Course coordinator
Dr William Woodgate
Course overview
- Study period
- Semester 2, 2024 (22/07/2024 - 18/11/2024)
- Study level
- Postgraduate Coursework
- Location
- External
- Attendance mode
- Online
- Units
- 2
- Administrative campus
- St Lucia
- Coordinating unit
- The Environment School
Remote sensing or earth observation, is an important tool for monitoring and modelling the condition and dynamics of terrestrial, aquatic and atmospheric environments. The information extracted from images may be used in many ways, as image or thematic maps, directly in decision making, as estimates of biophysical variables or integrated with other spatial information systems for further analysis and display. This information can be collected from local to global scales for examining changes in the habitat of endangered fauna or monitoring continental scale deforestation and global scale oceanic and atmospheric conditions.
This course is a logical progression from the remote sensing concepts and skills introduced in GEOM2000/7000. GEOM3001/7001 emphasises digital image processing for analysis of remotely sensed imagery, including airborne and satellite multi-spectral, hyper-spectral and synthetic aperture radar data. Practical sessions will involve a progression of practicals in the computer laboratory using the ENVI (Environment for Visualising Images) software package. Concepts and skills acquired in these sessions will be applied in the individual student remote sensing project that can be designed to suit your area of interest.
Remote sensing orﾠEarth Observation (EO) Scienceﾠis an important tool for monitoring and modelling the condition and dynamics of terrestrial, aquatic and atmospheric environments. The information extracted from images and remote collected datasets may be used in many ways, as image or thematic maps, directly in decision making, as estimates of biophysical variables or integrated with other spatial information systems for further analysis and display. This information can be collected from local to global scales for examining changes in the habitat of endangered fauna or monitoring continental scale deforestation and global scale oceanic and atmospheric conditions.
ﾠThis course is co-taught between GEOM3001 and GEOM7001.
How this course differs from the introductory GEOM 2000/7000 course:
This course is a logical progression from the remote sensing orﾠEarth Observation Scienceﾠconcepts and skills introduced in GEOM2000. This course emphasises digital image processing for analysis of remotely sensed imagery, including airborne and satellite multi-spectral, hyperspectral, LiDAR and synthetic aperture radar data. Practical sessions will involve a progression of practicals in the computer laboratory (See mySinet), using a combination of open-source and proprietary software.ﾠConcepts and skills acquired in these sessions will be applied in the individual student remote sensing project that can be designed to suit your area of interest.
This course builds upon the fundamentals taught in GEOM2000/7000 and will develop more advanced EO analytic skills and knowledge, including:
- Knowledge of the growing number of big data EO analytic platforms, and gaining practical experience with one or more of these technologies (e.g. Open Data Cube (https://www.opendatacube.org/), Microsoft Planetary Computer (https://planetarycomputer.microsoft.com/))
- Calculating maps of biophysical attributes (e.g. biomass) by combining remotely sensed data with ground data and/or empirical algorithms
- More advanced theoretical and practical skills for analysing new sources of remote sensing data (e.g. spaceborne LiDAR)
- Design, plan and execute a remote sensing project with detailed guidance
Course requirements
Assumed background
A highly desirable pre-requisite for this course is to have introductory knowledge of remote sensing from GEOM2000 (or equivalent). Prior to attending image processing practicals students are expected to be familiar with the basics of the Windows operating system (file naming conventions and management).
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If you have any concerns or special needs in relation to this course please see the course coordinator.
ﾠ
Support for students with a disability
Any student with a disability who may require alternative academic arrangements in the course/program is encouraged to seek advice at the commencement of the semester from a Disability Learning Adviser at Student Services.
Prerequisites
You'll need to complete the following courses before enrolling in this one:
GEOM2000 or 7000
Incompatible
You can't enrol in this course if you've already completed the following:
GEOM3001
Jointly taught details
This course is jointly-taught with:
- Another instance of the same course
This course is co-taught between GEOM3001 and GEOM7001. Assessments are individual.
Course contact
Course staff
Lecturer
Dr William Woodgate
demonstrator
Mr Raja Ram Aryal
Demonstrator
Ms Gillian Rowan
Timetable
The timetable for this course is available on the UQ Public Timetable.
Aims and outcomes
The aims of this courseﾠenable students to understand, think and operate like a remote sensing analyst by providing:
(1) ﾠa sound understanding of the theory of earth observation data acquisition and processing,
(2)ﾠ the practical skills to complete data acquisition and processing projects that produce information products using remote sensing data, and
(3) a detailed understanding of the processes to deliver information for mapping, measurement or monitoring applications, in a wide range of professions.
