse applications, including engineering and infrastructure positioning applications. Knowing how accurate the measurements that are derived from geospatial data can be a matter of life or death in some applications, like inaccurately locating a gas pipeline by an excavation team. In this lesson, you will be introduced to various statistical concepts that are related to determining geospatial data accuracy. You will also learn about the latest map accuracy standards designed for digital geospatial data published by the American Society of Photogrammetry and Remote Sensing (ASPRS).
Learning Objectives
At the successful completion of this lesson, you should be able to:
- understand basic statistical terms used to express product accuracy.
- understand errors in geospatial data.
- understand different types of accuracy.
- differentiate between different errors in geospatial data.
- describe factors affecting geospatial products accuracy.
- practice accuracy computations.
- understand The ASPRS positional accuracy standards.
Lesson Readings
- ASPRS Positional Accuracy Standards for Digital Geospatial Data, Edition 2, version 2 (2024)
- ASPRS Highlight Article “Best Practices in Evaluating Geospatial Mapping Accuracy according to the New Mapping Accuracy according to the New ASPRS Accuracy Standards”
- ASPRS Highlight Article “Overview of the ASPRS Positional Accuracy Standards for Digital Geospatial Data EDITION 2, VERSION 2 (2024)”
Lesson Activities
- Study lesson 8 materials on CANVAS/Drupal and the text books chapters assigned to the lesson
- Complete quiz 8
- Submit your COA Application
- Complete your discussions for the assignment on "FAA Road map"
- Complete your discussions for the assignment on "Differences Between Rules and Regulations"
- Attend the weekly call on Thursday evening at 8:00pm ET
- Practice computing products accuracy for each of the three data processing exercises.