Scalable Estimation of Port Areas from AIS Data
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Leonardo Maria Millefiori, Luca Cazzanti, Dimitris Zissis, Gianfranco Arcieri
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Abstract - This paper discusses work in progress to estimate port locations and operational areas in a scalable, data-driven, unsupervised way. Knowing the extent of port areas is an important component of larger maritime traffic analysis systems that inform stakeholders and decision makers in the maritime industry, governmental agencies, and international organizations. The proposed approach uses Kernel Density Estimator (KDE) and exploits the large volume of Automatic Identification System (AIS) data to learn the extent of port areas in a data-driven way. Example results for the port of La Spezia, Italy, demonstrate the approach for real data.

BibTex -
@INPROCEEDINGS{MillefioriKDAD2016,
TITLE = {Scalable Estimation of Port Areas from {AIS} Data},
AUTHOR = {L. M. Millefiori and L. Cazzanti and D. Zissis and G. Arcieri},
BOOKTITLE = {Workshop on Maritime Knowledge Discovery and Anomaly Detection),
PUBLISHER = {Europen Commission Joint Research Centre},
ADDRESS = {Ispra, Italy},
MONTH = {July},
YEAR = {2016}}