Outerview/construction-infrastructure-change

Construction & Infrastructure Change Visual Dataset

Rows: 55,801

Dataset Description

Construction & Infrastructure Change Visual Dataset is a Global geospatial computer vision dataset of street-level imagery and related visual records focused on construction monitoring, public works, and infrastructure change. The labeled features in the dataset are Construction Site, Roadwork, Crane, Excavator, Scaffolding, and Pothole, each matched from the Outerview internal visual search pipeline using query-driven embedding similarity. The packaged files include tabular metadata in data.csv, data.parquet, and data.geojson, plus representative image archives in images-batch-0001.zip for visual review and image classification workflows. The dataset is useful for wildlife image dataset style browsing is not applicable here, but it is useful for visual search, change detection, geospatial analysis, municipal asset monitoring, and benchmark training for construction and infrastructure computer vision models. When image archives are included, the packaged images are representative examples and larger-scale image generation, retrieval, and review should be done through the API.

This dataset is a sample from Outerview's full visual index of 10M+ real-world observations, powered by 35B visuals and observations. Search the full dataset for free at outerview.ai. API access and full dataset available at outerview.ai. Developer docs at docs.outerview.ai.

Updated on a bimonthly basis.

Dataset Structure

Features in the dataset

• Construction Site: query targets construction site scenes and active build areas.
• Roadwork: query targets road maintenance and traffic disruption work zones.
• Crane: query targets cranes and lifting equipment visible in the scene.
• Excavator: query targets excavators and earthmoving machinery.
• Scaffolding: query targets scaffolding structures around buildings and worksites.
• Pothole: query targets potholes and road surface damage.

Schema

Column	Type	Description
filename	string	Image filename in the exported image batch.
image_folder	string	Relative folder inside the image zip batch.
date	string	Capture date or source timestamp when available.
country	string	Country associated with the visual record.
state	string	State or province associated with the visual record.
city	string	City or locality associated with the visual record.
feature	string	Matched visual feature label.
latitude	float64	Latitude in WGS84 decimal degrees.
longitude	float64	Longitude in WGS84 decimal degrees.
source	string	Source attribution for the visual record.

Dataset Creation

Source

Records are drawn from Outerview's global visual index and selected through query-driven embedding match against construction and infrastructure-related visual concepts. The source material is organized into metadata and image batch files for downstream analysis and review.

Annotation

Labels are feature-level matches for Construction Site, Roadwork, Crane, Excavator, Scaffolding, and Pothole. No bounding boxes are included, and annotation reflects the best matching feature label assigned by the Outerview internal visual search pipeline.

Uses

Intended Uses

Municipal asset teams can use the dataset for monitoring roads, worksites, and infrastructure changes over time. AV perception engineers, insurance underwriters, environmental analysts, and retail site selection teams can use it for image classification, geospatial analysis, visual search, and location-aware model development.

Out-of-Scope Uses

Not intended for facial recognition, individual identification, or surveillance of private individuals.

License

This dataset is released under the CC BY 4.0 license. Free for research and commercial use with attribution.

Citation

@dataset{outerview_construction-infrastructure-change_2026,
  title={Construction & Infrastructure Change Visual Dataset},
  author={Outerview},
  year={2026},
  url={https://huggingface.co/datasets/outerview/construction-infrastructure-change}
}

About Outerview

Outerview is a research lab focused on understanding the physical world at planetary scale.

We are building systems that can organize the world's physical information and make it accessible and usable transforming raw imagery, video, location, and spatial context into knowledge that people and machines can search, interpret, and act on.

Our belief is simple: the physical world should be as searchable and understandable as the digital world.