## 5 Conclusions  
In the marine industry, it is important to estimate the fish catch and to prevent illegal, unreported, and unregulated (IUU) fishing and overfishing. The existing VMS system allows the Vessel Traffic Service officers to monitor the ship movements in real time and to guess their fishing activities only based on their expertise. The proposed fishing gear type identification method allows to identify \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & \multicolumn{4}{c}{**Predicted Label**} \\ \cline{3-8}  & & **Pune Seine** & **Stow Nets** & **Longline** & **Dift Gill Nets** & **Traps** & **Trawls** \\ \hline \multirow{4}{*}{**Actual Label**} & **Parse Seine** & 92.4\% (978,213) & 1.2\% (12,385) & 1.9\% (202.18) & 1.5\% (15,349) & 1.7\% (17,678) & 1.4\% (14,714) \\  & **Stow Nets** & 1.2\% (9329) & 84.5\% (662,501) & 5.4\% (194,940) & 6.7\% (52,288) & 1.3\% (9956) & 1.0\% (9781) \\  & **Longline** & 0.7\% (4582) & 5.9\% (36,345) & 87.4\% (541,019) & 4.9\% (307,727) & 0.8\% (4829) & 0.3\% (1610) \\  & **Dift Gill Nets** & 0.7\% (3262) & 6.7\% (508,84) & 5.0\% (52,287) & 85.7\% (703,775) & 1.5\% (704) & 0.4\% (655) \\  & **Traps** & 1.3\% (11,484) & 1.1\% (10,106) & 2.1\% (183,84) & 1.2\% (10,749) & 93.1\% (855,729) & 1.3\% (11851) \\  & **Trawls** & 1.7\% (19,262) & 1.4\% (15,687) & 1.5\% (6,725) & 1.5\% (17,071) & 1.5\% (17,417) & 92.5\% (1,067,286) \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Confusion matrix for the experiments of TPI. Figure 10: Classification results of the trained model for some trajectory data. The red tracks indicate the correct classification, the sky blue tracks indicate the trajectories of ships with a speed more than 6 knots, and the tracks in other colors indicate false classification for the corresponding sections. the fishing activities of ships from their fishing trajectories collected at the VMS system in real time. It makes it possible to compare the identified fishing gear type with the registered fishing gear type which is maintained at the VMS system. The comparison can detect the suspicious ships of IIU fishing. If the times of fishing activities of each ships are maintained at the VMS system, it is also possible to detect the overfishing ships with the help of some estimation method of fishing catch from the fishing activity times. AIS data of fishing ships provide information of their locations, speeds, and course changes on the fishing journey at a high sampling rate. It is important to identify the fishing gear types of fishing ships from AIS data because they reflect real fishing status for fish catch estimation, IUU fishing, and overfishing. This paper proposed a deep-learning based method to classify fishing trajectory data into six fishing gear groups. The proposed method has the following characteristics. First, an interpolation method is introduced for trajectory estimation to reduce the noises in AIS data and to recover the missing AIS data. Second, the proposed method makes use of fishing movement and environmental factors such as water temperature, daylight, tidal current, water depth, and seabed type as well as trajectory data. Third, it uses a sliding window technique to capture time series characteristics into the spatial domain and to capture various dynamic movements in trajectories. Fourth, the proposed deep-learning model consists of two feature extraction modules and a prediction module, where a 1D CNN model is used for extracting features from fishing ship movement data, a FCNN is used for extracting features from environmental data, and the FCNN-based prediction module is used to make prediction of fishing gear type from the two feature-extraction modules. The proposed model was evaluated with respect to the new proposed performance indices TPI and DPI. It showed good performance for purse seine, trap, and trawl fishing gear types, but relatively poor performance for stow net, longline, and drift gill net fishing gear types.