DOI: 10.11583/dtu.21511815.v1
title: Labelled evaluation datasets of AIS Trajectories from Danish Waters for Abnormal Behavior Detection

description: This item is part of the collection "AIS Trajectories from Danish Waters for Abnormal Behavior Detection" <br> DOI: https://doi.org/10.11583/DTU.c.6287841 <br> Using Deep Learning for detection of maritime abnormal behaviour in spatio temporal trajectories is a relatively new and promising application. Open access to the Automatic Identification System (AIS) has made large amounts of maritime trajectories publically avaliable. However, these trajectories are unannotated when it comes to the detection of abnormal behaviour. The lack of annotated datasets for abnormality detection on maritime trajectories makes it difficult to evaluate and compare suggested models quantitavely. With this dataset, we attempt to provide a way for researchers to evaluate and compare performance. <br> We have manually labelled trajectories which showcase abnormal behaviour following an collision accident. The annotated dataset consists of 521 data points with 25 abnormal trajectories. The abnormal trajectories cover amoung other; Colliding vessels, vessels engaged in Search-and-Rescue activities, law enforcement, and commercial maritime traffic forced to deviate from the normal course <br> These datasets consists of labelled trajectories for the purpose of evaluating unsupervised models for detection of abnormal maritime behavior. For unlabelled datasets for training please refer to the collection. Link in Related publications.<br> The dataset is an example of a SAR event and cannot not be considered representative of a large population of all SAR events. <br> The dataset consists of a total of 521 trajectories of which 25 is labelled as abnormal. the data is captured on a single day in a specific region. The remaining normal traffic is representative of the traffic during the winter season. The normal traffic in the ROI has a fairly high seasonality related to fishing and leisure sailing traffic. <br> The data is saved using the pickle format for Python. Each dataset is split into 2 files with naming convention: datasetInfo_XXX data_XXX Files named "data_XXX" contains the extracted trajectories serialized sequentially one at a time and must be read as such. Please refer to provided utility functions for examples. Files named "datasetInfo" contains Metadata related to the dataset and indecies at which trajectories begin in "data_XXX" files.<br> The data are sequences of maritime trajectories defined by their; timestamp, latitude/longitude position, speed, course, and unique ship identifer MMSI. In addition, the dataset contains metadata related to creation parameters. The dataset has been limited to a specific time period, ship types, moving AIS navigational statuses, and filtered within an region of interest (ROI). Trajectories were split if exceeding an upper limit and short trajectories were discarded. All values are given as metadata in the dataset and used in the naming syntax.<br> Naming syntax: data_AIS_Custom_STARTDATE_ENDDATE_SHIPTYPES_MINLENGTH_MAXLENGTH_RESAMPLEPERIOD.pkl<br> See datasheet for more detailed information and we refer to provided utility functions for examples on how to read and plot the data. <br>