A Data-Centric Approach for Wind Plant Instance-Level Segmentation Using Semantic Segmentation and GIS

Abstract

Wind energy is one of Brazil’s most promising energy sources, and the rapid growth of wind plants has increased the need for accurate and efficient inspection methods. The current onsite visits, which are laborious and costly, have become unsustainable due to the sheer scale of wind plants across the country. This study proposes a novel data-centric approach integrating semantic segmentation and GIS to obtain instance-level predictions of wind plants by using free orbital satellite images. Additionally, we introduce a new annotation pattern, which includes wind turbines and their shadows, leading to a larger object size. The elaboration of data collection used the panchromatic band of the China–Brazil Earth Resources Satellite (CBERS) 4A, with a 2-m spatial resolution, comprising 21 CBERS 4A scenes and more than 5000 wind plants annotated manually. This database has 5021 patches, each with 128 × 128 spatial dimensions. The deep learning model comparison involved evaluating six architectures and three backbones, totaling 15 models. The sliding windows approach allowed us to classify large areas, considering different pass values to obtain a balance between performance and computational time. The main results from this study include: (1) the LinkNet architecture with the Efficient-Net-B7 backbone was the best model, achieving an intersection over union score of 71%; (2) the use of smaller stride values improves the recognition process of large areas but increases computational power, and (3) the conversion of raster to polygon in GIS platforms leads to highly accurate instance-level predictions. This entire pipeline can be easily applied for mapping wind plants in Brazil and be expanded to other regions worldwide. With this approach, we aim to provide a cost-effective and efficient solution for inspecting and monitoring wind plants, contributing to the sustainability of the wind energy sector in Brazil and beyond.

Keywords

• deep learning
• instance segmentation
• semantic segmentation
• renewable energy
• small object
• GIS