{"url":"/dataset/semanticspray-dataset","name":"SemanticSpray Dataset","full_name":null,"description_markdown":"[Homepage](https://semantic-spray-dataset.github.io/) | [GitHub](https://github.com/aldipiroli/semantic_spray_dataset)\r\n\r\nLiDARs are one of the main sensors used for autonomous driving applications, providing accurate depth estimation regardless of lighting conditions. However, they are severely affected by adverse weather conditions such as rain, snow, and fog.\r\n\r\nThis dataset provides semantic labels for a subset of the [Road Spray dataset](https://www.fzd-datasets.de/spray/), which contains scenes of vehicles traveling at different speeds on wet surfaces, creating a trailing spray effect. We provide semantic labels for over 200 dynamic scenes, labeling each point in the LiDAR point clouds as background (road, vegetation, buildings, ...), foreground (moving vehicles), and noise (spray, LiDAR artifacts). \r\n\r\nThe SemanticSpray dataset contains scenes in wet surface conditions captured by Camera, LiDAR, and Radar.\r\n\r\nThe following label types are provided:\r\n\r\n- **Camera**: 2D Boxes\r\n\r\n- **LiDAR**: 3D Boxes, Semantic Labels\r\n\r\n- **Radar**: Semantic Labels","description_withheld":null,"homepage":"https://semantic-spray-dataset.github.io/","introduced_date":"2023-07-01","introduced_date_note":null,"introduced_by":null,"license":null,"modalities":[{"name":"3D","url":"/datasets/modality/3d"}],"tasks":[{"name":"Semantic Segmentation","url":"/task/semantic-segmentation","datasets_with_task":"/datasets/task/semantic-segmentation"}],"languages":[],"variants":["SemanticSpray Dataset"],"data_loaders":[{"repo":"https://github.com/aldipiroli/semantic_spray_dataset","url":"https://semantic-spray-dataset.github.io/","frameworks":["pytorch"]}],"num_papers_in_archive":2,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[],"papers_with_a_benchmark_row":[],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}