Sold by: MIT Lincoln Laboratory
Open data
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Deployed on AWS
The ARPA-I INSIGHTS Dataset is a large-scale, high-density (~85 pts/m) geiger-mode aerial LiDAR dataset covering over 1,600 km across the Salt Lake City, UT and Denver, CO metropolitan regions, including major freeway corridors. Collected in June 2025, the dataset provides dense three-dimensional measurements with approximately 30 cm vertical and horizontal point spacing, enabling detailed representation of transportation infrastructure and surrounding urban environments. The data was acquired as part of a U.S. Department of Transportation ARPA-I-funded project, executed by MIT Lincoln Laboratory, to support state and regional transportation agencies. The dataset is designed to enable transportation digital twins, supporting virtual inspection, measurement, and inventory of infrastructure assets at corridor and regional scales. In addition to raw point clouds, the dataset includes annotated subsets for machine learning, with labeled classes for surface types (e.g., road, sidewalk,[...]
Overview
The ARPA-I INSIGHTS Dataset is a large-scale, high-density (~85 pts/m²) geiger-mode aerial LiDAR dataset covering over 1,600 km² across the Salt Lake City, UT and Denver, CO metropolitan regions, including major freeway corridors. Collected in June 2025, the dataset provides dense three-dimensional measurements with approximately 30 cm vertical and horizontal point spacing, enabling detailed representation of transportation infrastructure and surrounding urban environments. The data was acquired as part of a U.S. Department of Transportation ARPA-I-funded project, executed by MIT Lincoln Laboratory, to support state and regional transportation agencies. The dataset is designed to enable transportation digital twins, supporting virtual inspection, measurement, and inventory of infrastructure assets at corridor and regional scales. In addition to raw point clouds, the dataset includes annotated subsets for machine learning, with labeled classes for surface types (e.g., road, sidewalk, trail, driveway) and infrastructure elements (e.g., power lines, traffic signals, light poles). The data is optimized for cloud-based access through a STAC catalog with GeoParquet index, and tiled storage of cloud-optimized point clouds, supporting scalable workflows for geospatial analysis, infrastructure monitoring, and multimodal AI research.
Features and programs
Open Data Sponsorship Program
This dataset is part of the Open Data Sponsorship Program, an AWS program that covers the cost of storage for publicly available high-value cloud-optimized datasets.
Pricing
This is a publicly available data set. No subscription is required.
Legal
Content disclaimer
Vendors are responsible for their product descriptions and other product content. AWS does not warrant that vendors' product descriptions or other product content are accurate, complete, reliable, current, or error-free.
Delivery details
AWS Data Exchange (ADX)
AWS Data Exchange is a service that helps AWS easily share and manage data entitlements from other organizations at scale.
Open data resources
Available with or without an AWS account.
- How to use
- To access these resources, reference the Amazon Resource Name (ARN) using the AWS Command Line Interface (CLI). Learn more
- Description
- S3 bucket containing STAC geoparquet indices, tiled COPC LAZ and LAS point clouds, and metadata for the INSIGHTS LiDAR, GIS-Surface-Labels, and Manual-Semantic-Labels datasets.
- Resource type
- S3 bucket
- Amazon Resource Name (ARN)
- arn:aws:s3:::arpa-i-insights
- AWS region
- us-west-2
- AWS CLI access (No AWS account required)
- aws s3 ls --no-sign-request s3://arpa-i-insights/
- Description
- Notifications for INSIGHTS bucket updates
- Resource type
- SNS topic
- Amazon Resource Name (ARN)
- arn:aws:sns:us-west-2:218254303756:arpa-i-insights-object_created
- AWS region
- us-west-2
Resources
Vendor resources
Support
Managed By
MIT Lincoln Laboratory
How to cite
ARPA-I INSIGHTS Dataset was accessed on DATE from https://registry.opendata.aws/arpa-i-insights .
License
CC-BY-4.0
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