Course Title: Conduct advanced remote sensing analysis
Part B: Course Detail
Teaching Period: Term2 2023
Course Code: GEOM5194C
Course Title: Conduct advanced remote sensing analysis
Important Information:
Please note that this course may have compulsory in-person attendance requirements for some teaching activities.
To participate in any RMIT course in-person activities or assessment, you will need to comply with RMIT vaccination requirements which are applicable during the duration of the course. This RMIT requirement includes being vaccinated against COVID-19 or holding a valid medical exemption.
Please read this RMIT Enrolment Procedure as it has important information regarding COVID vaccination and your study at RMIT: https://policies.rmit.edu.au/document/view.php?id=209.
Please read the Student website for additional requirements of in-person attendance: https://www.rmit.edu.au/covid/coming-to-campus
Please check your Canvas course shell closer to when the course starts to see if this course requires mandatory in-person attendance. The delivery method of the course might have to change quickly in response to changes in the local state/national directive regarding in-person course attendance.
School: 530T Built Environment and Sustainability
Campus: City Campus
Program: C6175 - Advanced Diploma of Surveying
Course Contact: Matthew Sweeney
Course Contact Phone: +61 3 9925 4105
Course Contact Email: matthew.sweeney@rmit.edu.au
Name and Contact Details of All Other Relevant Staff
Greg Patterson
Spatial and Surveying Teacher
School of Vocational Engineering, Health & Science
RMIT University, MELBOURNE 3001
Victoria, Australia
Email: gregory.patterson@rmit.edu.au
Nominal Hours: 120
Regardless of the mode of delivery, represent a guide to the relative teaching time and student effort required to successfully achieve a particular competency/module. This may include not only scheduled classes or workplace visits but also the amount of effort required to undertake, evaluate and complete all assessment requirements, including any non-classroom activities.
Pre-requisites and Co-requisites
None
Course Description
In this course you will develop the skills and knowledge required to conduct advanced remote sensing analysis on digital imagery. This includes using software and image processing systems to perform the required image enhancements, manipulations and analysis. The course also includes performing supervised and unsupervised classifications on datasets and conducting related error analysis.
This course is suitable for surveyors and skilled spatial information system (SIS) technicians who use broad theoretical and technical knowledge to analyse information as well as interpret and provide solutions to unpredictable and sometimes complex surveying/spatial information problems. The course supports those who work in a technical management role in a spatial information services team, in areas such as cartography, town planning and mapping.
National Codes, Titles, Elements and Performance Criteria
National Element Code & Title: |
CPPSSI6037 Conduct advanced remote sensing analysis |
Element: |
1. Plan remote sensing analysis |
Performance Criteria: |
1.1 Analyse project specifications and determine appropriate remote sensing analysis methods. 1.2 Select suitable image sources according to project specifications. 1.3 Identify suitable images and examine metadata to meet project specifications. 1.4 Obtain image data required to meet project specifications. 1.5 Assess constraints of use of remote sensing data and plan contingencies to meet project requirements. 1.6 Apply legislative and organisational requirements for accessing and using spatial data. |
Element: |
2. Analyse image using spectral indices |
Performance Criteria: |
2.1 Perform radiometric correction on image, including converting images to reflectance or radiance values to enhance quality of image data. 2.2 Apply spectral indices to image data and interpret results. |
Element: |
3. Analyse image using image classification algorithms |
Performance Criteria: |
3.1 Apply convolution matrices to enhance quality of image data. 3.2 Determine information classes required according to project specifications. 3.3 Create training samples for required information classes. 3.4 Evaluate training areas and create spectral signature file. 3.5 Apply supervised classification algorithms to signature file. 3.6 Conduct error analysis to calculate approximate accuracy of classification. 3.7 Interpret results according to project specifications. |
Element: |
4. Document image analysis |
Performance Criteria: |
4.1 Write up the methodology used to compile and analyse image data. 4.2 Write up interpretation of results, noting accuracy and limitations. 4.3 Present results in graphical, tabular or map format according to project requirements. |
Learning Outcomes
On successful completion of this course you will have developed and applied the skills and knowledge required to demonstrate competency in the above elements.
Details of Learning Activities
You will learn to:
- Determine project requirements
- Identify key spatial references
- Combine remote sensing imagery bands
- Use remote sensing software to determine healthy vegetation
- Interpret and evaluate results using error analysis
The total number of scheduled hours of teaching, learning and assessment involved in this course includes all planned activities (face to face classes, lectures, workshops and seminars; workplace visits, online learning and other forms of structured teaching and learning). It also covers the amount of effort necessary to undertake, evaluate and complete all assessment requirements, observation of work performance, discussions with supervisors and others providing third party evidence and one on one and group assessment sessions with students.