Accomplishing these aims develops applied knowledge, skills, practical experience, and professional networks that enable you to collect, process, analyse and communicate about satellite, airborne and drone data. The course will provide a detailed overview and practical skills of what is currently possible with remote sensing data for earth observing applications, and outline theﾠexpanding possibilities due to the rapidly evolving technology.
Learning outcomes
After successfully completing this course you should be able to:
LO1.
Understand the concepts of spatial, spectral, radiometric, and temporal resolutions of remotely sensed data and how these concepts relate to remote sensing applications in different environments.
LO2.
Explain and demonstrate how earth observation data are an essential form of information for industry, government, NGOs and sciences, in their routine activities, and for a range of professional positions.
LO3.
Explain the fundamental image processing operations for extracting thematic and biophysical information from remotely sensed data. This will be reinforced through practical implementation of the following operations using licensed and/or open-source software: a. Pre-processing and data integration b. Radiometric/Geometric Corrections and Enhancements c. Image Classification (parametric and non-parametric) d. Biophysical modelling e. Error and Accuracy Assessment, and f. Online and Object Based Image Processing
LO4.
Design, plan and execute a remote sensing project, including: a. Data acquisition; b. Data pre-processing and integration; c. Data analysis; and d. Report preparation and presentation.
LO5.
Identify commercially available remotely sensed data sets and discuss their strengths and weaknesses for specific applications.
LO6.
Select the most appropriate remotely sensed data set and ancillary data for a problem.
LO7.
Learn about the growing number of big data EO analytic platforms and gain practical experience with one or more.
LO8.
Import, display and analyse multispectral and RADAR images.
LO9.
Plan and conduct multi-temporal analyses using remotely sensed data.
LO10.
Integrate field data with remotely sensed data in a GIS or image processing environment and integrate non-image data within an image processing environment.
Assessment
Assessment summary
| Category | Assessment task | Weight | Due date |
|---|---|---|---|
| Project |
Remote Sensing Project Proposal
|
22% |
22/07/2024 -
Due 2pm. Optional draft proposal due 19th Aug 2pm. |
| Quiz |
Online quiz series (4 total)
|
15% 3 quizzes, 5% each. |
Quiz #1 13/08/2024 - 16/08/2024 Quiz #2 10/09/2024 - 13/09/2024 Quiz #3 15/10/2024 - 18/10/2024 |
| Project | Remote Sensing Project Report | 56% |
2/09/2024 - 25/10/2024
Optional draft report due 13th Oct |
| Participation/ Student contribution |
Engagement
|
15% |
22/07/2024 - 7/10/2024
Graded per practical (1.5% per practical). |
Assessment details
Remote Sensing Project Proposal
- Mode
- Written
- Category
- Project
- Weight
- 22%
- Due date
22/07/2024 -
Due 2pm.
Optional draft proposal due 19th Aug 2pm.
- Other conditions
- Student specific.
Task description
Aim:
To design an image processing project based on the image processing operations covered in weeks 1-5 of the course. This includes discussing and implementing the image processing sequence, including: geometric and radiometric corrections, spectral enhancements, spatial enhancements, image classification, and accuracy assessment. Note that some corrections may already be applied. You may choose an aspect of personal interest (land-use, agriculture, vegetation, geomorphology, soils, geology, urban form and condition, or another topic). If you do not have an interest in a particular topic, a land-cover classification project may be suitable.
Project Type: Individual Remote Sensing ProjectYou will work individually to apply an image processing sequence to remote sensing data for an area of your choice. The area extent is suggested to be up to 50km2 (with a large degree of freedom) in order to provide an informed analysis. You will need to select a suitable mapping or modelling application to apply. Please speak with the Course Coordinator for recommendations.
Overview:
To ensure your project is feasible you are required to submit an approx. 8-12 page proposal containing the following: a statement of the aim(s) of your image processing; a short description of the problem (preferably with relevant references), the used Remote sensing image data sets used, study site map, an outline or flowchart of the image processing operations you intend to apply; information extraction process, accuracy assessment, details of your intended output product(s), and strengths and limitations of the proposed workflow. The remote sensing project proposal should include scientific journal references to support the proposal project.
