Overview
This advanced course examines the design, implementation, and evaluation of geospatial data infrastructure integrated with Earth observation technologies. Students develop expertise in spatial databases, data catalogs, metadata, discovery services, interoperable standards, web mapping, application programming interfaces, cloud and distributed geospatial computing, data cubes, versioning, provenance, access control, and FAIR data principles.
The Earth observation component covers satellite and airborne platforms, orbital and sensor characteristics, optical, multispectral, hyperspectral, thermal, radar, and LiDAR data, and the processing of remotely sensed imagery. Topics include radiometric and geometric correction, atmospheric effects, cloud masking, compositing, spectral indices, classification, change detection, time-series analysis, data fusion, validation, and uncertainty assessment.
Students apply these methods to land-cover mapping, climate monitoring, hazard assessment, water resources, agriculture, and ecosystem change. Emphasis is placed on interoperable architecture, reproducible analytical pipelines, defensible accuracy assessment, uncertainty communication, and the delivery of Earth observation results to technical and decision-making audiences.
Learning Outcomes
- Design interoperable geospatial data architectures incorporating spatial databases, catalogs, metadata, standards, services, APIs, access controls, and FAIR data principles.
- Evaluate satellite and airborne platforms, orbital characteristics, sensor capabilities, spatial, spectral, temporal, and radiometric resolutions, and dataset suitability for defined applications.
- Process optical, multispectral, hyperspectral, thermal, radar, and LiDAR data using appropriate radiometric, geometric, atmospheric, and cloud-screening procedures.
- Implement analytical workflows involving compositing, spectral indices, classification, change detection, time-series analysis, data fusion, and multidimensional data cubes.
- Construct reproducible geospatial services or pipelines using versioning, provenance tracking, distributed or cloud computing, and documented quality controls.
- Assess the accuracy, uncertainty, validation requirements, and limitations of Earth observation products and analytical outputs.
- Communicate geospatial infrastructure designs and Earth observation findings clearly to technical specialists, stakeholders, and decision-makers.
Timetable
| Type | Length | Frequency | Period |
|---|---|---|---|
| Lecture | 2 hours | Weekly | All semester |
| Lab | 2 hours | Weekly | All semester |
| Workshop | 2 hours | Fortnightly | All semester |
| Tutorial | 1 hour | Fortnightly | All semester |
Assessment Schedule
| Type | Description | Weighting |
|---|---|---|
| Assignment | Infrastructure architecture and standards design brief | 15.00% |
| Assignment | Earth observation preprocessing and analysis report | 20.00% |
| Test | Practical geospatial pipeline test | 15.00% |
| Deliverable | Reproducible service or workflow deliverable | 20.00% |
| Exam | Final examination | 30.00% |
Prerequisites
- Requirement Prior university study in statistics, programming, or spatial data analysis
Teaching Staff & Programs
This course is delivered jointly by faculty from the participating programs listed below. In line with the Douchewater Way, the University of Sexology tailors core instruction directly to each cohort's specific discipline — adapting curriculum to program needs rather than forcing students into a one-size-fits-all model. Learn more about our approach at The Douchewater Way.