Ship positioning and maneuvering information is highly relevant to understand the levels of pollution on coastal cities and sea-life quality, containing latent patterns of vessels behavior, that are of utility on earth sciences and environmental research. Using Automatic Identification System (AIS) data enables air quality models to have finer grain estimations. However, the data as it is, carries uncertainty and errors. Therefore, there is a need for a methodology to filter and clean it and to extract patterns. Ship navigation traces can be understood as time series. Here, we present a methodology for characterizing ships by their navigation traces, using Conditional Restricted Boltzmann Machines (CRBMs) plus classic clustering techniques like k-Means. From the inputs received from ships using the AIS, containing ship positions, speed, and characteristics, we produce a processed cruising trace that a CRBM can encode while preserving the time factor and reducing dimensionality of data. Such codification can be then clustered or pattern-mined, then used not only for ship classification but also to cross such behavior patterns with environmental information. In this paper we detail such methodology and validate it using data from the Spanish Ports Authority records from national and international fishing vessels and passenger and cargo ships. Along the pattern mining methodology we propose how to use Apache Spark for the data cleaning process until it arrives to the Conditional Restricted Boltzmann Machine (CRBM). Finally, we develop a visualization tool for data exploration and pattern evaluation.