# Machine Learning Detection of Road Surface Conditions: A Generalizable Model using Traffic Cameras and Weather Data

arXiv: https://arxiv.org/abs/2510.06440 · HTML: https://mlsanity.com/paper/2510.06440/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 56 references that are in the map.
- First posted on arXiv 7 Oct 2025.

## Builds on (most cited first, 10 of 10)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017)
- [Xception: Deep Learning with Depthwise Separable Convolutions](https://mlsanity.com/paper/1610.02357/) (2016)
- [Integration of Roadside Camera Images and Weather Data for Monitoring Winter Road Surface Conditions](https://mlsanity.com/paper/2009.12165/) (2020)
- [Winter Road Surface Condition Recognition Using A Pretrained Deep Convolutional Network](https://mlsanity.com/paper/1812.06858/) (2018)
- [Design of Efficient Deep Learning models for Determining Road Surface Condition from Roadside Camera Images and Weather Data](https://mlsanity.com/paper/2009.10282/) (2020)
- [A Machine Learning Tutorial for Operational Meteorology, Part II: Neural Networks and Deep Learning](https://mlsanity.com/paper/2211.00147/) (2022)

## Abstract (arXiv)

Transportation agencies make critical operational decisions during hazardous weather events, including assessment of road conditions and resource allocation. In this study, machine learning models are developed to provide additional support for the New York State Department of Transportation (NYSDOT) by automatically classifying current road conditions across the state. Convolutional neural networks and random forests are trained on NYSDOT roadside camera images and weather data to predict road surface conditions. This task draws critically on a robust hand-labeled dataset of ~22,000 camera images containing six road surface conditions: severe snow, snow, wet, dry, poor visibility, or obstructed. Model generalizability is prioritized to meet the operational needs of the NYSDOT decision makers, including integration of operational datasets and use of representative and realistic images. The weather-related road surface condition model in this study achieves an accuracy of 81.5% on completely unseen cameras. With operational deployment, this model has the potential to improve spatial and temporal awareness of road surface conditions, which can strengthen decision-making for operations, roadway maintenance, and traveler safety, particularly during winter weather events.