Integrating Machine Learning and Computer Vision for Personalized Maritime Weather Hazard Warnings in Data-Sparse and High-Uncertainty Environments

The World Meteorological Organization’s Early Warnings for All (EW4All) initiative aims to ensure that every person, everywhere, is protected by life-saving early warning systems by 2027. Yet, the maritime navigation—particularly in offshore and remote oceanic regions— remains underserved in this global effort. The challenges arise mainly due to sparse meteorological data coverage, unreliable network connection, and a lack of tailored, context-aware alerting services designed specifically for sailors and seafarers.

In response, we present a novel AI- and ML-based maritime early warning system that provides real-time, personalized alerts for hazardous weather conditions, including dangerous sea states, strong winds, frontal systems, and convective thunderstorms.

The system integrates conventional numerical weather prediction (NWP) outputs and satellite-based remote sensing with machine learning algorithms and computer vision techniques to detect, track, and nowcast evolving hazardous features in the marine environment. Specifically, we apply front-detection algorithms to identify synoptic-scale boundaries using visual patterns in NWP outputs, while convective activity is monitored and nowcasted based on satellite imagery.

A key feature of the system is its user-centric design: alerts are dynamically adapted to individual vessel types, planned routes, and predefined risk thresholds, allowing for operationally relevant and personalized decision support. This personalization is crucial in fostering trust among end users, particularly in offshore sailing and commercial maritime operations.

Early results are promising, demonstrating the potential of AI to support marine situational awareness even in data-sparse regions. Looking forward, we aim to integrate deep learning models and ensemble forecasting techniques to enhance alert precision and better represent uncertainty in meteorological predictions.

By extending the reach of early warning systems to the open ocean, our solution contributes to the EW4All initiative’s goal of truly global coverage—bringing personalised, intelligent tools to improve safety at sea. It also exemplifies the role of private-sector innovation in complementing public meteorological infrastructure, reinforcing the value of collaborative frameworks.