Labelled evaluation 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 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.

The dataset is an example of a SAR event and cannot not be considered representative of a large population of all SAR events. 

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.

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.

本项属于“丹麦水域内异常行为检测的AIS轨迹”系列收藏。DOI: https://doi.org/10.11583/DTU.c.6287841

运用深度学习技术对时空轨迹中的海事异常行为进行检测，是一项新兴且具有广阔前景的应用领域。自动识别系统（AIS）的开放访问使得大量海事轨迹数据得以公开。然而，这些轨迹在异常行为检测方面并未进行标注。缺乏针对海事轨迹异常检测的标注数据集，使得对所提模型的评估与比较难以进行量化。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。

我们已手动标注了发生碰撞事故后展现异常行为的轨迹。该标注数据集包含521个数据点，其中25个轨迹被标注为异常。异常轨迹涵盖了碰撞船舶、参与搜救活动、执法以及因商业海事交通被迫偏离正常航线的船舶等多种情况。

这些数据集旨在评估无监督模型对异常海事行为的检测能力。有关用于训练的无标签数据集，请参阅相关收藏。相关出版物链接。

该数据集是SAR事件的一个示例，并不能代表所有SAR事件的大规模人群。数据集总共包含521个轨迹，其中25个被标注为异常。数据采集于特定区域的单日。剩余的正常交通流量代表了冬季季节的交通状况。该区域内的正常交通流量与渔业和休闲航海交通有较高的季节性相关。

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”的文件包含提取的轨迹序列，依次序列化并逐个读取，请参考提供的实用函数进行示例。

名为“datasetInfo”的文件包含与数据集相关的元数据和“data_XXX”文件中轨迹开始处的索引。

数据是按以下属性定义的海事轨迹序列：时间戳、纬度/经度位置、速度、航向和唯一的船舶标识MMSI。此外，数据集还包含与创建参数相关的元数据。

数据集已被限制在特定时间段、船舶类型、移动AIS导航状态以及特定兴趣区域（ROI）内。如果轨迹超过上限则进行分割，短轨迹则被丢弃。所有值都以元数据形式提供，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。