index.md

layout	home
title	MARINA Dataset
description	A large labeled dataset for underwater acoustic target recognition.
hero	background_image subheadline headline text buttons /assets/images/coming-soon-background.jpg Open benchmark · Underwater Acoustics <strong>UniqueShip:</strong> Large, public underwater acoustic target recognition (UATR) datasets for ships 2,460 hours of ship-radiated noise from 4,218 unique vessels, split by vessel ID so no ship appears in both training and test split. Sourced from the Ocean Networks Canada (ONC) repository. label url style Browse releases #releases primary label url style Read the overview #overview outline
overview	subheadline headline paragraphs cta_section stats Overview Built to train generalizable UATR models using leakproof splits UniqueShip pairs hydrophone recordings from seven ONC deployments in the Strait of Georgia (May 2016 – November 2023) with AIS vessel tracking data. Each 5-second sample is labeled with its vessel class and 17 AIS metadata fields. Unlike earlier ONC-based datasets, every split keeps each vessel in a single partition and groups background audio by day, so test accuracy reflects performance on ships the model has never heard. paragraphs buttons <strong>The paper</strong> describes the dataset in more detail and includes results with and without data leakage, ablations on vessel diversity vs. audio duration, a metadata analysis, and additional baseline results. label url target style Read the paper https://arxiv.org/pdf/2609.13659 _blank primary value label 3,437 Hours of ship & background audio value label 4,218 Unique vessels value label 11 Vessel classes value label 2.5M 5-second recordings
difference	subheadline headline items What makes it different? Larger, more diverse, and free of data leakage that inflates other benchmarks title text Leak-free splits Vessel audio is grouped by MMSI and background by day instead of random splitting. On previous datasets, random splitting inflated accuracy by 10–48 points. title text Largest open ONC dataset The balanced benchmark subset alone has 4× the audio and 12× the vessels of DeepShip, and 70% more audio than the unbalanced Oceanship dataset. title text Rich AIS metadata 17 fields per sample, including MMSI, distance to hydrophone, speed, course, length, beam, draught, and navigation status. title text Ready-made splits Choose anything from a 25-hour quick-start subset to the full 3,437-hour corpus, with five 80/10/10 folds. title text Baselines included MobileNetV3, ViT-B/16, and SwinV2 with STFT and Mel inputs. The best result is 66.5% accuracy (Swin + Mel). title text Cleaner background class 8km ship-free radius ensures quieter ambient samples for the background class
releases	subheadline headline text items Data releases Current dataset splits All splits are vessel-disjoint and include per-sample AIS metadata. Samples are 5-second clips at 20 kHz; full-length recordings are available through the codebase. *Request Google Drive permission to access the current splits*. title image image_alt description size views date buttons 5 Class - Balanced /assets/images/5shiptypes.png Aerial view of five vessel classes tracked in open water Contains the main 5 classes (Tug/Tow, Tanker, Passengership, Cargo) and balances the total audio for each class such that they are equal. Current version = 1.0 89 GB (Unzipped), 59 GB (Zipped) 213h, 3175 vessels, 5 classes Released September 2026 label url target style Download audio https://drive.google.com/drive/folders/1G0e-2Nhi6CWKuBRStlR8GTtcGyYiT2h6?usp=drive_link _blank primary label url target style Download spectrograms https://drive.google.com/drive/folders/1a6wLqfuBh3PMqSlZfy-IVjTfrJMtqo11?usp=sharing _blank primary title image image_alt description size views date buttons 12 Class - 5 Hours Each /assets/images/moreshiptypes.png Diverse vessels tracked in a busy coastal shipping channel Contains all ship classes and balances the total audio such that it is 5 hours each class. Current version = 1.0 10 GB (Unzipped), 7 GB (Zipped) 60h, 4218 vessels, 12 classes Released September 2026 label url target style Download audio https://drive.google.com/drive/folders/1HxV8AGh8ckZS3JehAU_gY2fuCnscX-eV?usp=drive_link _blank primary label url target style Download spectrograms https://drive.google.com/drive/folders/1xOXX4dUmtSET7Utb4gfscZp1dlPoT76e?usp=drive_link _blank primary
leaderboards	subheadline headline status text preview Benchmark results Leaderboards Coming soon Compare published results across the UniqueShip dataset splits. Rankings, evaluation metrics, and submission guidance will be available following the dataset release. columns rows Rank Submission Dataset split Score Updated 4
inquiries	subheadline headline text benefits form Inquiries Need additional information or a different split? Current dataset splits are available to <a href="#releases" class="text-primary hover:underline">download directly</a> — no request or approval is required. Use this form if you have questions, need additional information, or would like to request a split that is not currently available. Ask questions about the data or documentation Request additional or specialized dataset splits action submit_label submitting_label error_message success fields https://docs.google.com/forms/u/0/d/e/1FAIpQLScgdYqByeXFuj-dDJ-7_McmpmoaArhqkgZZsilMZhcHieopAA/formResponse Submit inquiry Submitting… We could not send your request. Check your connection and try again. eyebrow headline text Request received Thanks for your submission We have received your request and will be in touch. type name label autocomplete required width text entry.925008751 Full name name true half type name label autocomplete required width email entry.2144715529 Work email email true half type name label autocomplete required width text entry.241770130 Organization organization false full type name label required width textarea entry.1262269016 Inquiry true full
citation	subheadline headline text code contact Citing this dataset Reference the dataset paper If you use UniqueShip, please cite the paper below. @inproceedings{hashemi_2026_uniqueship, author = {Hashemi, Connor and Stout, Trevor and Hoogs, Anthony and Parham, Jason}, title = {UniqueShip: Mitigating Data Leakage in Acoustic Ship Classification Benchmark Datasets}, booktitle = {OCEANS 2026}, year = {2026}, pages = {TODO} } headline text button_label url Questions, corrections, or collaboration? Our team can help with access and research partnerships. Contact the team https://www.kitware.com/contact/