# Supervised Segmentation of NO2 Plumes from Individual Ships Using Troppomi Satellite Data
## 5 Conclusions  
In this study, we presented a new supervised-learning-based method for the automatic evaluation of emission plumes produced by individual ships using satellite data. The experiments were performed using NO\({}_{2}\) measurements from the TROPOMI/S5P satellite. We started with the enhancement of the satellite data in order to increase the contrast between the ship plume and the background. The applied image pre-processing technique enhances the intensity of high-value pixels located in a cluster (plume) and suppresses random concentration peaks in the background. We then automatically assigned a _ship sector_ to each analyzed ship, which excludes from the analysis parts of the image where the plume of the studied ship cannot be located based on wind conditions and the speed/direction of the ship.  
As a next step, we presented a feature construction method consisting of the normalization of the _ship sector_ and its division into smaller sub-regions. Each sub-region has a different probability to contain a plume produced by the ship of interest. This way, we differentiated the plume produced by the ship of interest from all the other plumes potentially located within the _ship sector_. The set of newly created spatial _ship-sector_-based features allowed us to perform ship plume segmentation using multivariate machine learning models. The application of the multivariate models gives the possibility to support the ship plume segmentation process with a set of additional one-dimensional features such as ship characteristics and speed.  
We integrated several data sources into a multivariate dataset. We manually labeled the data, so that the problem of individual ship plume segmentation can be addressed with supervised learning.  
We trained a set of robust linear and nonlinear multivariate classifiers and compared their performance with the segmentation results of thresholding-based univariate benchmarks. All studied nonlinear classifiers showed superior results in comparison to both linear models and univariate benchmarks. With the XGBoost model, we were able to achieve more than a 20% increase in the segmentation average precision in comparison to the best benchmark univariate model.  
We validated the proposed methodology using an independent measure, i.e., a theoretically derived NO\({}_{x}\) ship emission proxy that we used as a reference value. For the comparison, we estimated the amount of NO\({}_{2}\) produced by each of the analyzed ships and calculated the Pearson correlation of the obtained results with the ship emission proxy. We compared the obtained correlations and the number of plumes segmented by each of the studied models with the results obtained from manual segmentation. We showed that, with the XGBoost model, we are able to segment more plumes while achieving a 6.8% higher correlation with the emission proxy than when the plumes were segmented manually. That might suggest that the proposed method is able to find plumes that are hardly or not detectable by the human eye.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Segmentation Method** & **Pearson Correlation** & **Number of Detected Plumes** \\ \hline XGBoost & 0.834 & 371 \\ _Manual Labeling_ & _0.781_ & 334 \\ Random Forest & 0.775 & 436 \\ NO\({}_{2}\) & 0.774 & 334 \\ Logistic & 0.766 & 452 \\ Linear SVM & 0.765 & 452 \\ RBF SVM & 0.757 & 447 \\ Moran’s \(I\) on high NO\({}_{2}\) & 0.733 & 422 \\ Moran’s \(I\) & 0.681 & 448 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results on the comparison between the estimated amount of NO\({}_{2}\) and theoretically derived NO\({}_{x}\) ship emission proxy. Sorted in accordance with the achieved level of the Pearson correlation. Italic font indicates baseline results.