VesselVision

Source code for the "VesselVision - Fleet Safety Awareness over Streaming Vessel Trajectories" demonstration paper

Installation

In order to use VesselVision in your project, download all necessary modules in your directory of choice via pip or conda, and install their corresponding dependencies, as the following commands suggest:

# Using pip/virtualenv
pip install −r requirements.txt

# Using conda
conda install --file requirements.txt

In order to install ST_Visions, please consult the documentation at https://github.com/DataStories-UniPi/ST-Visions.

Usage

To see VesselVision in action, please visit https://www.datastories.org/vesselvision/.

To use VesselVision at first run the VesselVision.py script using the following command

python VesselVision.py

which will initialize Zookeeper and Apache Kafka and create the Topics needed for the data stream and its results. Afterwards, in order to instantiate the web application for the visualization, run the following command

python -m bokeh serve --show ./monitor --allow-websocket-origin=<SERVER_IP>

Adjusting the parameters of VesselVision is possible via the lib/kafka_config_c_p_v01.py file.

To train a VCRA model from scratch, please consult the code at https://github.com/DataStories-UniPi/VCRA. For convenience, a pretrained VCRA model is provided in the '''data/pickle''' directory.

Contributors

Andreas Tritsarolis; Department of Informatics, University of Piraeus

Nikos Pelekis; Department of Statistics & Insurance Science, University of Piraeus

Yannis Theodoridis; Department of Informatics, University of Piraeus

Citation

If you use VesselVision in your project, we would appreciate citations to the following paper:

Andreas Tritsarolis, Nikos Pelekis, and Yannis Theodoridis. 2023. VesselVision: Fleet Safety Awareness over Streaming Vessel Trajectories (Demo Paper). In proceedings of the 31st ACM International Conference on Advances in Geographic Information Systems (SIGSPATIAL). https://doi.org/10.1145/3589132.3625573

Acknowledgement

This work was supported in part by the Horizon Framework Programmes of the European Union under grant agreements No 957237 (VesselAI; https://vessel-ai.eu) and No 101070279 (MobiSpaces; https://mobispaces.eu).