CARLA Simulator Semantic Segmentation Images

20,000 synthetic images generated using the CARLA Simulator for semantic segmentation tasks. The dataset includes variations across different virtual maps and simulated weather conditions. The data was created using the CARLA open-source autonomous driving simulator.

Use Cases

• Train semantic segmentation models on 20,000 synthetic images with pixel-level labels.
• Evaluate model robustness using images from different simulated weather conditions.
• Benchmark segmentation algorithms across varied virtual map environments.
• Develop domain adaptation techniques from synthetic to real-world driving data.

Strengths

• 20,000 labeled images provides a substantial training corpus.
• Includes controlled variations in weather and map conditions for robustness testing.

Limitations

• Data is synthetic, which may not fully capture real-world complexity and noise.
• Unknown class distribution and potential label imbalance.

Provenance

Source

CARLA Simulator

Collection Method

Synthetically generated using the CARLA autonomous driving simulation platform.