AI for Geospatial Data

About this course

Apply machine learning to geospatial data while accounting for spatial dependence, meaningful features and honest spatial validation.

This advanced course is part of the AI & Generative AI school. Work through the curriculum below in a instructor-led / cohort format, with an estimated study time of 28 h.

Course curriculum

• Module 1 — Geospatial Data for Machine Learning
• Module 2 — Spatial Feature Engineering
• Module 3 — Spatial Autocorrelation
• Module 4 — Spatial Validation
• Module 5 — Spatial Prediction Models
• Module 6 — Areal Units, Neighbourhoods and Graphs
• Module 7 — Learning from Rasters
• Module 8 — GeoAI in Operation

Applied learning

• Case study — Pennard Regional Council: siting public chargers with a demand map
• Lab 1 — From layers to a feature table
• Lab 2 — Can we trust the demand map?
• Lab 3 — Where should the next 25 chargers go?
• Capstone — A Geospatial ML Study

Who this course is for

Technology Professional; Data Professional; Engineer / Architect

Prerequisites

NE-AI-201

Course details

• Course code: NE-AI-307
• Level: Advanced
• Estimated duration: 28 h
• Delivery: Instructor-Led / Cohort