Weather Classification by Utilizing Synthetic Data
In one sentence. Synthetic driving-simulator images increase CNN weather-condition classification training efficiency by up to 74%, addressing dataset bias in vision-based weather recognition.
Abstract
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.
Labels
- Topic
- Observations & remote sensing
- Type
- New model or method
- Method
- CNN / U-Net
- Domain
- Local / station
- Data
- Other
- Status
- Research
- Identifiers
- DOI:10.3390/s22093193 OpenAlex:W4224315532
- Added
- 2026-10-22
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