<p>The current project is focused on resolving the issue of identification of maritime vessels based on the aerial images with emphasis on their strategic importance to security and management of resources and trade in the world. The present project’s major aim was to produce a high-performance machine literacy model that could precisely identify vessels from large-scale unbalanced datasets. The originality of this contribution is that progress has been made towards integrating state-of-the-art image-processing methods into a deep-learning framework that has been optimised with regard to the specific features of maritime imagery, such a variability in scale, distortion, perspective skew, and heterogeneous environment. The effectiveness and actual performance of this model are demonstrated by the fact that the training process was finished in 1989. The developed model was capable of being trained on a dataset of approximately 156,953 images and obtained a training accuracy of 98.65% and a validation accuracy of 99.16%, with relating losses of -0.1388 and − 0.1366, respectively. Seven seconds, or around 33.1&#xa0;min, indicates that it has much promise for real-time marine resource management and monitoring, as well as being a practical instrument to improve the maritime industry’s operational efficacy and global security posture.</p>