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A warming planet means rising oceans, but seaports are not prepared for the expensive construction they will need to protect themselves, according a global survey of ports conducted by Stanford researchers. But the researchers have created a computer model that will help ports with their planning.

Coast Guard alarmed by rising cyber intrusions on ships.

U.S. Cyberattack Hurt Iran's Ability to Target Oil Tankers, Officials Say.

Single cyber attack on Asia-Pacific ports could cost $110bn.

Marine traffic, coastal planning to benefit from improved forecasts in wave variation.

Accurate and automated detection of maritime vessels present in aerial images is a considerable challenge. While significant progress has been made in recent years by adopting neural network architectures in detection and classification systems, these systems are usually designed specific to a sensor, dataset or location. In this paper, we present a system which uses multiple sensors and a convolutional neural network (CNN) architecture to test cross-sensor object detection resiliency. The system is composed of five main subsystems: Image Capture, Image Processing, Model Creation, Object-of-Interest Detection and System Evaluation. We show that the system has a high degree of cross-sensor vessel detection accuracy, paving the way for the design of similar systems which could prove robust across applications, sensors, ship types and ship sizes.