Marine life significantly impacts our planet by providing essential resources such as food,oxygen, and biodiversity. The surveillance of illegal fishing activities is critical for thesustainable management of marine resources. In this study, we investigate the use oftwo publicly available data sources to automatically detect fishing activities. Onedataset is the Automatic Identification System (AIS) data, the other is the CatchReports from the Norwegian fishing authorities. The data from fisheries along theNorwegian coast, specifically from vessels with a length of 15 meters or more, covers aperiod of 1 year and 11 months (January 2022 to November 2023). The AIS data wascleaned by removing duplicates and outliers, then interpolated using piecewise linearinterpolation and resampling at a five-minute rate to turn the AIS data into time seriesof one-hour length. The time series were then tagged using the Catch Report data. Thedataset is severely unbalanced with fishing and non-fishing tags. For better learning,the total data was resampled to create 100 balanced bootstrap sets, ensuring equalrepresentation of fishing and non-fishing activities. This process resulted in abenchmark dataset containing about 30 million data points in about 2.5 million timeseries. We applied a range of classification methods, based on random forests and deepconvolution networks. Three types of features were used, related to secant speed,distance to shore, and curvature. We achieved an accuracy ranging from about 92% to93.7% depending on the features and classification methods used. The predictivecapabilities of the classifiers are investigated and significance of the features are studied.The uncertainty of the classification was assessed using the bootstrap sets, providingrobust evaluation metrics. Overall, this study demonstrates the effectiveness ofleveraging AIS and Catch Report data and advanced data processing techniques forautomatic and accurate monitoring of marine fishing activities.