## 3 Fishing Gear Type Identification from Automatic Identification System Trajectory Data
### The Proposed Deep Neural Network Model for Fishing Gear Type Identification
#### 3.4.1 Input Data Preparation Module  
AIS data are received in discrete but not regular intervals, and we want to use the processed trajectory data sampled at a regular rate. To handle this problem, we sample the AIS trajectory data. Such sampled trajectory data are pseudo-sample (interpolated) dynamic data which consists of ship position (\(pp\ (t_{i})\)), speed (\(spd\ (t_{i})\)), and course (\(co\ (t_{i})\)) where \(t_{i}\) is the \(i\)-th reference time at which sampling is made according to Section 3.3. A trajectory window \(TW(i)\) consists of a specific number s of consecutive pseudo-sample dynamic data. The reference time points corresponding to trajectory windows are referred to as boundary reference time (\(rt\)) points. Let \(rt(i)\) denote the boundary reference time point which splits the trajectory segments \(TW(i-1)\) and \(TW(i)\). Suppose that \(TW(i)\) contains consecutive \(s\) pseudo-samples. If the boundary reference time point \(rt(i)\) of the trajectory segment \(TW(i)\) is \(t_{i}\), the trajectory segment \(TW(i)\) can be expressed in the sequence of s pseudo-sample dynamic data as follows:  
\[TW(i)\ =\ ([pp(t_{i}),co(t_{i}),spd(t_{i})],\ [pp(t_{i+1}),co(t_{i+1}), spd(t_{i+1})],\ \ldots,\ [pp(t_{i+s}),co(t_{i+s}),spd(t_{i+s})]) \tag{5}\]  
The trajectory windows are constructed by a sliding window scheme in which the following trajectory window starts at the next second segment of the preceding trajectory window. Suppose that the \(i\)-th trajectory window \(TW(i)\) of size \(s\) consists of \(TW(i)\) = \(([pp(t_{i}),co(t_{i}),spd(t_{i})],\ [pp(t_{i+1}),co(t_{i+1}),spd(t_{i+1})],\ \ldots,\ [pp(t_{i+s}),co(t_{i+s}), spd(t_{i+s})])\). Then the next trajectory window \(TW(i+1)\) is set to \(W(i+1)\ =\ ([pp(t_{i+1}),co(t_{i+1}),spd(t_{i+1})],\)  
Figure 4: The proposed deep neural network model for fishing gear type classification. \([pp(t_{i+2}),co(t_{i+2}),spd(t_{i+2})],\ \ldots,[pp(t_{i+s+1}),co(t_{i+s+1}),spd(t_{i+s +1})])\). Here, the number of pseudo-samples for a TW window is determined to sufficiently cover the trajectory patterns encompassing their setting, waiting, and hauling phases. In the experiments, a TW window is set to contain 1440 psuedo-samples over about 24 h. Figure 5 represents trajectory window data over the sliding trajectory window. A ship in fishing activity exhibits a specific trajectory pattern for the fishing gear type while making frequent course direction changes at a low speed. The course change CC (\(t_{i}\)) of a ship at a time point \(t_{i}\) is defined as the absolute course difference \(Cd(t_{i})\) between \(Co(t_{i+1})\) and \(Co(t_{i})\) as in Equation (6). \[Cd(t_{i})\ =\ \big{|}Co(t_{i+1})-Co(t_{i})\big{|}\] \[CC(t_{i})\ =\ \begin{cases}360-Cd(t_{i})&\text{if }Cd(t_{i})\ \geq 180\\ Cd(t_{i})&\text{otherwise}\end{cases}  \tag{6}\]  
The training data set \(FAD(i)\) for the proposed deep-learning model consists of both the sequence of course changes and the sequence of speeds at each sampling time point in the sampling window \(TW(i)\). \[FAD(i)\ =\ ([CC(t_{i}),CC(t_{i+1}),\ldots,\ CC(t_{i+s-1})],\ \ [Spd(t_{i}),\ Spd(t_{i+1}),\ldots,\ Spd(t_{i+s-1})]) \tag{7}\]  
There are some environmental factors that affect fishing activities such as daylights, tidal current, water temperature, water depth, and seabed type. The proposed method uses an environmental dataset that contains information of such environmental factors. Some fish are phototactic and hence daylight information is expressed as a binary value where 0 indicates daytime and 1 indicates nighttime. Tidal current speed affects the distributions of planktons of which fish move along, and hence the tidal current speed difference information is used, which is set to the difference between maximum tidal current speed and minimum tidal current speed in the trajectory window. Each fish species has its own favorite water temperature. The environmental data set contains a field for the water temperature, which is a binary value 1 for a temperature of higher than 25\({}^{\circ}\) and 0 for other temperatures.