# Region-Based Convolutional Neural Network for Wind Turbine Wake Characterization in Complex Terrain
## 3 Results  
### CNN Model Performance  
The CNN exhibits a 95.65% success rate in identifying and outputting sufficient masks for the wind turbine wake shape when compared to the ground truth annotation in the independent test sample of 59 scans (Table 3). For wake fragments, the model exhibits a 77.53% success rate. In cases where the wind turbine wakes are embedded in a highly heterogeneous data field as a result of the removal of many data points because of CNR filtering, the success rate decreases to 87.18%. However, all four wake fragments that are embedded in a highly heterogeneous data field as a result of the removal of many radial velocity estimates because of CNR filtering are correctly identified. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Success Rate** & **Wakes** & \begin{tabular}{c} **Wakes** \\ **Fragments** \\ \end{tabular} & \
\begin{tabular}{c} **Wakes in Complex Data Fields** \\ **Because of Carrier-to-Noise Ratio** \\ **(CNR) Filtering** \\ \end{tabular} &
\begin{tabular}{c} **Wake Fragments in Complex Data** \\ **Fields Because of CNR Filtering** \\ \end{tabular} \\ \hline Full resolution (range gate of 50 m) & 95.65\% & 77.53\% & 87.18\% & 100\% \\ \hline Half resolution (data down-sampled to 100 m range gate) & 92.19\% & 60.92\% & 65.52\% & 75\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Success rates for wake and wake fragment identification and characterization for the Mask region-based CNN model with ResNet-101 feature pyramid network backbone. Figure 8: (**a**) Scan example; (**b**) wake skeletons (medial axes) for wakes and wake fragments present in the scan (red). Scan colored by flow field horizontal velocities (\(\mathbf{V_{H}}\)) normalized by calculated bulk wind speed (\(V\)). Scan parameters: arc scan, captured on 11 September 2018, 02:57 UTC, elevation angle \(\varnothing=3.5^{\circ}\), azimuth angle \(\mathbf{0}\in[\mathbf{290}^{\circ},\mathbf{345}^{\circ}]\), azimuthal resolution \(1^{\circ}\), range gate 50 m, 55 s scan time. The most common mode of failure for the CNN is a false negative--i.e., there is a wake or wake fragment in the scan and the model does not identify it as an object at all (Figure 9). The second most common mode of failure is returning a false positive--i.e., a non-wake (such as a wind turbine) structure is identified as a wake. False positives may also be attributed to uncertainties in radial velocity measurements that arise from misalignment between the lidar line of sight and the wind direction [50]. Failure associated with low IoU (incorrect wake shape) is very rare (one case). This indicates that, when the model does correctly identify a wake or wake fragment, it is highly skilled at returning the correct mask for the structure, especially if the structure is not obscured by data removal. Although more than three-quarters of wake fragments are captured by the model, they are associated with a higher number of false positives that are caused by the model's sensitivity to other low-velocity flow structures in the scans. Illustrative examples of wake identification in different scan sectors (see Figure 1b) are shown in Figures 10 and 11. The scan from Region A (northwest) exhibits a correct identification of wakes and wake fragments for a data-dense flow field (few velocity values are removed by the CNR screening) (Figure 10a,b). The CNN differentiates between the region of low velocity in the lower right corner of the scan and the wake that is near it. Further, the CNN is able to successfully identify and mask the complex wake fragment in the scan. The scan from Region B exhibits a larger area scanned than that of Region A because of a higher maximum range distance, which reduces the visibility of the wakes in the image (Figure 10c,d). Regardless, the CNN is able to successfully identify the wakes and wake fragments and output successful masks, even when wind turbine wakes are close to regions with missing radial velocity estimates.