Weather Classification by Utilizing Synthetic Data

Saad Minhas*, Zeba Khanam, Shoaib Ehsan, Klaus McDonald-Maier, Aura Hernández-Sabaté

*Corresponding author for this work

Research output: Contribution to journalArticleResearchpeer-review


Weather prediction from real-world images can be termed a complex task when targeting classification using neural networks. Moreover, the number of images throughout the available datasets can contain a huge amount of variance when comparing locations with the weather those images are representing. In this article, the capabilities of a custom built driver simulator are explored specifically to simulate a wide range of weather conditions. Moreover, the performance of a new synthetic dataset generated by the above simulator is also assessed. The results indicate that the use of synthetic datasets in conjunction with real-world datasets can increase the training efficiency of the CNNs by as much as 74%. The article paves a way forward to tackle the persistent problem of bias in vision-based datasets.

Original languageEnglish
Article number3193
Issue number9
Publication statusPublished - 1 May 2022


  • advanced driver assistance systems
  • autonomous car
  • computer vision
  • dataset
  • deep learning
  • intelligent transportation systems
  • synthetic data
  • weather classification


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