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Fort Benton

Overview

We provided end-to-end remote sensing services to support the design of a new bridge under MDT’s standards. We began by establishing precise survey control tied into MDT’s statewide network: GNSS base stations were set up over permanent benchmarks, and total station traverses were used to connect these points to MDT’s horizontal and vertical control monuments. Once control points were firmly established, our team planned and executed UAV flights over the entire project corridor, capturing both aerial imagery and lidar returns over the vegetated banks, exposed gravel bars, and portions of the existing roadway that intersect the proposed bridge alignment. At the same time, a survey crew performed conventional topographic checks around critical features—such as the existing bridge abutments, guardrail ends, and roadway centerline—using total stations and GNSS receivers to measure spot elevations. Back in the office, we post-processed the lidar point clouds and aerial imagery in tandem, merging them into a seamless, design-grade surface. During quality assurance, all UAV-derived elevations were compared against the field checks; any discrepancies identified in spot checks near structural elements were reconciled by revisiting the raw data or, if necessary, conducting a brief field verification. Once satisfied that the data met MDT’s tolerances, we generated a LandXML file representing the final topographic surface, complete with breaklines inferred from the lidar data and verified by our ground checks.

Project Size

N/A

Project Value

$N/A

Year Completed

2025

Details

In addition to the LandXML surface, we delivered georeferenced orthomosaics that HDR could overlay in their CAD environment for planimetric reference, a Digital Surface Model (DSM) suitable for visualizing existing terrain and vegetation, and high-density point cloud files to allow detailed spatial analysis of bridge substructure locations. Throughout the process, we maintained detailed field notes that documented our control setup, conventional survey observations, and any adjustments made during QA. The outcome was a reliable, design-ready dataset that HDR leveraged to develop bridge geometry, select appropriate approach grades, and prepare environmental permitting submittals—all while minimizing additional field visits thanks to our thorough integration of UAV and conventional survey data.

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