Outerview/urban-climate-green-infrastructure

Urban Climate & Green Infrastructure Visual Dataset

Rows: 18,107

Dataset Description

Urban Climate & Green Infrastructure Visual Dataset is a global wildlife image dataset and geospatial computer vision dataset focused on street-level imagery of city features that support lower-carbon and more resilient planning. The labels cover Street Tree, Bike Lane, Solar Panel, EV Charger, Rain Garden, and Green Roof, produced through Outerview's query-driven embedding matching and feature labeling workflow. Included files are data.csv, data.geojson, and data.parquet, plus a representative image archive images-batch-0001.zip for packaged samples. The dataset is useful for image classification, visual search, municipal asset inventory, environmental analysis, and retail or infrastructure site screening across Region: Global.

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.

When image archives are included, explain naturally that the packaged images are representative examples and that larger-scale image generation, retrieval, and review should be done through the API.

Updated on a bimonthly basis.

Dataset Structure

Features in the dataset

• Street Tree: query targets visible street trees and roadside tree canopy in urban environments.
• Bike Lane: query targets dedicated cycling lanes and painted or separated bike infrastructure.
• Solar Panel: query targets rooftop or ground-mounted photovoltaic panels visible in street-level imagery.
• EV Charger: query targets electric vehicle charging equipment in public or commercial locations.
• Rain Garden: query targets planted stormwater features and bioswales designed for runoff management.
• Green Roof: query targets vegetated roof surfaces and rooftop greenery visible from the scene.

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

The records come from Outerview's full visual index, which pairs real-world imagery with geographic context for query-based discovery. Candidate observations are retrieved through the Outerview internal visual search pipeline using feature-specific similarity queries over city imagery.

Annotation

Labels were assigned at the feature level for Street Tree, Bike Lane, Solar Panel, EV Charger, Rain Garden, and Green Roof. No bounding boxes are included, and each row records the best matching visual feature label for the associated image and location metadata.

Uses

Intended Uses

Municipal asset teams can use the labels for inventory screening and streetscape audits. AV perception engineers, insurance underwriters, environmental analysts, and retail site selection teams can use the imagery and geospatial metadata for image classification, visual search, and spatial trend analysis.

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_urban-climate-green-infrastructure_2026,
  title={Urban Climate & Green Infrastructure Visual Dataset},
  author={Outerview},
  year={2026},
  url={https://huggingface.co/datasets/outerview/urban-climate-green-infrastructure}
}

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.