Offshore Wind Plant Instance Segmentation Using Sentinel-1 Time Series, GIS, and Semantic Segmentation Models

Offshore wind farms represent a renewable energy source with a significant global growth trend, and their monitoring is strategic for territorial and environmental planning. This study's primary objective is to detect offshore wind plants at an instance level using semantic segmentation models...

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Bibliographic Details
Published inarXiv.org
Main Authors Osmar Luiz Ferreira de Carvalho, Osmar Abilio de Carvalho Junior, Anesmar Olino de Albuquerque, Guerreiro e Silva, Daniel
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 14.12.2023
Subjects
Instance segmentation
Offshore
Offshore energy sources
Renewable energy sources
Semantic segmentation
Semantics
Strategy
Target detection
Time series
Wind power
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Summary:Offshore wind farms represent a renewable energy source with a significant global growth trend, and their monitoring is strategic for territorial and environmental planning. This study's primary objective is to detect offshore wind plants at an instance level using semantic segmentation models and Sentinel-1 time series. The secondary objectives are: (a) to develop a database consisting of labeled data and S-1 time series; (b) to compare the performance of five deep semantic segmentation architectures (U-Net, U-Net++, Feature Pyramid Network - FPN, DeepLabv3+, and LinkNet); (c) develop a novel augmentation strategy that shuffles the positions of the images within the time series; (d) investigate different dimensions of time series intervals (1, 5, 10, and 15 images); and (e) evaluate the semantic-to-instance conversion procedure. LinkNet was the top-performing model, followed by U-Net++ and U-Net, while FPN and DeepLabv3+ presented the worst results. The evaluation of semantic segmentation models reveals enhanced Intersection over Union (IoU) (25%) and F-score metrics (18%) with the augmentation of time series images. The study showcases the augmentation strategy's capability to mitigate biases and precisely detect invariant targets. Furthermore, the conversion from semantic to instance segmentation demonstrates its efficacy in accurately isolating individual instances within classified regions - simplifying training data and reducing annotation effort and complexity.
ISSN:2331-8422