Teaching Schedule
Syllabus
The following syllabus provides you with this course's Training and Assessment schedule. Refer to this page to find out what themes will be discussed each week and when assessments are due. You will also find important information on census dates, excursions and practices. While we endeavour to deliver and assess in line with this syllabus, we reserve the right to make changes to accommodate unexpected circumstances.
Session/Date |
Theme |
Assessments |
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10-16 July |
Lesson Title: Introduction to the subject Description: During this session, the student will be provided with an overview of the assessment. The teacher will guide the students through the assessment documentation, explaining the requirements and expectations. Furthermore, software licenses will be distributed to the students, enabling them to install and utilize the required software on their personal computers. This will allow them to work on the assessment tasks conveniently from their home environment. In addition to the assessment materials, students will also be introduced to various supplementary training resources. These resources may include platforms like LinkedIn Training, where they can access additional training modules and materials to enhance their knowledge and skills beyond the scope of the assessment. |
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17-23 July |
Lesson Title: Introduction to Remote Sensing Description: In this session, students will be introduced to remote sensing and its terminology. The student will be required to answer a series of knowledge questions pertaining to the topic. By engaging with these concepts and answering a series of questions, students will cultivate a comprehensive understanding of remote sensing principles and practices. This understanding will empower them to effectively apply their knowledge in practical situations and make informed decisions when working with remote sensing data and imagery. |
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24-30 July |
Lesson Title: Initial meeting requirement with client Description: During this session, the student will be assigned an assessment task that involves participating in a meeting with the client. The purpose of the meeting is to engage in extensive discussions and document all project requirements, while also addressing a series of questions. The main objective of these discussions and documentation is to establish a clear and precise understanding of the project scope, ensuring alignment among all stakeholders. This meticulous process will facilitate effective planning and execution of the assessment task, ultimately leading to a successful outcome. By successfully completing these tasks, the student will demonstrate their proficiency in understanding and managing project requirements, as well as their ability to communicate effectively with clients and stakeholders. |
Assessment 1 Determine healthy vegetation using NDVI Section B – Marking Guide 1A |
|
31 July - 6 August |
Lesson Title: Determine suitable imagery Description: In this session, the student will receive an assessment task that involves the identification of suitable imagery through the examination of metadata, as well as obtaining and validating the associated imagery. In addition to these tasks, you will be responsible for documenting the URL from which you obtained the imagery and noting any constraints related to its usage. It is essential to identify potential contingencies in situations where the imagery is unsuitable or cannot be utilized due to legal reasons. This may require exploring alternative sources or approaches to ensure the project requirements are fulfilled. Furthermore, the student is expected to answer knowledge questions that demonstrate their understanding of selecting appropriate imagery. This will serve as evidence of their comprehension of the subject matter and their ability to apply theoretical concepts to practical scenarios. By successfully completing these tasks and showcasing their knowledge, the student will demonstrate their proficiency in identifying and validating suitable imagery for geospatial projects. |
Assessment 1 Determine healthy vegetation using NDVI Section B – Marking Guide 1B |
|
7-13 August |
Lesson Title: Analyse imagery using image classification algorithms to conduct NDVI – ArcGIS Pro Description: During this session, the students will be introduced to the process of determining healthy vegetation using the Normalized Difference Vegetation Index (NDVI). They will be required to obtain the necessary multispectral imagery and utilize ArcGIS Software, analyze the imagery and identify areas with healthy vegetation. Additionally, the students will document the process and answer a series of questions related to the topic. This exercise will enable them to reinforce their understanding and demonstrate their knowledge of the NDVI analysis for vegetation assessment. |
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14-20 August
