Grants and Contributions
About this information
In June 2016, as part of the Open Government Action Plan, the Treasury Board of Canada Secretariat (TBS) committed to increasing the transparency and usefulness of grants and contribution data and subsequently launched the Guidelines on the Reporting of Grants and Contributions Awards, effective April 1, 2018.
The rules and principles governing government grants and contributions are outlined in the Treasury Board Policy on Transfer Payments. Transfer payments are transfers of money, goods, services or assets made from an appropriation to individuals, organizations or other levels of government, without the federal government directly receiving goods or services in return, but which may require the recipient to provide a report or other information subsequent to receiving payment. These expenditures are reported in the Public Accounts of Canada. The major types of transfer payments are grants, contributions and \'other transfer payments\'.
Included in this category, but not to be reported under proactive disclosure of awards, are (1) transfers to other levels of government such as Equalization payments as well as Canada Health and Social Transfer payments. (2) Grants and contributions reallocated or otherwise redistributed by the recipient to third parties; and (3) information that would normally be withheld under the Access to Information Act and the Privacy Act.
$150,000.00
Nov 1, 2022
For-profit organization
AI-driven Mobile LiDAR Segmentation in a Scalable Cloud-Based Environment
997722
This project will enable Nektar to transition its AI-driven LiDAR processing algorithms into a cloud-based solution, providing our global customer base with direct access to our state-of-the-art processes in a flexible, scalable, and subscription-based environment.
$250,000.00
May 1, 2020
For-profit organization
Utilizing Deep Learning for Fully Automated Feature Extraction from Mobile LiDAR Scans of Highways
952279
The proposed work aims to advance Nektar’s LiDAR processing capabilities by developing Deep Neural Networks for fully automated point cloud processing. Over the past 2 years, Nektar has invested heavily in mobile laser scanning equipment while also developing multiple state-of-the-art handcrafted algorithms for efficient processing of LiDAR datasets for transportation infrastructure projects. As the LiDAR market grows, Nektar is challenged with improving the efficiency of its processing to handle 100s of million points and Gigabytes/Terabytes of data. Nektar aims to employ Artificial Intelligence to develop models for automated simultaneous detection of multiple objects within the volumetric, exhaustive, and unorganized point clouds.
$20,000.00
Jul 1, 2018
For-profit organization
Youth-Green: Streamlining data extraction process from mobile lidar to help identify highway safety deficiencies.
909188
Develop algorithms that will be able to use point cloud data and establish the identity, location, type, visibility etc. of roadside assets, primarily signage.