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Oceans constitute over 70% of the earth's surface, and the marine environment and ecosystems are central to many global challenges. Not only are the oceans an important source of food and other resources, but they also play a important roles in the earth's climate and provide crucial ecosystem services. To monitor the environment and ensure sustainable exploitation of marine resources, extensive data collection and analysis efforts form the backbone of management programmes on global, regional, or national levels. Technological advances in sensor technology, autonomous platforms, and information and communications technology now allow marine scientists to collect data in larger volumes than ever before. But our capacity for data analysis has not progressed comparably, and the growing discrepancy is becoming a major bottleneck for effective use of the available data, as well as an obstacle to scaling up data collection further. Recent years have seen rapid advances in the fields of artificial intelligence and machine learning, and in particular, so-called deep learning systems are now able to solve complex tasks that previously required human expertise. This technology is directly applicable to many important data analysis problems and it will provide tools that are needed to solve many complex challenges in marine science and resource management. Here we give a brief review of recent developments in deep learning, and highlight the many opportunities and challenges for effective adoption of this technology across the marine sciences.

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Land cover classification of remote sensing images is a challenging task due to limited amounts of annotated data, highly imbalanced classes, frequent incorrect pixel-level annotations, and an inherent complexity in the semantic segmentation task. In this work, we propose a novel architecture called the Dense Dilated Convolutions Merging Network (DDCM-Net) to address this task. The proposed DDCM-Net consists of dense dilated image convolutions merged with varying dilation rates. This effectively utilizes rich combinations of dilated convolutions that enlarge the network's receptive fields with less parameters and features compared to the state-of-the-art approaches in the remote sensing domain. Importantly, DDCM-Net obtains fused local and global context information, in effect incorporating surrounding discriminative capability for multi-scale and complex shaped objects with similar color and textures in very high resolution aerial imagery. We demonstrate the effectiveness, robustness and flexibility of the proposed DDCM-Net on the publicly available ISPRS Potsdam and Vaihingen data, as well as the DeepGlobe land cover dataset. Our single model, trained on 3-band Potsdam and Vaihingen data, achieves better accuracy in terms of both mean intersection over union (mIoU) and F1-score compared to other published models trained with more than 3band data. We further validate our model on the DeepGlobe dataset, achieving state-of-the-art result 56.2% mIoU with much less parameters and at a lower computational cost compared to related recent work.