Examples of suitable topics are: land-cover classification for a region; quantifying mine rehabilitation, quantifying snow melt characteristics of an alpine region, evaluating seasonal vegetation patterns etc.
The proposal contributes 22% to the course grade for GEOM3001/7001 and is essential for good preparation of your project and can provide a basis for the final project report.
See the supplied Proposal Template and follow this for the submission.
Submission guidelines
Submit electronically through the course Blackboard site using Turnitin.
Deferral or extension
You may be able to apply for an extension.
The maximum extension allowed is 28 days. Extensions are given in multiples of 24 hours.
Late submission
A penalty of 10% of the maximum possible mark will be deducted per 24 hours from time submission is due for up to 7 days. After 7 days, you will receive a mark of 0.
Online quiz series (4 total)
- Online
- Mode
- Written
- Category
- Quiz
- Weight
- 15% 3 quizzes, 5% each.
- Due date
Quiz #1 13/08/2024 - 16/08/2024
Quiz #2 10/09/2024 - 13/09/2024
Quiz #3 15/10/2024 - 18/10/2024
Task description
There are a series of three Blackboard quizzes that accompany the lecture and practical content taught in W1-W12.
Each quiz will cover lecture content up to that week and practical content up to the week prior.
Each quiz is worth 5% of the final course grade (total 15%).
Due dates are as follows:
Quiz 1: Fri 16th Aug., 2pm
Quiz 2: Fri 13th Sept., 2pm
Quiz 3: Fri 18th Oct., 2pm
Submission guidelines
Complete online via Blackboard before the due date. The quiz will be released on the Tues before the due date (3 days prior),
Deferral or extension
You may be able to apply for an extension.
The maximum extension allowed is 28 days. Extensions are given in multiples of 24 hours.
Email the course coordinator if an extension to the quiz is needed in line with UQ policy "Acceptable reasons for an extension" (https://my.uq.edu.au/information-and-services/manage-my-program/exams-and-assessment/applying-assessment-extension?p=1#1)
Late submission
A penalty of 10% of the maximum possible mark will be deducted per 24 hours from time submission is due for up to 7 days. After 7 days, you will receive a mark of 0.
Remote Sensing Project Report
- Mode
- Written
- Category
- Project
- Weight
- 56%
- Due date
2/09/2024 - 25/10/2024
Optional draft report due 13th Oct
Task description
Aim:
To complete the project described in the Project Proposal (Assessment #1).
Note: the project report can vary from that described in the proposal - please consult the Course Coordinator and/or the demonstration team for guidance.
Overview:
Upon completion of your image processing and analysis of results you are to compile a report that includes: a brief literature review on the application of remote sensing in your selected topic area; a description of the processing stages and techniques applied; a summary of the results obtained (output maps, summary statistics); comments on the value of processing techniques; and suggestions on how the results may be improved. The remote sensing project report should approx. be 4000-6000 words (excluding tables and bibliography) and should include a background review and synthesis of (at least 10 peer reviewed articles GEOM 3001, or 15 articles for GEOM 7001) refereed scientific journals describing remote sensing applications in your selected area of interest studied in your project.
Note:
Most of the allocated practical times will be used to introduce image analysis tasks.
Suggested Structure for Remote Sensing Report Write-up:
- Title
- Contents
- Abstract
- Introduction and Objectives
- Data and Methods
- Results and Discussion
- Conclusions and Future Work
- References
- Appendix with Processing Log
Your assignment will be marked out of 100 possible points and then re-scaled to a mark out of 48%. Marks are assigned using the assessment criteria.
Please use the Project Report template and also refer to the Project Proposal and Project Report guidance document for more information (posted in Blackboard).
In order to receive a passing grade, students must obtain at least 40% of the marks on the Project Report.
Submission guidelines
Submit electronically through the course Blackboard site using Turnitin.
Deferral or extension
You may be able to apply for an extension.
The maximum extension allowed is 28 days. Extensions are given in multiples of 24 hours.
Refer to the UQ policy on granting assessment extensions.
Late submission
A penalty of 10% of the maximum possible mark will be deducted per 24 hours from time submission is due for up to 7 days. After 7 days, you will receive a mark of 0.
Engagement
- Identity Verified
- Mode
- Activity/ Performance
- Category
- Participation/ Student contribution
- Weight
- 15%
- Due date
22/07/2024 - 7/10/2024
Graded per practical (1.5% per practical).
Task description
Assessment that grades engagement in practicals.
Submission guidelines
Attend the practical to qualify for this grade.
