Model Inventory of Roadway Elements (MIRE) Road Surface Type Identification

Background

The Michigan Department of Transportation (MDOT) has been working on meeting the Federal Highway Administration (FHWA) requirements for the Model Inventory of Roadway Elements (MIRE). More information about the MIRE program can be accessed at the United States' DOT website.

Previous state GIS road layer does not have road surface type for over half of Michigan roads, as of 2022
There are over 122,000 centerline miles of public roads

92% managed by over 600 local agencies in Michigan, 21% of this is federal aid eligible

Methods

We built from the methods originally established to analyze high-resolution, 4-band imagery to identify paved vs. unpaved roads.

Brooks, C.N., Dean, D.B., Dobson, R.J., Roussi, C., Carter, J.F., VanderWoude, A.J., Colling, T. and Banach, D.M., 2017. Identification of unpaved roads in a regional road network using remote sensing. Photogrammetric Engineering & Remote Sensing, 83(5):377-383

Four-band imagery of Oakland County, MI — Example 3-inch Resolution, 4-band imagery for Oakland County, MI. The four bands are blue, green, red, and near-infrared (NIR)

In Phase I of the MIRE project, we assessed three artificial intelligence (AI) models for pixel classification of paved vs. unpaved:

XGBoost (fastest, accurate) – XGB (selected as our AI/ML tool)
Random Forest (slower, accurate) - RF

Gaussian Copula (lower accuracy) – GC

map view of an intersection before and after road classifcation — Example Imagery Analysis with XGBoost

In Phase 2, we added Concrete vs. Asphalt classification and addressed the issue of roads not aligned with imagery.

Map of Monroe County, MI showing positional offsets of roads — Road Misalignment Offsets Seen for Monroe County, MI

Results

Results were generated for all lane miles in Michigan.

Map of Michigan showing all roads have been labeled — Road Classifications for all Road Miles in Michigan

Work completed in August, 2024 and all data delivered to MDOT, with ESRI-compatible metadata
Mean F1 accuracy score was 95.7% for asphalt, 67.3% for concrete (more challenging), and 80.2% for unpaved roads
Mapping totaling 93,713.4 miles of asphalt roads; 8,652.3 miles of concrete roads (102,366 miles paved total), and 43,385.8 miles of unpaved roads.
- Surface type identification for 145,752 miles total
Results were approved for a poster presentation at TRB 2025
Methods are expandable to other states and countries

Map of Wayne County, MI showing road classifications — Road Classifications for Wayne County, MI Produced by Machine Learning Methods

Map of Houghton, MI showing road type classifications — Road Classification for Houghton County, MI Produced by Machine Learning Methods

Publications

You can read more about the project's methods, analyses, and outcomes in this publication for the project.

Brooks, C., Sawtell, R., Redhuis, D., Janiszewski, S., Chavez, A., Barber, V., Dobson, R., Graham, J., Colling, T., Kozsykowski, N., LaCost, A., Deljevic, G., Jenkins, A., Toth, M., Himmelein, A. 2025. Updated Methods for Identification of Road Surface Type using Machine Learning-Enabled Remote Sensing. Proceedings of the Transportation Research Board 104th Annual Meeting, 20 pgs.