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Lesson Title: Analyse imagery using image classification algorithms to conduct NDVI – Safe Software FME Description: During this session, the students will be introduced to the process of determining healthy vegetation using the Normalized Difference Vegetation Index (NDVI). They will be required to obtain the necessary multispectral imagery and utilize Safe Software FME to analyze the imagery and identify areas with healthy vegetation. Additionally, the students will document the process and answer a series of questions related to the topic. This exercise will enable them to reinforce their understanding and demonstrate their knowledge of the NDVI analysis for vegetation assessment. |
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21-27 August |
Lesson Title: Determine Healthy vegetation using Multispectral Imagery Description: During this session, the student will engage in an assessment task focused on determining healthy vegetation using the Normalized Difference Vegetation Index (NDVI) method. The task involves obtaining multispectral imagery and utilizing appropriate GIS software to analyze the imagery and identify areas with healthy vegetation. The student will be responsible for validating and processing the imagery according to specified requirements while documenting the employed process. Additionally, the student will need to answer a series of questions related to the topic. This assessment aims to evaluate the student's understanding of the NDVI method and their ability to apply it effectively in practical scenarios. Successful completion of these tasks will demonstrate the student's proficiency in determining healthy vegetation using the NDVI method. By successfully completing these tasks, the student will showcase their proficiency in analyzing multispectral imagery, applying the NDVI method, and interpreting the results to identify areas with healthy vegetation. |
Assessment 1 Determine healthy vegetation using NDVI Section B – Marking Guide 1C |
|
Mid-semester break 28 August - 3 September |
The Mid-semester break is a scheduled break in the semester. No teaching or assessment will occur during this time. Also, your Trainer/Assessor won't be available during this time. If you need to contact them, please email them via your student email account, and they will respond once they return from the break. Census date for Semester 2: 31 August |
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4-10 September |
Lesson Title: Interpret results from univariate and multivariate statistics Description: In this session, the student will engage in an assessment task focused on interpreting and creating histogram plots based on univariate and multivariate statistics. The task includes two main components: creating a pie chart to visualize the correlation range with percentage coverage, and generating a histogram plot to depict the correlation range and its corresponding percentage coverage. By completing these tasks, the student will demonstrate their ability to analyze statistical data, effectively represent correlations through visualizations, and interpret the information conveyed by the histogram plots. |
Assessment 1 Conduct error analysis to determine accuracy of results Section B – Marking Guide 1E |
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11-17 September |
Lesson Title: Conduct error analysis to determine accuracy of results Description: In this session, the student will be assigned an assessment task focused on analyzing and validating the percentages within a pie graph. The task requires comparing the percentage values depicted in the pie graph with the statistical analysis and the output map to ensure consistency and accuracy. The student will be responsible for validating processing the data according to specified requirements while documenting the employed process. Additionally, the student is required to interpret the output results and respond to questions concerning limitations that may impact the accuracy of the output. By successfully accomplishing this task, the student will exhibit their proficiency in critically evaluating data, identifying potential discrepancies or inconsistencies, and ensuring the integrity of graphical representations. This demonstrates their ability to assess and validate information effectively. |
Assessment 1: Finalise portfolio Section B – Marking Guide 1I |
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18-24 September |
Lesson Title: Finalise and submit project assessment Description: During this session, the student will be assigned an assessment task focused on finalizing all documentation. The completed portfolio is to be uploaded onto canvas
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25 September - 1 October |
Lesson Title: Initial meeting requirement with client Description: During this session, the student will be assigned an assessment task that involves participating in a meeting with the client. The purpose of the meeting is to engage in extensive discussions and document all project requirements, while also addressing a series of questions. The main objective of these discussions and documentation is to establish a clear and precise understanding of the project scope, ensuring alignment among all stakeholders. This meticulous process will facilitate effective planning and execution of the assessment task, ultimately leading to a successful outcome. By successfully completing these tasks, the student will demonstrate their proficiency in understanding and managing project requirements, as well as their ability to communicate effectively with clients and stakeholders.
