Unlabelled training datasets of AIS Trajectories from Danish Waters for Abnormal Behavior Detection

This item is part of the collection "AIS Trajectories from Danish Waters for Abnormal Behavior Detection"

DOI: https://doi.org/10.11583/DTU.c.6287841

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.  

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  

These datasets consists of unlabelled trajectories for the purpose of training unsupervised models. For labelled datasets for evaluation please refer to the collection. Link in Related publications.

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.

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.

Naming syntax:
data_AIS_Custom_STARTDATE_ENDDATE_SHIPTYPES_MINLENGTH_MAXLENGTH_RESAMPLEPERIOD.pkl

See datasheet for more detailed information and we refer to provided utility functions for examples on how to read and plot the data.

本项内容隶属于“丹麦水域中用于异常行为检测的AI航迹数据集”。
DOI: https://doi.org/10.11583/DTU.c.6287841

运用深度学习技术检测时空轨迹中的海事异常行为，是一项新颖且颇具潜力的应用领域。开放式的自动识别系统（AIS）使得大量的海事轨迹数据得以公开获取。然而，这些轨迹数据在异常行为检测方面并未进行标注。

海事轨迹异常行为检测标注数据集的缺失，使得对所提出模型的定量评估与比较变得尤为困难。本数据集旨在为研究人员提供一种评估和比较性能的方法。

我们对发生碰撞事故后的异常行为轨迹进行了人工标注。该标注数据集包含521个数据点，其中25个为异常轨迹。异常轨迹涵盖了包括但不限于以下情况：碰撞船舶、参与搜救活动的船舶、执法船舶以及被迫偏离正常航线的商业海事交通。

本数据集包含未标注的轨迹，用于训练无监督模型。用于评估的标注数据集，请参阅相关出版物中的数据集。

数据采用Python的pickle格式保存。
每个数据集分为两个文件，命名规范如下：

datasetInfo_XXX
data_XXX

名为"data_XXX"的文件包含序列化提取的轨迹，每个轨迹依次保存，必须按此方式读取。请参考提供的实用函数以获取示例。
名为"datasetInfo"的文件包含与数据集相关的元数据和"data_XXX"文件中轨迹开始的索引。

数据是按时间戳、纬度/经度位置、速度、航向和唯一船舶标识MMSI定义的海事轨迹序列。此外，数据集还包含了与创建参数相关的元数据。
数据集在特定时间段、船舶类型、移动AIS导航状态以及兴趣区域（ROI）内进行了限制，超出上限的轨迹被分割，短轨迹被丢弃。所有值均作为元数据包含在数据集中，并在命名语法中使用。

命名语法：

data_AIS_Custom_STARTDATE_ENDDATE_SHIPTYPES_MINLENGTH_MAXLENGTH_RESAMPLEPERIOD.pkl

请参阅数据表以获取更详细的信息，并参考提供的实用函数以了解如何读取和绘制数据。