Deferral or extension
You may be able to apply for an extension.
The maximum extension allowed is 7 days. Extensions are given in multiples of 24 hours.
Email the course coordinator if there is a clash with other teaching.
Late submission
You will receive a mark of 0 if this assessment is submitted late.
Course grading
Full criteria for each grade is available in the Assessment Procedure.
| Grade | Description |
|---|---|
| 1 (Low Fail) |
Absence of evidence of achievement of course learning outcomes. Course grade description: The minimum percentage required for this grade is: 0% |
| 2 (Fail) |
Minimal evidence of achievement of course learning outcomes. Course grade description: The minimum percentage required for this grade is: 30% |
| 3 (Marginal Fail) |
Demonstrated evidence of developing achievement of course learning outcomes Course grade description: The minimum percentage required for this grade is: 45% In order to receive a passing grade, students must obtain at least 40% of the marks on the Project Report. |
| 4 (Pass) |
Demonstrated evidence of functional achievement of course learning outcomes. Course grade description: The minimum percentage required for this grade is: 50% In order to receive a passing grade, students must obtain at least 40% of the marks on the Project Report. |
| 5 (Credit) |
Demonstrated evidence of proficient achievement of course learning outcomes. Course grade description: The minimum percentage required for this grade is: 65% In order to receive a passing grade, students must obtain at least 40% of the marks on the Project Report. |
| 6 (Distinction) |
Demonstrated evidence of advanced achievement of course learning outcomes. Course grade description: The minimum percentage required for this grade is: 75% In order to receive a passing grade, students must obtain at least 40% of the marks on the Project Report. |
| 7 (High Distinction) |
Demonstrated evidence of exceptional achievement of course learning outcomes. Course grade description: The minimum percentage required for this grade is: 85% In order to receive a passing grade, students must obtain at least 40% of the marks on the Project Report. |
Additional course grading information
The final grade for the course will typically fall within the above mentioned ranges.
In order to receive a passing grade, students must obtain at least 45% of the marks on the project report.
Supplementary assessment
Supplementary assessment is available for this course.
Supplementary assessment is available
Courses graded 1-7
Should you fail a course with a grade of 3, you may be eligible for supplementary assessment. Refer to my.UQ for information on supplementary assessment and how to apply.
Supplementary assessment provides an additional opportunity to demonstrate you have achieved all the required learning outcomes for a course.
If you apply and are granted supplementary assessment, the type of supplementary assessment set will consider which learning outcome(s) have not been met.
Supplementary assessment can take any form (such as a written report, oral presentation, examination or other appropriate assessment) and may test specific learning outcomes tailored to the individual student, or all learning outcomes.
To receive a passing grade of 3S4, you must obtain a mark of 50% or more on the supplementary assessment.
Additional assessment information
Applications for Extensions
Information on applying for an extension can be found here - my.UQ Applying for an extension
Extension applications must be received by the assessment due date and time.
If you are unable to provide documentation to support your application by the due date and time you must still submit your application on time and attach a Word document outlining why you cannot provide the documentation and upload the documentation to the portal within 24 hours.
Please note: While your extension request is being considered, you should work towards completing and submitting your assessment as soon as possible.
If you have been ill or unable to attend class for more than 4 weeks in a semester, we advise you to carefully consider whether you are capable of successfully completing your courses. You might need to consider applying for removal of course. We strongly recommend you seek advice from the Faculty that administers your program.
Extensions with Student Access Plans (SAP)
For extensions up to 7 days, your SAP is all that is required as documentation to support your application. However, extension requests longer than 7 days (for any one assessment item) will require the submission of additional supporting documentation e.g., a medical certificate. A maximum of two applications may be submitted for any one assessment item, unless exceptional circumstances can be demonstrated. All extension requests must be received by the assessment due date and time.
Artificial Intelligence Use (AI)
The assessment tasks in this course evaluate students’ abilities, skills, and knowledge without the aid of Artificial Intelligence (AI).
Students are advised that the use of AI technologies to develop responses is strictly prohibited and may constitute misconduct under the Student Code of Conduct.
ﾠ
Turnitin
By submitting work through Turnitin you are deemed to have accepted the following declaration “I certify that this assignment is my own work and has not been submitted, either previously or concurrently, in whole or in part, to this University or any other educational institution, for marking or assessment”.
