## 4 Experiments  
### Data Preparation  
For the experiments of the proposed method, we used an AIS trajectory data set collected over the one year (year 2016) in a Southern part of the Korean peninsula water area, which covers the Jeju island. The AIS data of size 2.5 giga bytes were collected daily over the water area, and the total size of the data set is approximately 900 giga bytes. The AIS data set contains all types of ships in the water area, of which approximately one-third are fishing ships and the remaining are merchant and other ships. The number of fishing ship records in the AIS data set is 5,865,696,000, including both dynamic and static data for 1380 fishing ships. All the AIS data for each fishing ship are sorted out and trajectories are interpolated according to the method explained in Section 3.3. For training and testing of the classification model, the trajectory data for each ship was created by sampling its interpolated trajectory every 1 min over the trajectory window length of 24 h, where the trajectory window slides over the trajectory by 3 min. The number of trajectory window data was 52,560,000. Eighty percent (292 days) of the data set was used for training, and twenty percent (73 days) of the data set was used for testing. The correlation between the neighboring trajectory windows is very high and hence, in the experiments, the data set was divided into a training data set and a testing data set in a day-wise manner. For example, the trajectory data set of the first 24 days of a month is used for training, and the remaining data set for the following 6 days of the month is used for testing. Among the data sets, the trajectory data records for non-fishing activities like high-speed navigation, and in-harbor area berthing or movements were ignored. Out of the total trajectories, 24,966,000 trajectory windows were used for training and testing.  
The AIS data contains information about ship type, in which the fishing ships are labeled with the code '30' that indicates 'fishing ship', but the AIS data does not contain information about fishing gear type for the fishing ships. The fishing gear-type information is mandatory for training a fishing gear-type classification model. To get this information, we searched for the fishing gear type of each fishing ship in the Korean Fishing Ship Register Database, which is managed by the National Federation of Fisheries Cooperatives of Korea and to which all fishing ships must register their information, including fishing gear type. In the experiments, we grouped the fishing gear types into 6 classes. Figure 7 shows the experimental procedure for the proposed methods. First, it extracts the AIS data for ships whose ship type is '30' and maintains them for each fishing ship separately. Next, it looks up the Korean Fishing Ship Register Database for the fishing gear type of each ship and augments the fishing gear-type information to the AIS data. After that, it interpolates the trajectories of ships and creates the trajectory data over the sliding window of 24 h. Finally, it partitions the trajectory data set into a training data set and testing data set while ignoring the non-fishing-activity trajectory data.  
In the experiments we have used the environmental data set of tidal current, water temperature, water depth, and seabed type, which was collected by the Korea Hydrographic and Oceanographic  
Figure 7: Experimental procedure for the proposed methods.  
Agency (KHOA) that conducts ocean observation and hydrographic survey in the Korean waters [29]. KHOA measured those environmental factors in the grid of 5 km\({}^{2}\) rectangular regions and stored them in NetCDF (network common data form) format.