MobiML

Framework for machine learning from movement data

Development of this framework was inspired by https://github.com/wherobots/GeoTorchAI

Installation

Note: As of today (2025-03-18), one of our main dependencies, pymeos, is not available on Windows. Therefore we recommend using MobiML on Linux.

Development installation

Install uv.

Clone this repository.

Set up the project:

uv sync

Run tests:

uv run pytest

In your application that uses mobiml, add these lines to the pyproject.toml file:

[tool.hatch.metadata]
allow-direct-references = true

and install

uv add  ../my/local/mobiml

For an introduction to uv, see e.g. the docs.

MobiML modules

MobiML contains various modules for learning and data preprocessing for movement data.

• datasets: This module contains classes for handling popular movement datasets.
• models: This module contains models for a variety of mobility-related ML tasks.
• preprocessing: This module contains tools to preprocess movement data to make it ready for ML development. Preprocessing tools always return a mobiml.Dataset object.
• samplers: This module contains tools for sampling movement data while accounting for its spatiotemporal characteristics.
• transforms: This module contains various transformation operations that can be applied to datasets. Transforms convert a mobiml.Dataset into a different data structure.

Documentation

Usage examples are provided in the examples directory, with instructions.

Included models

• GeoTrackNet -- Anomaly detection in maritime traffic patterns based on https://github.com/CIA-Oceanix/GeoTrackNet, as presented in Nguyen, D., Vadaine, R., Hajduch, G., Garello, R. (2022). GeoTrackNet - A Maritime Anomaly Detector Using Probabilistic Neural Network Representation of AIS Tracks and A Contrario Detection. In IEEE Transactions on Intelligent Transportation Systems, 23(6). arXiv:1912.00682
• Nautilus -- Vessel Route Forecasting (VRF) based on https://github.com/DataStories-UniPi/Nautilus, as presented in Tritsarolis, A., Pelekis, N., Bereta, K., Zissis, D., & Theodoridis, Y. (2024). On Vessel Location Forecasting and the Effect of Federated Learning. In Proceedings of the 25th Conference on Mobile Data Management (MDM). arXiv:2405.19870.
• SummarizedAISTrajectoryClassifier -- A basic example model implementing LogisticRegression for trajectory classification in a federated learning setting.

Publications

[0] Graser, A. & Dragaschnig, M. (2025). Learning From Trajectory Data With MobiML. Workshop on Big Mobility Data Analysis (BMDA2025) in conjuction with EDBT/ICDT 2025.

Acknowledgements

This work was supported in part by the Horizon Framework Programme of the European Union under grant agreement No. 101070279 (MobiSpaces).