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Assessment 2 Project specifications and documented requirements Section B – Marking Guide 1A |
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2-8 October |
Lesson Title: Determine suitable imagery Description: In this session, the student will receive an assessment task that involves the identification of suitable imagery through the examination of metadata, as well as obtaining and validating the associated imagery. In addition to these tasks, you will be responsible for documenting the URL from which you obtained the imagery and noting any constraints related to its usage. It is essential to identify potential contingencies in situations where the imagery is unsuitable or cannot be utilized due to legal reasons. This may require exploring alternative sources or approaches to ensure the project requirements are fulfilled. Furthermore, the student is expected to answer knowledge questions that demonstrate their understanding of selecting appropriate imagery. This will serve as evidence of their comprehension of the subject matter and their ability to apply theoretical concepts to practical scenarios. By successfully completing these tasks and showcasing their knowledge, the student will demonstrate their proficiency in identifying and validating suitable imagery for geospatial projects. |
Assessment 2 Determined suitability of imagery: Section B – Marking Guide 1B |
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9-15 October |
Lesson Title: Analyse imagery using image classification algorithms to conduct Supervised and Unsupervised classification – ArcGIS Pro Description: During this session, the students will learn about the process of conducting supervised and unsupervised classification using ArcGIS Pro software. The objective is to classify the obtained multispectral imagery and identify features such as Bare Earth, Vegetation (including forest areas, pine trees, and grass), and Hydrography features (such as dams, lakes, and reservoirs). The tasks assigned to the students include:
By successfully completing these tasks, the students will showcase their proficiency in conducting supervised and unsupervised classification using ArcGIS Pro software. They will demonstrate their ability to accurately classify the imagery and identify specific features based on their spectral characteristics. |
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16-22 October |
Lesson Title: Perform supervised classification Description: In this session, the student will participate in an assessment task focused on supervised classification using ArcGIS Pro software. The task entails obtaining multispectral imagery and utilizing ArcGIS Software to analyze the imagery and identify Bare Earth, Vegetation (Forest areas, Pine Trees, grass), and Hydrography features (Dams, Lakes reservoirs, etc). Additionally, the student will be responsible for documenting the methodology used during the classification process. The student's responsibilities include:
By successfully fulfilling these tasks, the students will demonstrate their proficiency in conducting supervised classification using ArcGIS Pro software. They will showcase their aptitude in effectively analyzing imagery, accurately classifying diverse features, and providing well-documented methodologies for their classification processes. |
Assessment 2 Performed supervised classification Section B – Marking Guide 1C, 1D |
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23-29 October |
Lesson Title: Conduct error analysis Description: During this session, the student will undertake an assessment task that centers on calculating the approximate percentage error to evaluate the accuracy of their classification using error analysis techniques. The task involves completing and interpreting the results, evaluating the outcome, identifying and rectifying any identified issues, updating the documentation, and including a copy of it in the portfolio. Additionally, the student will be required to explain the difference between supervised and unsupervised classification. By successfully accomplishing these tasks, the students will demonstrate their proficiency in error analysis, result interpretation, problem-solving, and understanding the distinctions between supervised and unsupervised classification methods. |
Assessment 2 Interpreted the error analysis Section B – Marking Guide 1C |
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30 October - 5 November |
Lesson Title: Finalise and submit project assessment Description: During this session, the student will be assigned an assessment task focused on finalizing all documentation. The completed portfolio is to be uploaded onto canvas
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Assessment 2 Finalise portfolio Section B – Marking Guide 1E |
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6-12 November |
Revision and feedback on work completed | |
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13-19 November |
Assessment marking and finalising results | |
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Official Results Release DateLinks to an external site. 27 November |
Important: It is your responsibility to check your results on this date. Your official results for this course will be released on this date. Your teacher will not inform you of your final result. It will only be available via My Student Record on RMIT's website. It is not your Trainer/Assessors responsibility to let you know your final result. Your teacher will not be available to comment on your assessment or final results from 27 November 2023. After this date, you can contact them to talk about your final result if you need it. |
Learning Resources
Prescribed Texts
References
Other Resources
Overview of Assessment
Assessment for this course is ongoing throughout the semester. Your knowledge and understanding of course content is assessed through participation in class exercises, oral presentations and through the application of learned skills and insights to your written tasks. Full assessment briefs will be provided and can be found on CANVAS.
