Official component Credits Suggested duration Assessment anchor
LMS onboarding layer added to support delivery of the QCTO-aligned programme Not separately credited; supports readiness for all assessed sections Week 1 Setup checklist submission
This opening section establishes the learning contract, course workflow, software environment, and ethical posture required for the rest of the programme. It orients learners to the logic of the course, the role of self-directed practice, and the evidence they will need to produce over six months.
Official component Credits Suggested duration Assessment anchor
900037-000-00-KM-01 8 credits Weeks 2–7 Lab 1 — Municipal or public-service spatial scan
This section builds the spatial-data foundation of the course. Learners move from understanding what spatial intelligence is to handling projections, metadata, exploratory analysis, hotspot detection, predictive reasoning, and cloud-oriented workflows. By the end, the learner should be able to approach a spatial problem methodically and explain what data, methods, and governance issues matter before automation is attempted.
Official component Credits Suggested duration Assessment anchor
900037-000-00-KM-02 8 credits Weeks 8–12 Lab 2 — GeoAI workflow design
This section translates AI concepts into geospatial practice. Learners study how machine learning and deep learning support spatial feature extraction, imagery interpretation, object detection, evaluation, and responsible deployment. The focus is conceptual fluency plus workflow design, not blind model worship.