DeepOWT: a global offshore wind turbine data set derived with deep learning from Sentinel-1 data

Abstract

Abstract. Offshore wind energy is at the advent of a massive global expansion. To investigate the development of the offshore wind energy sector, optimal offshore wind farm locations, or the impact of offshore wind farm projects, a freely accessible spatiotemporal data set of offshore wind energy infrastructure is necessary. With free and direct access to such data, it is more likely that all stakeholders who operate in marine and coastal environments will become involved in the upcoming massive expansion of offshore wind farms. To that end, we introduce the DeepOWT (Deep-learning-derived Offshore Wind Turbines) data set (available at https://doi.org/10.5281/zenodo.5933967, Hoeser and Kuenzer, 2022b), which provides 9941 offshore wind energy infrastructure locations along with their deployment stages on a global scale. DeepOWT is based on freely accessible Earth observation data from the Sentinel-1 radar mission. The offshore wind energy infrastructure locations were derived by applying deep-learning-based object detection with two cascading convolutional neural networks (CNNs) to search the entire Sentinel-1 archive on a global scale. The two successive CNNs have previously been optimised solely on synthetic training examples to detect the offshore wind energy infrastructures in real-world imagery. With subsequent temporal analysis of the radar signal at the detected locations, the DeepOWT data set reports the deployment stages of each infrastructure with a quarterly frequency from July 2016 until June 2021. The spatiotemporal information is compiled in a ready-to-use geographic information system (GIS) format to make the usability of the data set as accessible as possible.

Marine Infrastructure Detection with Satellite Data—A Review

“…Bridges are generally also detected on the mainland and not specifically only "sea-crossing" bridges. OWFs were investigated six times from 2020 on by four different author groups [41,42,51,[107][108][109]. and are assumed to continue to be investigated.…”

“…These last two study region sizes of continental and global include 3% and 7% of the studies, respectively. These are less method papers but are almost exclusively multi-temporal and time-series studies (e.g., [108,109]), which examined the distribution and a trend or development of the examined infrastructure and presented their work more as a product than, for example, the testing of a new method.…”

“…It is important to note and consider that the six global studies are not shown here. One was dealing with the detection of platforms [36] while the remaining five detected OWFs [41,42,[107][108][109]. In addition to the global studies, OWFs were also investigated in the Yellow Sea near China and the North Sea [51].…”

Machine Learning Solutions for Offshore Wind Farms: A Review of Applications and Impacts

“…Hoeser et al introduced the DeepOWT dataset (Deep-learning-derived Offshore Wind Turbines), which encompasses 9941 global offshore wind energy infrastructure locations, along with their corresponding deployment stages [18]. DeepOWT harnesses publicly accessible Earth observation data derived from the Sentinel-1 radar mission.…”

“…Figure2. The procedural process employed to create the DeepOWT dataset using the Sentinel-1 archive by[18].…”