AIS Trajectories from Danish Waters for Abnormal Behavior Detection
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 available. 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 quantitatively. With this dataset, we attempt to provide a way for researchers to evaluate and compare performance.
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 among other; Colliding vessels, vessels engaged in Search-and-Rescue activities, law enforcement, and commercial maritime traffic forced to deviate from the normal course
This collection consists of two datasets. One dataset consists of unlabelled trajectories for the purpose of training unsupervised models. This dataset can be considered representative of normal maritime traffic during the collection period. The normal traffic in the ROI has a fairly high seasonality related to fishing and leisure sailing traffic. The other dataset consists of a total of 521 trajectories of which 25 is labelled as abnormal for the purpose of evaluation. The labelled dataset is an example of a SAR event and cannot not be considered representative of a large population of all SAR events. The remaining normal traffic in the labelled dataset is representative of the traffic during the winter season.
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 indices at which trajectories begin in "data_XXX" files.
The data are sequences of maritime trajectories defined by their; timestamp, latitude/longitude position, speed, course, and unique ship identifier 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.
Naming syntax: data_AIS_Custom_STARTDATE_ENDDATE_SHIPTYPES_MINLENGTH_MAXLENGTH_RESAMPLEPERIOD.pkl
See datasheet for more detailed information and we refer to provide utility functions for examples on how to read and plot the data on Github.
Github:
https://github.com/KristofferOlesen/Datasets-of-AIS-Trajectories-from-Danish-Waters/