Students can download the digital receipt in the Assignment inbox to confirm successful submission. A valid Turnitin receipt will be the only evidence accepted if assessments are missing. Without evidence, the assessment will receive 10% per day of the available marks late penalty deduction, or after seven days, will receive zero marks.
In the case of a Blackboard outage, please contact the Course Coordinator as soon as possible to confirm the outage with ITS.
Learning resources
You'll need the following resources to successfully complete the course. We've indicated below if you need a personal copy of the reading materials or your own item.
Library resources
Find the required and recommended resources for this course on the UQ Library website.
Additional learning resources information
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Recommended reading:
- CRCSI (2016a) Earth Observation: Data, Processing and Applications. (Eds. Harrison, B.A., Jupp, D.L.B., Lewis, M.M., Forster, B., Mueller, N., Smith, C., Phinn, S., Hudson, D., Grant, I., Coppa, I.) CRCSI, Melbourne.Available from:ﾠ http://www.crcsi.com.au/earth-observation-series
- Schowengerdt, R.A. 1997. Remote sensing: Models and methods for image processing, 2nd Edition, Academic Press: San Diego.
- Schott, J.R. (1997) Remote sensing: the image chain approach. Oxford University Press.
- Lillesand, T.M. and Kiefer, R.W. 2000. Remote Sensing and Image Interpretation. 4thﾠEdition, John Wiley and Sons.
Note: All readings refer to chapters Jensen (2007 2 edition), additional information may be found in Chapter 7 in Lillesand and Kiefer (2000) and selected sections in Schott (1997) and Schowengerdt (1997).
Other useful reference books for remote sensing are:
- Purkis, S. and C. Roelfsema (2015). 11 Remote Sensing of Submerged Aquatic Vegetation and Coral Reefs. Remote Sensing of Wetlands: Applications and Advances.pp: 223.
- Goodman, J., Purkis, S. and Phinn, S.R. (2013) Coral Reef Remote Sensing: A Guide for Multi-level Sensing Mapping and Assessment. Goodman, J., Purkis, S. and Phinn, S.R. Springer Publishing pp3-25 ISBN 978-90-481-9291-5
- American Society of Photogrammetry (1983) Manual of Remote sensing, 2nd Edition, ASP, Falls Church.
- Barrett, E.C.ﾠand Curtis L.F. (1992) Introduction to environmental remote sensing. 3rd ed., Melbourne, Vic. : Chapman & Hall.
- Campbell, J. B. (1996) Introduction to remote sensing. 2nd Ed. The Guilford Press, New York.
- Cracknell A. and Hayes L. (1991) Introduction to remote sensing. London: Taylor & Francis
- Curran, P.J. (1985) Principles of remote sensing. Longman.
- Harrison, B.A.ﾠandﾠJupp , D.L.B. (1989) Introduction to remotely sensed data. Canberra : CSIRO, Division of Water Resources.
- Harrison, B.A.ﾠandﾠJupp , D.L.B. (1990) Introduction to image processing. Canberra : CSIRO, Division of Water Resources, 1990
- Richards, J.A. (1993) Remote sensing digital image analysis: an introduction. 2nd Ed. Springer Verlag.
- Robert A. Ryerson Ed. (1997) Manual of remote sensing. 3rd Edition, published in cooperation with the American Society for Photogrammetry and Remote Sensing.ﾠJ. Wiley, New YorkSSH Library - G70.4 .M36 1997 v.1, V.2, V.3.
- Volume. 1. Earth observing platforms and sensors, Stanley A. Morain and Amelia M. Budge (CD-ROM)
- Volume 2: Principles and applications of imaging radar, Floyd M. Henderson and Anthony J. Lewis
- Volume 3: Remote sensing for the earth sciences, Andrew Rencz.
There are a number of remote sensing texts in the main, undergraduate, and Physical Sciences and Engineering (PSE) Libraries which can be used for the course.
The bold journals are available on-line through the University Library’s web page (www.library.uq.edu.au)
The main journals which will be useful for the course include:
- Canadian Journal of Remote Sensing
- Geocarto International
- International Journal of Remote Sensing
- IEEE Transactions on Geosciences and Remote Sensing
- Photogrammetric Engineering and Remote Sensing
- Remote Sensing of Environment
- International Journal of Photogrammetry and Remote Sensing
- The Remote Sensing Journal
- Journal of Applied Remote Sensing
- Remote Sensing (Open Source)
On-line remote sensing toolkitﾠ https://www.rsrc.org.au/rstoolkit
Learning activities
The learning activities for this course are outlined below. Learn more about the learning outcomes that apply to this course.
