GEOG 761 : Special Topic: Monitoring Change from Space with Machine Learning
Science
2025 Semester Two (1255) (15 POINTS)
Course Prescription
Course Overview
The use of remotely sensed data (satellite) with machine learning techniques to classify and analyse both commercial and environmental targets through time. Techniques will focus on both pixel classification and object detection. The primary platform for this will be the Python programming language and/or Google Earth Engine, with collaborative projects managed through git. Students will experience the latest in satellite imagery analysis with a focus on deriving actionable information. No formal prerequisites are required but it is highly advised for students to be comfortable with basic programming concepts and use. Students will gain a set of skills that will allow them to apply remote sensing data to a range of problem sets that they may meet in both academia and industry. Problem sets will be derived from both those set by the instructor and any which interest those taking part in the course. The course has a strong emphasis on teamwork and collaborative problem-solving. The use of AI as a tool to enable rapid implementation is explored and taught as part of this course.
Course Objectives:
(1) To understand the fundamental concepts and theories of machine learning algorithms as applied to satellite data.
(2) To learn how to use satellite data within a programmatic context in order to apply machine learning packages.
(3) To be able to examine a problem, select the appropriate data and interrogative approach, producing an analytical outcome.
(4) To develop skills in the presentation of code and analysis for both specialist and non-specialist audiences.
(5) To gain skills in small team leadership and management for scientific problem solving.
Course Structure: This course consists of scheduled lectures during which students can expect to engage with both the instructor and their peers throughout. Lectures and labs take place in the computer laboratory in which students will learn how to make use of relevant systems/packages, run the analyses taught in class and to interpret the results with the aim of producing actionable information for change. Project work will consist of small teams (2-3) working together to produce an analytical pipeline which they then write up and present to the course in an advocative manner (i.e., make the case).
In addition, relevant readings on topics will be provided for those who would like to gain in-depth knowledge on the topic throughout the semester.
Capabilities Developed in this Course
| Capability 1: | People and Place |
| Capability 2: | Sustainability |
| Capability 3: | Knowledge and Practice |
| Capability 4: | Critical Thinking |
| Capability 5: | Solution Seeking |
| Capability 6: | Communication |
| Capability 7: | Collaboration |
| Capability 8: | Ethics and Professionalism |
Learning Outcomes
- Understand satellite data and its unique properties (Capability 3, 4 and 5)
- Understand how different machine learning algorithms can be used to extract information from satellite images (Capability 3, 4 and 5)
- Apply a programmatic approach to satellite data analysis in a manner that allows reproducibility and teamwork (Capability 3, 4 and 5)
- Undertake a supervised study on a selected problem set within a small team context (Capability 1, 2, 3, 4, 5, 6, 7 and 8)
- Explain and communicate results to an audience in both oral and in written form in order to advocate for change (Capability 1, 3, 4, 6 and 7)
- Manage both self and teammates in the completion of a challenging technical task (Capability 6, 7 and 8)
Assessments
| Assessment Type | Percentage | Classification |
|---|---|---|
| Seminar | 10% | Individual Coursework |
| Project | 40% | Group Coursework |
| Laboratories | 50% | Individual Coursework |
| 3 types | 100% |
| Assessment Type | Learning Outcome Addressed | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 2 | 3 | 4 | 5 | 6 | |||||
| Seminar | ||||||||||
| Project | ||||||||||
| Laboratories | ||||||||||
Key Topics
Satellite data analysis, machine learning, remote sensing, change, processing at scale, cloud computing.
Tuākana
Workload Expectations
This course is a standard 15-point course and students are expected to spend 10 hours per week involved in each 15-point course that they are enrolled in.
For this course, you can expect 24 hours of lectures, a two-hour lab each week (24h), 15 hours of reading and thinking about the content and 87 hours of work on assignments.
Delivery Mode
Campus Experience
Attendance is expected at scheduled classes including lectures and laboratories/tutorials to complete components of the course. Lectures will be available as recordings, other learning activities including laboratories/tutorials will be available as self-led tutorials. Full engagement with the teamwork aspects of the course is essential for both your own learning and those of your colleagues.
