Artificial Intelligence for Geodata
These days, you can't escape terms like artificial intelligence, machine learning, deep learning, computer vision, NLP, and data science. But what exactly do these terms mean, and how can these techniques be applied to geodata to solve your spatial problems? How do you determine if AI is the right solution for your problem? Get started during this one-day session, which will teach you the concepts, tools, typical project workflows, and pitfalls to avoid.
Practical information:
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In this course, you will learn:
The basic concepts, tools, development process, and criteria that allow you to evaluate how GeoAI can help solve your spatial problems.
We will delve deeper into how AI works and is developed, but we will not program or build models ourselves. For this, GIM offers an advanced course "Artificial Intelligence for Geodata (Advanced)."
Topics:
- Introduction and basic concepts in artificial intelligence, machine learning, deep learning, and GeoAI.
- Taxonomy of AI techniques.
- Basic concepts such as supervised, unsupervised, and reinforcement learning methods. I
- nsights into the development process.
- Insights for evaluating the feasibility of your project and planning projects.
- Practical work with GeoAI using a few simple use cases.
- This course is taught in ENGLISH.