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--- |
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license: cc-by-nc-4.0 |
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language: |
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- en |
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tags: |
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- architecture |
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- urban |
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- type |
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- art |
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pretty_name: Large City Architecture - Generated Cityscape Set |
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size_categories: |
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- 1M<n<10M |
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--- |
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# LCA-GCS: Large City Architecture - Generated Cityscape Set |
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The Large City Architecture - Generated Cityscape dataset (LCA-GCS) is a comprehensive collection of 1,060,166 AI-generated images representing architectural features of 5,856 global cities. Created using advanced diffusion models, this dataset offers over 200 samples of various architectural types for each city with a population exceeding 100,000. LCA-GCS aims to facilitate comparative analysis, synthesis, and learning from AI-represented architecture, providing a unique resource for researchers, urban planners, and AI developers interested in global architectural trends and urban design. |
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## Dataset Description |
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The LCA-GCS-set represents cities by situating architectural features from details to interiors to urban forms uncommon for existing databases in a flat ontology that allows to search and compare architectural representations in various ways. We hope that the LCA-GCS set benefits future research that could identify recurring spatial configurations, material palettes, and design elements characteristic of specific regions, cultures, or building typologies. Building on it, the synthetic dataset can contribute to e.g., training site-specific models, identifying commonalities, or extracting features that are difficult to access in existing data. |
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You can find a searchable version of the dataset at https://www.largecityarchitecture.org |
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## Methodology |
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Prompts: "Type in City." Where type iterates through the list of types below, and city iterates through the list of cities above 100,000 inhabitants listed at https://simplemaps.com/data/world-cities |
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Types: Apartment, Apartment Building, Arch, Atrium, Auditorium, Balcony, Bathhouse, Boulevard, Breezeways, Bridge, Brise Soleil, Bungalow, Bus Stop, Cafe, Campus, Canopies, Cityscape, Column, Communal Living Space, Courtyard, Door, Eaves, Enfilade, Entrance, Farm, Flat, Foyer, Forest, Garden, Hall, Habitat, House, Housing, Indoor Market, Indoor Plaza, Kitchen, Kiosk, Large Building, Large House, Living Room, Loft, Loggia, Lobby, Office, Park, Patio, Photo of the City, Piazza, Place, Playhouse, Porch, Rondavel, Roof, Roof Garden, Roof Terrace, Room, Row House, Semi-Detached House, Siedlung, Square, Staircase, Street, Terrace, Tower, Townhouse, Tree, Urban Block, Urban Forest, Urban House, Vegetation, Veranda, Vertical Garden, Villa, Window, Workshop Space. |
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## Technical Details |
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Generated with Stability SD2.1, SDXL 1.0, SDXL-Turbo, SD3 using random seeds. For parameters per image, please see the metadata.csv |
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## Citation |
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If you use the LCA dataset in your research, please cite it as follows: |
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Koehler, D. (2024). Large City Architecture - Generated Cityscape Set (LCA-GCS): A Synthetic Dataset of Global City Architecture [Data set]. Huggingface. https://huggingface.co/datasets/Punktiert/LCA-GCS, https://doi.org/10.57967/hf/3111 |
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## Disclaimer of Warranties and Liability |
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Accuracy and Reliability |
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The webpage "Large City Architecture" and its authors provide the data 'as is' and do not guarantee the accuracy, completeness, or usefulness of the data. No warranties are provided. |
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## Limitation of Liability |
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To the fullest extent permitted by law, in no event will LCA-GCS or its authors be liable for any claims, damages, or other liabilities, whether in an action of contract, tort, or otherwise, arising from, out of, or in connection with the data or the use or other dealings in the data. |
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## License |
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This dataset is licensed under the [Creative Commons Attribution-NonCommercial 4.0 International License](https://creativecommons.org/licenses/by-nc/4.0/?ref=chooser-v1). |
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For more details, visit the [Creative Commons website](https://creativecommons.org/licenses/by-nc/4.0/?ref=chooser-v1). |
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## Usage |
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To use this dataset locally or in your own projects, you can use the `WebDataset` library and ensure that your code supports the `.webp` format. |
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To load this dataset with WebDataset: |
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```python |
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import webdataset as wds |
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dataset = wds.WebDataset("path/to/tarfiles/{00000..00500}.tar").decode("pil") |
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for sample in dataset: |
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img = sample['webp'] # Handle webp files |
