{"url":"/dataset/waterscenes","name":"WaterScenes","full_name":null,"description_markdown":"A Multi-Task 4D Radar-Camera Fusion Dataset for Autonomous Driving on Water Surfaces description of the dataset \r\n\r\n* WaterScenes, the first multi-task 4D radar-camera fusion dataset on water surfaces, which offers data from multiple sensors, including a 4D radar, monocular camera, GPS, and IMU. It can be applied in multiple tasks, such as object detection, instance segmentation, semantic segmentation, free-space segmentation, and waterline segmentation.\r\n* Our dataset covers diverse time conditions (daytime, nightfall, night), lighting conditions (normal, dim, strong), weather conditions (sunny, overcast, rainy, snowy) and waterway conditions (river, lake, canal, moat). An information list is also offered for retrieving specific data for experiments under different conditions.\r\n* We provide 2D box-level and pixel-level annotations for camera images, and 3D point-level annotations for radar point clouds. We also offer precise timestamps for the synchronization of different sensors, as well as intrinsic and extrinsic parameters.\r\n* We provide a toolkit for radar point clouds that includes: pre-processing, labeling, projection and visualization, assisting researchers in processing and analyzing our dataset.","description_withheld":null,"homepage":"https://waterscenes.github.io","introduced_date":"2023-07-13","introduced_date_note":null,"introduced_by":{"paper":"/paper/waterscenes-a-multi-task-4d-radar-camera","title":"WaterScenes: A Multi-Task 4D Radar-Camera Fusion Dataset and Benchmarks for Autonomous Driving on Water Surfaces","first_author":"Shanliang Yao","url":null},"license":null,"modalities":[{"name":"Images","url":"/datasets/modality/images"},{"name":"Point cloud","url":"/datasets/modality/point-cloud"}],"tasks":[{"name":"Object Detection","url":"/task/object-detection","datasets_with_task":"/datasets/task/object-detection"},{"name":"Semantic Segmentation","url":"/task/semantic-segmentation","datasets_with_task":"/datasets/task/semantic-segmentation"},{"name":"2D Semantic Segmentation","url":"/task/2d-semantic-segmentation","datasets_with_task":"/datasets/task/2d-semantic-segmentation"},{"name":"Instance Segmentation","url":"/task/instance-segmentation","datasets_with_task":"/datasets/task/instance-segmentation"},{"name":"Panoptic Segmentation","url":"/task/panoptic-segmentation","datasets_with_task":"/datasets/task/panoptic-segmentation"},{"name":"Line Segment Detection","url":"/task/line-segment-detection","datasets_with_task":"/datasets/task/line-segment-detection"},{"name":"Point Cloud Segmentation","url":"/task/point-cloud-segmentation","datasets_with_task":"/datasets/task/point-cloud-segmentation"}],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["WaterScenes"],"data_loaders":[{"repo":"https://github.com/waterscenes/waterscenes","url":"https://github.com/waterscenes/waterscenes","frameworks":["pytorch"]}],"num_papers_in_archive":13,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/object-detection-on-waterscenes","task":"Object Detection","dataset_variant":"WaterScenes","rows":4,"metrics":["mAP@50-95"],"first_row_in_archive_order":{"model":"YOLOv8-M","paper":"/paper/waterscenes-a-multi-task-4d-radar-camera","metrics":{"mAP@50-95":"59.2"},"code_links":[{"title":"waterscenes/waterscenes","url":"https://github.com/waterscenes/waterscenes"},{"title":"WaterScenes/Awesome-Water-Surface-Perception","url":"https://github.com/WaterScenes/Awesome-Water-Surface-Perception"},{"title":"WaterScenes/USVTrack-Baseline-YOLO-Tracking","url":"https://github.com/WaterScenes/USVTrack-Baseline-YOLO-Tracking"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/2d-semantic-segmentation-on-waterscenes","task":"2D Semantic Segmentation","dataset_variant":"WaterScenes","rows":1,"metrics":["mIoU"],"first_row_in_archive_order":{"model":"Achelous-FV-RDF-S2","paper":"/paper/achelous-a-fast-unified-water-surface","metrics":{"mIoU":"79.6"},"code_links":[{"title":"GuanRunwei/Achelous","url":"https://github.com/GuanRunwei/Achelous"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/achelous-a-fast-unified-water-surface","title":"Achelous: A Fast Unified Water-surface Panoptic Perception Framework based on Fusion of Monocular Camera and 4D mmWave Radar","date":"2023-07-14","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"paper":"/paper/waterscenes-a-multi-task-4d-radar-camera","title":"WaterScenes: A Multi-Task 4D Radar-Camera Fusion Dataset and Benchmarks for Autonomous Driving on Water Surfaces","date":"2023-07-13","rows_on_this_dataset":2,"code_links":3,"syntology":null},{"paper":"/paper/yolox-exceeding-yolo-series-in-2021","title":"YOLOX: Exceeding YOLO Series in 2021","date":"2021-07-18","rows_on_this_dataset":1,"code_links":42,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":23,"samples_ran":1,"samples_unverified":22,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":1,"samples_harvested":23,"samples_ran":1,"samples_unverified":22,"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."}