Roadway Inventory Video and LiDAR (RIVaL)
Closed State & local contract opportunity Posted
- Solicitation number
- 601440000043179
- State
- Texas
- Responses due
Opportunity facts
- Jurisdiction
Opportunity details come from txsmartbuy.com. Updated .
About this opportunity
The Texas Department of Transportation (TxDOT) is seeking to establish a contract for services related to imagery and LiDAR (Light Detection and Ranging) data collection, data extraction of roadway attributes, and hosting services for all TxDOT state-maintained roadways, as well as non-state-maintained roadways that are Federally Functionally Classified above Local. The contract will be for a Software as a Service (SaaS) street view web-based application that allows users to navigate through high-resolution imagery and take measurements against the LiDAR point cloud. The solicitation is a re-solicitation of a previous opportunity, 601440000038637, and is open for responses until March 27, 2024.
The solicitation utilizes the National Institute of Governmental Purchasing (NIGP) code to standardize purchases, and TxDOT is offering an Early Payment Program to respondents that provide prioritized invoice processing and payments. Vendors must register through the Bonfire website to respond, and new suppliers can apply to be on the Centralized Master Bidders List (CMBL) to receive bidding opportunities. No information is provided about set-aside designations, incumbent vendors, or award values.
Opportunity text
This is a re-solicitation of 601440000038637. It is to establish a contract for services for imagery and LiDAR (Light Detection and Ranging) data collection, data extraction of roadway attributes from collected imagery and LiDAR, and hosting services for all TxDOT state-maintained roadways, as well as non-state-maintained roadways that are Federally Functionally Classified above Local via a Software as a Service (SaaS) street view web-based application in which the user can navigate through the high-resolution imagery and take measurements against the LiDAR point cloud stacked behind the imagery.
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