Data presentation and correlation tool. OWL™ MAP presents both lidar measurement data and camera data in an easy-to-use graphical user interface.
Navigate the timeline of the chosen location. This enables you to put actions in context with each other, and from get a better understanding of the situation.
OWL™ MAP is a multi-layer, multi-sensor presentation software. In addition to presenting several sensors, we also present other real-time information systems such as ships (AIS), meteorological data and points of interest (POIs), such as aquaculture sites or oil rig / drilling sites in the map layer.
Algorithms, convolutional neural networks and deep learning models can assist in the analysis of satellite images for early detection. Prediction models can provide useful event forecasts and help in the cleanup and handling process.
Monitor large areas quickly, identify subtle patterns and provide real-time updates for immediate action. By utilizing these models we can predict oil spill movement based on factors like ocean currents and wind, allowing for proactive measures. Machine learning can also suggest the best cleanup methods and improvements by analyzing past operations and environmental conditions.
When developing predictive models and AI systems using machine learning, access to relevant, high-quality sensor data is essential for accurate analysis and meaningful correlations.
Sensor data are visualized in real-time via OWL™ MAP. Combine data with other layers of information, e.g. automatic identification of ships (AIS) and weather data.
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“The operational use of this technology has the potential to improve the verification of suspicious oil features, the detection of oil in the water column, reducing the need for conventional laboratory analysis.”
Cristina Maria Bentz
Ocean Visuals AS
Oslo Science Park
Gaustadalléen 21, 0349 Oslo - Norway
+47 21 50 71 00
post@oceanvisuals.no