Brief Introduction
This course focuses on augmenting geometric models with an additional layer of semantic information. You will learn how geometric entities can be tagged with additional attributes, and how these attributes can then be used for querying your models.
Description
As part of our “Spatial Computational Thinking” program, this “Semantic Modelling” course focuses on augmenting geometric models with an additional layer of semantic data. You will learn how geometric entities can be tagged with additional attribute values of different data types, and how these attributes can then be used for querying your models.
During the course, you will build on the foundations developed in the previous course, where the focus was on procedural modelling using geometric entities. In this course, you will first discover that the geometric entities actually have a topological structure that allows you to manipulate these models at a much deeper level.
You will then learn how to add semantics to your models, thereby allowing you to create data-rich spatial information models. This will allow you to apply powerful procedural data modelling techniques, especially the ability to query your semantic model and extract subsets of information.
In the process, you will also further develop your coding skills in the semantic world of computer science. You will revisit the loops and conditional and discover how these can be nested to create more complex control flows. You will also discover how list and dictionary data structures can be nested to create more complex types of data structures.
The modelling exercises and assignments during this course will progress from where the previous course left off. The geometric complexity of the modelling exercises and assignments will increase, but more important is the addition of layers of attribute data to all type of geometric entities, including positions, topological components, geometric objects, and collections of geometric objects. You will also learn how to add attributes to define colour, materials, and other visual properties.
The course prepares you for the next course in the “Spatial Computational Thinking” program, focusing on generative modelling of more complex types of spatial information models.
Knowledge
- Learning algorithmic thinking:
- How semantics can be used to augment geometric models
- The difference between geometry, topology, and attributes
- How query languages can be used to extract data from models
- Become familiar with a range of existing spatial data formats and representations
- Learning semantic modelling:
- Modelling with geometry, topology, and collections
- Attaching attribute data to geometry, topology, and collections
- Querying and filtering data in the model using attributes
- Pushing attributes through the topological hierarchy
- Visualizing models with colour and materials
- Understanding polygon normals and their impact on light
- Importing and exporting geometric and geospatial data models
- Learning coding:
- Developing complex data structures using nested lists and dictionaries
- Using nested loops and nested conditionals
- Strategies for looping: using a counter or iterating over a list?
- How to avoid deep nesting of loops using data structures
- Learning Möbius Modeller:
- The Möbius Spatial Information data model
- The 3D viewer and the attribute tables
- Interrogating models in the 3D viewer
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