# Integration of Roadside Camera Images and Weather Data for Monitoring Winter Road Surface Conditions

arXiv: https://arxiv.org/abs/2009.12165 · HTML: https://mlsanity.com/paper/2009.12165/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Sep 2020.

## Built on by (most cited first, 5 of 5)

- [Road Surface Translation Under Snow-covered and Semantic Segmentation for Snow Hazard Index](https://mlsanity.com/paper/2101.05616/) (2021)
- [Snowy Night-to-Day Translator and Semantic Segmentation Label Similarity for Snow Hazard Indicator](https://mlsanity.com/paper/2103.00545/) (2021)
- [VAE-iForest: Auto-encoding Reconstruction and Isolation-based Anomalies Detecting Fallen Objects on Road Surface](https://mlsanity.com/paper/2203.01193/) (2022)
- [Label Correction for Road Segmentation Using Road-side Cameras](https://mlsanity.com/paper/2502.01281/) (2025)
- [Machine Learning Detection of Road Surface Conditions: A Generalizable Model using Traffic Cameras and Weather Data](https://mlsanity.com/paper/2510.06440/) (2025)

## Abstract (arXiv)

During the winter season, real-time monitoring of road surface conditions is critical for the safety of drivers and road maintenance operations. Previous research has evaluated the potential of image classification methods for detecting road snow coverage by processing images from roadside cameras installed in RWIS (Road Weather Information System) stations. However, there are a limited number of RWIS stations across Ontario, Canada; therefore, the network has reduced spatial coverage. In this study, we suggest improving performance on this task through the integration of images and weather data collected from the RWIS stations with images from other MTO (Ministry of Transportation of Ontario) roadside cameras and weather data from Environment Canada stations. We use spatial statistics to quantify the benefits of integrating the three datasets across Southern Ontario, showing evidence of a six-fold increase in the number of available roadside cameras and therefore improving the spatial coverage in the most populous ecoregions in Ontario. Additionally, we evaluate three spatial interpolation methods for inferring weather variables in locations without weather measurement instruments and identify the one that offers the best tradeoff between accuracy and ease of implementation.