Filter activity type by
Please select
| Learning period | Activity type | Topic |
|---|---|---|
Week 1 (22 Jul - 28 Jul) |
Lecture |
Lecture 1 Course introduction and overview |
Practical |
Practical 1 code based computing for geospatial data. Part 1: basic python and Sandbox/Planetary PC. intro to data cubes (Digital Earth Aus P1) code based computing for geospatial data: intro to data cubes (Digital Earth Aus P1) |
|
Week 2 (29 Jul - 04 Aug) |
Lecture |
Lecture 2 Digital earths & multi-temporal data analysis Digital earths & multi-temporal data analysis |
Practical |
Practical 2 code based computing for geospatial data: DEA Part 2 - multitemporal data code based computing for geospatial data: DEA P2 - multitemporal |
|
Week 3 (05 Aug - 11 Aug) |
Lecture |
Lecture 3 Open and Online (big) Data Science for Earth Observation (Part 1), Drone processing workflows (Part 2) Open and Online (big) Data Science for Earth Observation (Part 1), Drone processing workflows (Part 2) |
Practical |
Practical 3 code based computing for geospatial data: DEA Part 3 code based computing for geospatial data: DEA P3 - reading in external data |
|
Week 4 (12 Aug - 18 Aug) |
Lecture |
Lecture 4 Pre-processing (Geometric, Radiometric, atmospheric) |
Practical |
PUBLIC HOLIDAY No practical |
|
Week 5 (19 Aug - 25 Aug) |
Lecture |
Lecture 5 Thematic information extraction (across platforms) Thematic information extraction (across platforms) |
Practical |
Practical 5 Pre-processing Operations (ENVI) |
|
Week 6 (26 Aug - 01 Sep) |
Lecture |
Lecture 6 Biophysical information extraction & error and accuracy assessment Biophysical information extraction & error and accuracy assessment |
Practical |
Practical 5: Pre-processing Operations (ENVI) Pre-processing Operations (ENVI) - learning the essential image corrections |
|
Week 7 (02 Sep - 08 Sep) |
Lecture |
Lecture 7: LiDAR image processing & radiative transfer model applications LiDAR image processing & radiative transfer model applications |
Practical |
Practical 6: Supervised and unsupervised image classifications (ENVI) Supervised and unsupervised image classifications (ENVI) |
|
Week 8 (09 Sep - 15 Sep) |
Lecture |
Lecture 8 Aquatic remote sensing |
Practical |
No Practical (week 8) |
|
Week 9 (16 Sep - 22 Sep) |
Lecture |
Lecture 9: Project review discussion Project review discussion |
Practical |
Practical 7: Accuracy assessment with reference thematic map and data points (ENVI) code based computing for geospatial data: MS Planetary PC (Part 1/2) |
|
Mid Sem break (23 Sep - 29 Sep) |
Lecture |
MID-SEMESTER BREAK No class |
Practical |
Mid-semester break No class |
|
Week 10 (30 Sep - 06 Oct) |
Lecture |
Lecture 10: Drones for Earth Observation Drones for Earth Observation |
Practical |
Practical 8: code based computing for geospatial data - other data and tools (Part 1) code based computing for geospatial data: MS Planetary PC (part 2/2) |
|
Week 11 (07 Oct - 13 Oct) |
Lecture |
Public Holiday No lecture |
Practical |
Practical 9: code based computing for geospatial data & project support code based computing for geospatial data |
|
Week 12 (14 Oct - 20 Oct) |
Lecture |
Lecture 11: Radar image processing and applications Radar image processing and applications |
Practical |
Practical 10: code based computing for geospatial data & project support code based computing for geospatial data |
|
Week 13 (21 Oct - 27 Oct) |
Lecture |
Lecture 12 Class discussion: job ready skills and the future trajectory of space and spatial Class discussion: job ready skills and the future trajectory of space and spatial |
Practical |
Practical 11: project support Support for project - optional time to work on the Project Report |
Policies and procedures
University policies and procedures apply to all aspects of student life. As a UQ student, you must comply with University-wide and program-specific requirements, including the:
- Student Code of Conduct Policy
- Student Integrity and Misconduct Policy and Procedure
- Assessment Procedure
- Examinations Procedure
- Reasonable Adjustments - Students Policy and Procedure
Learn more about UQ policies on my.UQ and the Policy and Procedure Library.