README.md

metadata

pretty_name: FMUKF Containership Dataset
license: cc-by-4.0
tags:
  - simulation
  - time-series
  - maritime
  - control
  - state-estimation
  - ukf
  - foundation-models
  - hdf5
  - fmukf
task_categories:
  - time-series-forecasting
size_categories:
  - 100K<n<1M

FMUKF Containership Dataset

Dataset Summary

Containerships-Large is the dataset used in our paper:

Sailing Towards Zero-Shot State Estimation using Foundation Models Combined with a UKF (CDC 2025, preprint to be added).

Please refer to the papers's GitHub repo GitHub for details and full usage.

It contains 1,000 unique parameterizations of a 6-DOF containership model and 400 simulated trajectories per parameterization (each 384 seconds long), useful for training and testing foundation models.
The base ship model follows Son & Nomoto (1981 and the implementation are adapted from on parameters/dynamics from Thor I. Fossen’s Marine System Simulator (MSS).

• File format: single HDF5 file Containerships_Large.h5
• State vector X shape: (384, 10)
• Control vector U shape: (384, 2)

Supported Tasks & Benchmarks

• State estimation / filtering
• System identification & dynamics modeling
• Time-series forecasting / foundation models

Dataset Structure

• Total environments (ships): 1,000
• Trajectories per environment: 400
• Timesteps per trajectory: 384 (1 s per step)

Containerships_Large.h5
├── env_0/                     # First Ship Parametrization
│   ├── .attrs["parameters"]   # YAML string encoding parameters
│   ├── traj_0/                
│   │   ├── x                  # (384, 10) state trajectory
│   │   └── u                  # (384, 2) control inputs
│   ├── traj_1/
│   │   ├── x
│   │   └── u
│   └── ...
├── env_1/
│   ├── .attrs["parameters"]
│   ├── traj_0/  ...
└── ...

Data Fields

State x (10-dim):

• u — surge velocity [m/s]
• v — sway velocity [m/s]
• r — yaw rate [deg/s]
• x — North position [km]
• y — East position [km]
• psi — yaw angle [deg]
• p — roll rate [deg/s]
• phi — roll angle [deg]
• delta — rudder angle [deg]
• n — propeller shaft velocity [rpm]

Control u (2-dim,):

• delta — commanded rudder angle [deg]
• n — commanded propeller shaft velocity [rpm]

Parameter Variations

We sample 1,000 ship parameterizations by perturbing key physical parameters by ≈±30% around the original base model. We filter unstable or near-duplicate systems using a similarity metric to ensure diversity and stability. Each trajectory starts from a randomized initial state; control inputs are pink noise.

How to Use

Quick start (download via huggingface_hub)

from huggingface_hub import hf_hub_download
import h5py, yaml, numpy as np

# replace with your dataset repo id
repo_id = "your-username/containerships-large"
path = hf_hub_download(repo_id=repo_id, filename="Containerships_Large.h5", repo_type="dataset")

with h5py.File(path, "r") as f:
    env_keys = list(f.keys())

    # Select env (ship parametrization) and trajectory number
    env_key = env_keys[0]  #<-- "env_0", "env_1", ..., "env_999"
    traj_key = "traj_0"    #<-- "traj_0", "traj_1", ..., "traj_399"

    # Get State Vector and Control Inputs
    X = np.array(f[env_key][traj_key]["x"])  # (384, 10)
    U = np.array(f[env_key][traj_key]["u"])  # (384, 2)

    # Load parameters
    params = yaml.safe_load(f[env_key].attrs["parameters"])

Data Generation & Provenance

Licensing

Dataset license: CC-BY-4.0 — you may use, share, and adapt the data with attribution to the authors.

Intended Uses & Limitations

• Intended uses: research on filtering, state estimation, dynamics learning, forecasting, and control.
• Not for safety-critical use: do not use to design or validate real-world maritime operations without expert verification and domain-specific validation.

Maintenance

• Authors: Tobin Holtmann, Data Science in Mechenical Engineering, RWTH Aachen University
• Version: v1.0 (2025-09-03)

Citation

Please cite our paper and acknowledge the dataset:

@inproceedings{Holtmann2025FMUKF,
  title     = {Sailing Towards Zero-Shot State Estimation using Foundation Models Combined with a UKF},
  booktitle = {IEEE Conference on Decision and Control (CDC)},
  year      = {2025},
  url       = {https://github.com/Data-Science-in-Mechanical-Engineering/fm-ukf}
}