Assessment Tasks
- Assessment 1: Determine healthy vegetation using NDVI
- Assessment 2: Supervised / Unsupervised Classification
Assessment Matrix
Mapping Assessments to the Unit of Competency – Instructions
Performance Evidence | ||
To demonstrate competency a candidate must meet the elements and performance criteria of this unit by using a computer and remote sensing software system to conduct and report an advanced remote sensing analysis for two different projects: |
Assessment Task 1: Determine healthy vegetation using NDVI |
Assessment Task 2: Supervised / Unsupervised Classification |
One project must analyse remote sensing data using spectral indices to transform the image to identify landscape patterns or features. |
NDVI |
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One project must focus on performing classifications on datasets using supervised and unsupervised classification algorithms and training samples. |
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Supervised Classification |
Knowledge Evidence | ||
To be competent in this unit a candidate must demonstrate knowledge of: |
Assessment Task 1: Determine healthy vegetation using NDVI |
Assessment Task 2: Supervised / Unsupervised Classification |
Metadata relating to remote sensing data |
1B |
1B |
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Characteristics of multispectral imagery, including:
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1C |
1C |
Spectral response patterns of common landcovers |
1D |
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Geometric and radiometric corrections applied to multispectral imagery |
1C |
1C |
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Functions and statistics available in image processing systems:
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1C & 1E |
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Remote sensing indices:
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1C |
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Industry-accepted techniques for applying supervised and unsupervised classification algorithms to remote sensing data |
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1C |
Legislative requirements for data privacy, intellectual property and licensing when using remotely sensed data |
1B |
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Digital image data formats |
1A |
1A |
Sources of spatial datasets |
1A |
1A |
Image enhancement and processing techniques, including convolution matrices |
1C |
1C |
Methods for validating spatial data sources and constraints on use |
1B |
1A |
Key features of coordinate reference systems |
1B |
1A |
Assessment conditions |
Describe how assessments meet the assessment conditions |
Assessors must meet the requirements for assessors contained in the Standards for Registered Training Organisations. |
RMIT employment requires all trainers and assessors to comply with the Standards for RTOs in respect to holding the TAE40116, or higher VE qualification including any necessary updated units. All employees must show currency within their vocational specialty along with their professional employment. |
Competency is to be assessed in the workplace or a simulated environment that accurately reflects performance in a real workplace setting where these skills and knowledge would be performed. |
Assessments reflect the workspace environment. Assessors to have appropriate industry experience and knowledge. Students have access to computers with the latest GIS software packages during scheduled class times that comply with current industry practices. |
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Candidates must have access to:
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All labs will have the appropriate computers and software installed. Software that students required include:
Students will have access to additional software through Office 365. The students can sign in with your RMIT email address and password. |
Other Information
Interim Results
After you have submitted an assessment, you will receive an interim result. This is displayed in the Grades section of this Canvas shell. These will be as follows:
Results |
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Satisfactory (S) |
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Not Yet Satisfactory (NYS) |
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Did Not Submit (DNS) |
You will receive this result when you have not submitted your assessment by the due date or time. Your Trainer/Assessor, the Program Coordinator or the Program Manager cannot overturn this interim result. An approved Special Consideration is required to allow further opportunities to complete the assessment. Additionally, moderation panels cannot overturn this result either. |
Note: You must achieve a Satisfactory (S) result for every assessment in this course in order to pass.
Interim results will inform the moderation panel of their decision as to whether or not you are deemed competent and can pass the course. Your final and official result will be published via My Results on RMIT's website. It is your responsibility to check your final results. Your Trainer/Assessor, the Program Coordinator or the Program Manager will not inform you of your final result.
Resubmission Policy:
After you submit your assessment, your Trainer/Assessor will review your submission. If you have met all the requirements of the assessment task, you will receive an S result in your Grades section of this course.
If the Trainer/Assessor determines that you have not met the assessment requirements, you will receive an NYS result in the Grades section. In this case, your Trainer/Assessor will provide you with the following opportunities to resubmit:
- Project/Practical-Based Assessment Task: One Resubmission per assessment.
- Knowledge-Based Assessment Task (Tests): Two Resubmissions per assessment
Important: If you do not submit an assessment by its due date or time, you are not entitled to a resubmission. You will only receive an opportunity to submit an assessment that you have failed to submit on time if you provide an approved Special Consideration. Your Trainer/Assessor, Program Coordinator & Program Manager or the Moderation panel cannot overturn a DNS result without approved Special Consideration.
*Resubmissions cannot be accepted via email or as attachments to the comments of another assessment. Any work submitted in this manner will not be accepted or recognised, regardless of the circumstances. All resubmissions must be uploaded via the original submission folder and by the due date and time set by the Trainer/Assessor.
**Your Trainer/Assessor, Program Coordinator or Program Manager cannot issue further resubmission opportunities beyond those stated here. You will only be provided with further opportunities based on the successful application for Special Consideration or upon the course moderation panel's review and decision at the course's end.
Attendance:
You are required to attend a minimum of 85% of your classes. If you miss classes, you will need to provide a medical certificate to your Trainer/Assessor for classes that result in you not meeting the 85% requirement.
If you fail to attend the minimum required classes and engage in each class, your trainer/assessor will not accept your assessment submissions. You will be required to attend an in-person interview with your trainer/assessor at a time determined by them. Failure to attend this interview will result in your assessment being rejected. Additionally, the trainer/assessor reserves the right to reject your assessment based on the interview's outcome. This is required to ensure RMIT meets the Standards set for RTOs to ensure the validity of your work.
Course Overview: Access Course Overview