## 2 Related Work  
Most fishing activity identification methods have been developed for VMS data. VMS data allows for proper monitoring of the movements of fishing ships with a temporal resolution of hourly order. VMS data are useful for the estimation of fishing effort, which is fundamental data for inventory management in fisheries [13]. Fishing ships broadcast messages containing their unique identifier, like a maritime mobile service identities (MMSI) number, with which their associated information like fishing gear type can be retrieved from the vessel register (VR) database. To identify the fishing activities, such as fishing or non-fishing and setting or hauling, the VMS data analysis methods interpolate the trajectories of fishing ships over a long period of time (e.g., 30 days). The methods discriminate fishing activities from the interpolated trajectories data by exploiting the information on fishing gear type retrieved from the VR database. There have been some works that have focused on determining whether a fishing ship of a specific type is engaging in a fishing activity or on a non-fishing activity from the speed profile information extracted from the interpolated trajectory data. [PERSON] proposed a method to identify the three fishing activities such as setting, hauling, and others (streaming, soak-time) for longliners [14]. He conducted the statistical analysis fishing ship speed for each fishing activity, and found out the speed ranges of each activity as follows: setting (4-6 knots), hauling (2-4 knots), and others (less than 2 knots or greater than 6 knots). His method is only applicable to a specific fishing gear type (i.e., longliners) and his results cannot be directly applied to other fishing gear types. In addition, there are no guarantees that other fishing gear types have such simple speed profiles for activity discrimination. [PERSON] et al. [15] used VMS data to extract information on the fishing ship fleet at three levels. The first-level analysis is conducted to determine the number of fishing ships in a fleet. The second-level analysis is made to estimate the fishing effort of fishing ships, and the third-level analysis is conducted to estimate such fishing parameters as the proportion of time spent fishing, fish speed, or gear characteristics like specific gear size. [PERSON] et al. [16] proposed a Bayesian hierarchical model-based method using a hidden Markov process to identify three fishing activities (i.e., steaming, fishing, or stopping) for pelagic trawlers. Their hidden Markov model (HMM) defines the observed positions conditioned on the latent variables of fishing ship activities and movement parameters which are modeled by a Bayesian model. Their method uses the amount of time spent in the fishing state to estimate the fishing effort of a pelagic trawler from the VMS data. [PERSON] et al. [17] proposed a hidden semi-Markov model (HSMM) to discriminate three fishing activities (i.e., searching, fishing, or cruising) from VMS data of purse-seiners used for anchovy fishing. They claimed that their HSMM model outperformed the HMM model and as well as other classification methods like support vector machine, random forest, and artificial neural network with one hidden layer. [PERSON] et al. [18] proposed a probabilistic state-space model to discriminate three fishing activities (i.e., fishing, stopping, or cruising) from the VMS data of tuna purse-seiners. [PERSON] et al. [19] proposed a neural network model which classifies the fishing work into three fishing gear types of passive gears, towed gears, and mobile gears. The method uses coarsely-grained VMS data which contains information about ship speed, course change, water depth statistics of water depths, trip duration, and maximum distance from the coast over an entire fishing trip duration of about two weeks. Their method achieved a 94.4% classification accuracy. However, their method takes into account only three fishing gear types and requires fishing trip data over about two weeks.