The dataset viewer is not available for this split.
Error code: StreamingRowsError
Exception: CastError
Message: Couldn't cast
slug: string
name: string
country: string
lat: double
lng: double
pop: int64
clip: list<item: double>
child 0, item: double
n: int64
sheet: int64
x: int64
y: int64
features: null
type: string
to
{'type': Value('string'), 'features': List({'type': Value('string'), 'properties': {'name': Value('string')}, 'geometry': {'type': Value('string'), 'coordinates': List(List(List(Json(decode=True))))}})}
because column names don't match
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 147, in get_rows_or_raise
return get_rows(
dataset=dataset,
...<4 lines>...
column_names=column_names,
)
File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
return func(*args, **kwargs)
File "/src/services/worker/src/worker/utils.py", line 127, in get_rows
rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
File "/src/services/worker/src/worker/utils.py", line 483, in safe_iter
yield from ds.decode(False) if ds.features else ds
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2840, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2373, in __iter__
for key, pa_table in self._iter_arrow():
~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2398, in _iter_arrow
for key, pa_table in self.ex_iterable._iter_arrow():
~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 536, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 419, in _iter_arrow
for key, pa_table in self.generate_tables_fn(**gen_kwags):
~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 343, in _generate_tables
self._cast_table(pa_table, json_field_paths=json_field_paths),
~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
pa_table = table_cast(pa_table, self.info.features.arrow_schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2378, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2306, in cast_table_to_schema
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
slug: string
name: string
country: string
lat: double
lng: double
pop: int64
clip: list<item: double>
child 0, item: double
n: int64
sheet: int64
x: int64
y: int64
features: null
type: string
to
{'type': Value('string'), 'features': List({'type': Value('string'), 'properties': {'name': Value('string')}, 'geometry': {'type': Value('string'), 'coordinates': List(List(List(Json(decode=True))))}})}
because column names don't matchNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
LCA-GCS: Large City Architecture - Generated Cityscape Set
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.
Dataset Description
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.
You can find a searchable version of the dataset at https://www.largecityarchitecture.org
Browse it
The set can be searched city by city without downloading it: the LCA-GCS explorer (also at lab-eds.org/tools/lca-gcs-explorer) shows every image of a city grouped by type and model, compares one type across cities, and carries the maps and the CLIP scores. It reads each image straight out of the tar shards by HTTP byte range, using the small index in viewer/ (city rows, per-city and per-type offset tables, thumbnail sheets, maps, country outlines; see viewer/README.md).
Methodology
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
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.
Technical Details
Generated with Stability SD2.1, SDXL 1.0, SDXL-Turbo, SD3 using random seeds. For parameters per image, please see the metadata.csv
Citation
If you use the LCA dataset in your research, please cite it as follows: 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
Disclaimer of Warranties and Liability
Accuracy and Reliability 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.
Limitation of Liability
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.
License
This dataset is licensed under the Creative Commons Attribution-NonCommercial 4.0 International License. For more details, visit the Creative Commons website.
Usage
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.
To load this dataset with WebDataset:
import webdataset as wds
dataset = wds.WebDataset("path/to/tarfiles/{00000..00500}.tar").decode("pil")
for sample in dataset:
img = sample['webp'] # Handle webp files
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