Module Information
Course Delivery
Assessment
| Assessment Type | Assessment length / details | Proportion |
|---|---|---|
| Semester Assessment | Laboratory Notebook Students will submit a laboratory notebook from the practical sessions for the regression analysis annotating the analysis they having undertaken to demonstrate their interpretation of the results and understanding of the methods applied. 1000 Words | 30% |
| Semester Assessment | Journal Article Students will apply a habitat suitability analysis with appropriate input datasets, with one provided through the application of a machine learning based classification, for a given topic. 4000 Words | 70% |
| Supplementary Assessment | Laboratory Notebook Students will submit a laboratory notebook from the practical sessions for the regression analysis annotating the analysis they having undertaken to demonstrate their interpretation of the results and understanding of the methods applied. 1000 Words | 30% |
| Supplementary Assessment | Journal Article Students will apply a habitat suitability analysis with appropriate input datasets, with one provided through the application of a machine learning based classification, for a given topic. 4000 Words | 70% |
Learning Outcomes
On successful completion of this module students should be able to:
Demonstrate familiarity with the use of machine learning techniques used to undertake a land cover classification
Show awareness of how to appropriately assess the accuracy of a classification product.
Be able to appropriately apply a habitat suitability analysis and select input datasets
Demonstrate familiarity with the use of machine learning techniques to solve regression problems using geospatial data.
Critically evaluate machine learning methodologies to assess whether they are suitable for the task in hand.
Design and implement a research plan to solve a given problem
Present findings in a concise, informative and professional manner
Brief description
This module aims to provide students with the experience and knowledge to apply machine learning and artificial intelligence techniques to solve problems using geospatial data such as those from Earth Observation (EO) and Geographical Information Systems (GIS). This module will also provide students with further opportunities to apply scripting and automation to the analysis of geospatial data. For Geospatial data analysis, these techniques are becoming more essential, and a good understanding of these methods and techniques is vital for opportunities within the job market.
Aims
This module aims to provide students with the experience and knowledge to apply machine learning and artificial intelligence techniques to solve problems using geospatial data such as those from Earth Observation (EO) and Geographical Information Systems (GIS).
Content
The module will consider key techniques such as:
• The application of machine learning for classification problems
• How we appropriately assess the accuracy of our classification results
• How we can map change and assess the accuracy of change.
• Modelling habitats using habitat suitability modelling
• Applying machine learning to predict continuous variables through regression
Module Skills
| Skills Type | Skills details |
|---|---|
| Creative Problem Solving | Independently using the methods taught to solve the problems set for the assessments |
| Critical and analytical thinking | Appropriate interpretation and further analysis of the results of a data processing task |
| Digital capability | Using machine learning techniques to solve problems and scripting solution to automate analysis |
| Professional communication | Writing to a scientific audience |
| Subject Specific Skills | Appropriately using and understanding geospatial data to solve problems |
Notes
This module is at CQFW Level 7