# Region-Based Convolutional Neural Network for Wind Turbine Wake Characterization in Complex Terrain
## 1 Introduction
They have been demonstrated to be effective in interpreting multiple types of inputs, including sound, images and raw numerical data [26; 27; 28], and provide an ideal tool for understanding and interpreting the complexities and patterns in wind turbine wake flows in complex terrain [29]. Neural networks have the potential to address multiple common issues in wind turbine wake characterization and identification in complex terrain: simultaneously identifying the exact wake structures and characteristics of multiple wind turbine wakes; distinguishing flow-field turbulent structures generated by wakes from those embedded in the background atmospheric flow; identifying turbulent wake dissipation; and even locating wakes in lidar data that wind up incomplete or limited in spatial resolution after filtering for a low signal-to-noise (SNR) or carrier-to-noise ratio (CNR). Although there are many forms of neural networks available, this work focuses on implementing a convolutional neural network (CNN). CNNs are commonly used for image processing and object detection or instance segmentation [30; 31]. A CNN can analyze input images and identify objects based on their type, quantity, location and size. CNNs have been trained on datasets of varying complexity and have accurately performed complex tasks, such as detecting ships from synthetic aperture radar scans and identifying fish species from images [32; 33]. Although CNN models continue to grow in complexity and capability, recent developments in these models have improved their computational time [34]. Traditional CNN models struggled with or were unable to detect multiple instances of objects within an image until the development of a region-based CNN (R-CNN) [35]. From R-CNN, Mask R-CNN was developed to output image segmentation masks for multiple instances of objects within an image [36]. In this context, a mask is a binary pixel matrix representing the exact location and size of detected objects. Mask R-CNN has been shown to identify and characterize objects in remote-sensing data robustly and successfully, such as identifying buildings from satellite imagery and mapping topographic features captured with lidar data [37; 38]. As a result of its computational efficiency and accuracy in object segmentation and classification, the Mask R-CNN is a robust option for identifying and characterizing wind turbine wakes in complex terrain. The objective of this research is to implement and evaluate a Mask R-CNN for wake identification and characterization using lidar data collected in complex terrain. The primary objectives of this proof of concept are to quantify the skill with which the CNN can: (i) identify multiple wind turbine wakes simultaneously from a variety of scan orientations (flow field quadrant, elevation angle, range coverage and resolution, time of day), (ii) characterize wake shapes as the wakes form and dissipate in the flow field and (iii) identify wakes even if they are obscured by missing data points. All objectives are evaluated over a range of wind turbine layout geometries (single and multiple rows of turbines and/or individual turbines).