The activities for the course are scheduled as a standard weekly timetable.
Learning Resources
Course materials are made available in a learning and collaboration tool called Canvas which also includes reading lists and lecture recordings (where available).
Please remember that the recording of any class on a personal device requires the permission of the instructor.
Student Feedback
During the course Class Representatives in each class can take feedback to the staff responsible for the course and staff-student consultative committees.
At the end of the course students will be invited to give feedback on the course and teaching through a tool called SET or Qualtrics. The lecturers and course co-ordinators will consider all feedback.
Your feedback helps to improve the course and its delivery for all students.
Academic Integrity
The University of Auckland will not tolerate cheating, or assisting others to cheat, and views cheating in coursework as a serious academic offence. The work that a student submits for grading must be the student's own work, reflecting their learning. Where work from other sources is used, it must be properly acknowledged and referenced. This requirement also applies to sources on the internet. A student's assessed work may be reviewed for potential plagiarism or other forms of academic misconduct, using computerised detection mechanisms.
Class Representatives
Class representatives are students tasked with representing student issues to departments, faculties, and the wider university. If you have a complaint about this course, please contact your class rep who will know how to raise it in the right channels. See your departmental noticeboard for contact details for your class reps.
Copyright
The content and delivery of content in this course are protected by copyright. Material belonging to others may have been used in this course and copied by and solely for the educational purposes of the University under license.
You may copy the course content for the purposes of private study or research, but you may not upload onto any third party site, make a further copy or sell, alter or further reproduce or distribute any part of the course content to another person.
Inclusive Learning
All students are asked to discuss any impairment related requirements privately, face to face and/or in written form with the course coordinator, lecturer or tutor.
Student Disability Services also provides support for students with a wide range of impairments, both visible and invisible, to succeed and excel at the University. For more information and contact details, please visit the Student Disability Services’ website http://disability.auckland.ac.nz
Special Circumstances
If your ability to complete assessed coursework is affected by illness or other personal circumstances outside of your control, contact a member of teaching staff as soon as possible before the assessment is due.
If your personal circumstances significantly affect your performance, or preparation, for an exam or eligible written test, refer to the University’s aegrotat or compassionate consideration page https://www.auckland.ac.nz/en/students/academic-information/exams-and-final-results/during-exams/aegrotat-and-compassionate-consideration.html.
This should be done as soon as possible and no later than seven days after the affected test or exam date.
Learning Continuity
In the event of an unexpected disruption we undertake to maintain the continuity and standard of teaching and learning in all your courses throughout the year. If there are unexpected disruptions the University has contingency plans to ensure that access to your course continues and your assessment is fair, and not compromised. Some adjustments may need to be made in emergencies. You will be kept fully informed by your course co-ordinator, and if disruption occurs you should refer to the University Website for information about how to proceed.
The delivery mode may change depending on COVID restrictions. Any changes will be communicated through Canvas.
Student Charter and Responsibilities
The Student Charter assumes and acknowledges that students are active participants in the learning process and that they have responsibilities to the institution and the international community of scholars. The University expects that students will act at all times in a way that demonstrates respect for the rights of other students and staff so that the learning environment is both safe and productive. For further information visit Student Charter https://www.auckland.ac.nz/en/students/forms-policies-and-guidelines/student-policies-and-guidelines/student-charter.html.
Disclaimer
Elements of this outline may be subject to change. The latest information about the course will be available for enrolled students in Canvas.
In this course you may be asked to submit your coursework assessments digitally. The University reserves the right to conduct scheduled tests and examinations for this course online or through the use of computers or other electronic devices. Where tests or examinations are conducted online remote invigilation arrangements may be used. The final decision on the completion mode for a test or examination, and remote invigilation arrangements where applicable, will be advised to students at least 10 days prior to the scheduled date of the assessment, or in the case of an examination when the examination timetable is published.