Automating offshore infrastructure extractions using synthetic aperture radar & Google Earth Engine

Although Land Use and Land Cover (LULC) change is primarily focused on the types, rates, causes, and consequences of land change, increased anthropogenic development on the ocean's surface, such as offshore oil extraction, offshore wind energy, aquaculture, and coral reef conversion to military outposts, suggest that LULC change not only pertains to historically terrestrial space, but also new lands created on top of ocean surfaces. Therefore, similar human disturbance analyses are necessary for these transformed marine environments, but the lack of accurate, accessible, and up-to-date location information about these spatially dispersed changes significantly limits examination of their environmental impacts. Subsequently these dynamic changes across the oceans are poorly documented. Here, we developed a cloud-native geoprocessing algorithm to automatically detect and extract offshore oil platforms in the Gulf of Mexico using synthetic aperture radar and Google Earth Engine. Cross-validated results indicate our top model identified offshore infrastructure with a probability of detection of 98.70%, an overall accuracy of 96.09%, a commission error rate of 2.68%, and an omission error rate of 1.30%. Its generalizability was tested across wind farms in waters of China and the United Kingdom, which resulted in an overall accuracy of 97.00%, a commission error rate of 2.07%, and omission error rate of 0.97%. These generalization capabilities indicate our model can be potentially used to map global offshore infrastructure. Such increased ocean transparency could allow for improved marine environmental management by bringing objectivity, scalability, and accessibility.

Duke Scholars

Author Patrick N. Halpin Marine Science and Conservation