Shipwreck Detection

Shipwrecks pose significant threats to the safety of shipping, which may cause dangerous situations for vessels that navigate within the waters near the detected objects. The location, detection, and labeling of the shipwrecks are necessary for safe navigation, nautical charting updates, maritime surveys, and underwater situational awareness. Yet, the traditional shipwreck detection approaches are usually based on the manual analysis of geospatial data collected with sonar, satellite, aircraft, or other means, making such processes complicated and time-consuming, especially in the case of a large amount of imagery.

This deep learning model presents an automated and efficient tool to detect and identify shipwrecks based on the appropriate geospatial and remote sensing data. Using artificial intelligence and deep learning methods, this model is able to analyze imagery and find possible features of shipwrecks, allowing for a decrease in manual work in maritime mapping and surveying operations.

This model is developed for applications that require the automated detection of shipwreck features. Such a model can be used by organizations and professionals engaged in marine surveying, hydrographic mapping, maritime safety, ocean monitoring, nautical charting, coastal management, and geospatial analysis.

The first and foremost advantage is flexibility. The model can be used out of the box for the appropriate datasets to serve as an initial solution for the shipwreck detection process. Moreover, the model can be fine-tuned on a particular dataset, geographic region, sensor type, or image characteristics to become more suited for the particular application. In this way, the users will be able to optimize the use of the model to the regional peculiarities and specifics of the dataset under consideration.

As a result of applying automation during the preliminary analysis, the model can assist in accelerating the geospatial analysis process and making it more efficient and effective, as well as in creating a more complete maritime dataset. Further on, the detected objects can be manually checked by experts and added to other work processes.

In general, this example of the use of deep learning and artificial intelligence in geospatial intelligence of the maritime domain shows that it is possible to accelerate analysis of huge volumes of data and to become less dependent on manual labor. This model serves as a basis for the integration of such processes as shipwreck detection, deep learning, computer vision, and geospatial AI into maritime navigation and mapping.