The dataset viewer is not available for this subset.
Exception: SplitsNotFoundError
Message: The split names could not be parsed from the dataset config.
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.12/site-packages/datasets/inspect.py", line 289, in get_dataset_config_info
for split_generator in builder._split_generators(
^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/folder_based_builder/folder_based_builder.py", line 237, in _split_generators
raise ValueError(
ValueError: `file_name` or `*_file_name` must be present as dictionary key (with type string) in metadata files
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/split_names.py", line 65, in compute_split_names_from_streaming_response
for split in get_dataset_split_names(
^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/inspect.py", line 343, in get_dataset_split_names
info = get_dataset_config_info(
^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/inspect.py", line 294, in get_dataset_config_info
raise SplitsNotFoundError("The split names could not be parsed from the dataset config.") from err
datasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config.Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
CityLine — Temporal Aerial Construction Dataset (Sample)
Temporal Aerial Vision · Construction Progress · Multiview Geometry · San Jose, CA
CityLine is a multi-year aerial imagery sequence captured from a helicopter during the construction of a major mixed-use development in San Jose, California.
This sample highlights multiple construction phases over time, with several oblique views per capture date.
The full (commercial) dataset contains hundreds of high-resolution images with monthly coverage across several years — suitable for world models, 3D reconstruction, change detection, construction analytics, and urban growth modeling.
This dataset is a limited preview sample intended for evaluation and experimentation.
📍 Project Overview
| Property | Value |
|---|---|
| Project name | CityLine |
| Location | San Jose, California, USA |
| Capture type | Helicopter-based oblique aerial imagery |
| Resolution | 12MP JPEG (RAW available commercially) |
| Coverage period (full set) | 2017 → 2025 (approx.) |
| Temporal cadence | ~monthly |
| Viewpoints per capture | Multiple oblique angles |
| Coordinates | 37.374751, -122.032811 |
🎯 Machine Learning Use Cases
| Category | Tasks Enabled |
|---|---|
| Temporal Vision | World models, change detection, temporal consistency |
| Multiview Geometry | Structure-from-motion, NeRF, depth from motion |
| Autonomy + Robotics | Mapping, localization, spatial reasoning |
| Construction Analytics | Progress estimation, digital twins, safety monitoring |
| Earth Observation | Urban growth, infrastructure evolution |
📁 Dataset Contents (Sample)
Folder structure:
preview/ # resized JPEG previews for fast HF browsing
images/ # full-resolution JPEGs grouped by month
2017-12/
2019-01/
2020-06/
2021-09/
2023-06/
2025-01/
metadata.csv
➡ Preview images are 2048px max dimension, ideal for Hugging Face’s viewer
➡ Full-resolution files contain the highest-quality data for research/licensing
metadata.csv Schema
| Column | Description |
|---|---|
project_id |
Numeric ID for the project |
project_name |
"CityLine" |
filename |
Full-resolution image filename |
preview_filename |
Lower-resolution preview filename |
date |
Capture date parsed from filename |
year_month |
Monthly grouping |
image_seq |
Sequence index derived from filename |
orbit_index |
Orbit grouping (sample = 1) |
orbit_frame |
Ordered view index (1…N) |
latitude |
Project latitude |
longitude |
Project longitude |
notes |
Optional annotation |
🔧 Quick Usage Example
import pandas as pd
from pathlib import Path
from PIL import Image
meta = pd.read_csv("metadata.csv")
# Load preview image first (fast)
preview_path = Path("preview") / meta['preview_filename'][0]
img_preview = Image.open(preview_path).convert("RGB")
img_preview.show()
# Load matching full-resolution image when needed
full_path = Path("images") / meta['year_month'][0] / meta['filename'][0]
img_full = Image.open(full_path).convert("RGB")
img_full.show()
🔐 Full Dataset Access & Licensing
This sample is provided for evaluation purposes only.
The complete CityLine dataset (836 images) and a library of 270+ full-lifecycle construction projects are available under commercial license:
- Towers
- Hospitals
- Stadiums
- Highways & interchanges
- Commercial sites
Contact for full access:
📧 [email protected]
🛰 About SharpShots Aerial
SharpShots Aerial specializes in long-term helicopter-based imaging of major construction and urban projects, enabling advanced mapping and AI research applications.
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