Roadscapes: 8,981 Annotated Images for Autonomous Driving in India

Roadscapes is a multitask multimodal dataset for autonomous driving scene understanding. It contains 8,981 annotated images capturing diverse and unstructured road environments typical of India. The dataset was created by author vijpandaturtle and was last updated on the platform in February 2026.

Use Cases

• Train object detection models for vehicles and pedestrians based on annotated images.
• Develop scene segmentation algorithms for unstructured road environments described in the overview.
• Benchmark multitask learning models for autonomous driving using multimodal annotations.
• Study domain adaptation for autonomous systems in diverse geographic settings like Indian roads.

Strengths

• Contains 8,981 annotated images, providing a substantial base for model training.
• Focuses on diverse and unstructured Indian road environments, a specific and challenging domain.
• Designed for multitask multimodal understanding, suggesting multiple annotation types per image.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• Description metadata is limited; actual data quality requires manual inspection after download.

Provenance

Source

huggingface user vijpandaturtle

Freshness

Last updated 2026-02-22 09:01:27; freshness should be verified.

Geography

Indian road environments