https://github.com/aidapt-a/ai_in_architectural_design_tud
AI_in_Architectural_Design_TUD
Science Score: 26.0%
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○CITATION.cff file
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○Scientific vocabulary similarity
Low similarity (9.1%) to scientific vocabulary
Repository
AI_in_Architectural_Design_TUD
Basic Info
Statistics
- Stars: 3
- Watchers: 0
- Forks: 1
- Open Issues: 0
- Releases: 1
Metadata Files
README.md
AI in Architectural Design
The aim of the course is to unlock and channel the creative potential of architects in the era of AI. This will be done mainly by providing valuable resources and methods for quantitatively curating and evaluating visual architectural data. This course is delivered at the Faculty of Architecture and the Built Environment (Delft University of Technology) as a 5EC course in the third quarter of the first year of the Architecture Master's track: Master 2, Q3. Flyer.
- Instructor: Seyran Khademi.
- Assistants: Casper van Engelenburg, Fatemeh Mostafavi, Pablo G. Morato, Julien Vuillamy
Tutorials
| Tutorial | Learning objectives |
| --- | --- |
| T0IntrotoPythonand_Colab | Get familiar Python programming in Google Colab|
| T1FromCodetoCanvas | Use Google Collab and run code
Create and print most common data types
Create and manipulate polygonal shapes
Plot polygonal shapes
Use for loops and functions|
| T2FromNumberstoPlots | Make use of CSV files to create a DataFrame
Cleaning Data (reading, sorting, and selecting)
Plotting FloorPlans|
| T3FromGeometriestoGraphs | Define a graph
Create, manipulate, and visualize a graph in Python
Describe the access graph of a floor plan
Extract (apartment-level) access graphs from the IFC building elements.|
| T4FromFootprintstoPhotos | Visualize and interpret building+context representations
Automatically collect aerial images and create a customized dataset
Locate building footprints from geographical information|
| T5FromPhotostoEmbeddings | Generate image embeddings from pre-trained foundation models
Compute the cosine similarity between embeddings
Interpret building+context representations|
| T6FromImagesto3D_Understanding | Understand opportunities of AI in computer vision and photogrammetry
Introduce the concept of data fusion when working with multiple modalities|
| T7FromGraphstoSimilarity | Investigate floor layout similarity using pre-trained deep neural networks specialized in extracting layout-specific features|
| T8SimilarityUrban_Scale | Visualize and interpret urban representations at scale
Create a customized dataset of aerial and street view images
Generate an urban similarity score by combining similarity scores computed from both aerial and street view images|
| W0WELLfor_residential | Create a visual narrative to verify some health and well-being concepts of WELL for residential standard|
Owner
- Name: AiDAPT-A
- Login: AiDAPT-A
- Kind: organization
- Repositories: 1
- Profile: https://github.com/AiDAPT-A
GitHub Events
Total
- Push event: 14
Last Year
- Push event: 14
Issues and Pull Requests
Last synced: 10 months ago
All Time
- Total issues: 0
- Total pull requests: 16
- Average time to close issues: N/A
- Average time to close pull requests: about 7 hours
- Total issue authors: 0
- Total pull request authors: 1
- Average comments per issue: 0
- Average comments per pull request: 0.0
- Merged pull requests: 16
- Bot issues: 0
- Bot pull requests: 0
Past Year
- Issues: 0
- Pull requests: 1
- Average time to close issues: N/A
- Average time to close pull requests: less than a minute
- Issue authors: 0
- Pull request authors: 1
- Average comments per issue: 0
- Average comments per pull request: 0.0
- Merged pull requests: 1
- Bot issues: 0
- Bot pull requests: 0
Top Authors
Issue Authors
Pull Request Authors
- moratodpg (16)