# Region-Based Convolutional Neural Network for Wind Turbine Wake Characterization in Complex Terrain
## 2 Materials and Methods
### The Proof-of-Concept Dataset: Methodology  
Out of the 3307 available arc scans, we selected a subset of 354 to design a proof-of-concept dataset. The sub-selection was based on the following objective criteria:  
1. Wakes cannot mix with each other, i.e., all wakes must be distinct;
2. Wakes mut be distinct from local flow features (i.e., edges must be detectable amid local turbulent structures).  
Scans were subject to both criteria through visual inspection. The majority of rejected scans exhibited discernible wakes, but displayed wake mixing and thus violated criterion (1). Cases in which CNR filtering obscured the wake shape were included in the dataset to evaluate the model's efficacy in identifying wakes in regions of low-quality measurements. The 354-scan dataset was split into three subsets: two-thirds of the scans were selected for the training subset, whereas the remaining one-third was split evenly between the validation subset and testing subset. Although 354 scans represent approximately 10% of the original arc scan subset, there are over 1000 instances of wakes within these scans. Further, many arc scans were disqualified from inclusion in the proof-of-concept dataset, since they were taken at high elevation angles to survey the atmospheric conditions during scan collection time, and thus did not exhibit wakes. Wakes are visible in all scans in the proof-of-concept dataset; due to the methodology of Mask R-CNN (proposes regions of interest within the images; if there are no regions of interest, Mask R-CNN will identify no objects), there is no need to train the model on scan images without wakes. The model was trained using the training subset, and the validation subset was utilized to ensure the model is not overfit or underfit during training. The model was then evaluated using the independent testing subset. Subset scans encompassed a wide variety of scan geometries and parameters (Figure 4).  
Figure 3: Example of training scan collected under easterly flow. Missing velocity measurements as assessed by carrier-to-noise ratio (CNR) values are shown in black. Data captured on 30 July 2018, 04:38 UTC, elevation angle \(\varnothing=1.5^{\circ}\), azimuth angle \(\Theta\in[\mathbf{180}^{\circ},\mathbf{270}^{\circ}]\), azimuthal resolution \(1^{\circ}\), range gate 50 m, 120 s scan time. Scan image dimensions are 217 (width) by 334 (length) pixels.  
The model evaluation metrics in Section 3 rely on the following objective definition of wakes and wake fragments (dissipated wakes) (Figure 5). _Wakes_ are defined as the first instance of low velocity flow emanating from a known turbine location. _Wake fragments_ are defined as any background low-speed turbulent structures parallel to known wakes in the scan or collinear with wakes emanating from known turbine locations.  
The wide variety of azimuth angles encompassed by scans in the subsets result in a high coverage of the flow field; wakes were captured from each turbine depicted in Figure 1. These scans collectively include nearly 1500 instances of wakes and wake fragments and nearly 1000 hard-target returns attributed to turbines obstructing the flow field i.e., two classes (types of objects). Both class annotations are depicted in Figure 5. All data subsets (testing, validation, training) frequently included multiple wake or wake fragment instances per scan. Depending on the arc scan region (i.e., A, B, C or D), a single scan can cover between 2 (Region D) and 10 wakes (Region B).