## 7 Conclusions and Future Work  
This paper investigates the problem of urban land cover classification by using Sentinel-2 satellite remote sensing imageries and machine learning based classifiers. In particular, Bayesian optimization is drawn to automatically tune the hyperparameters of random forest classifiers so that its performance can be improved. An urban land cover classification example in Beijing, China is drawn to demonstrate the performance of optimized random forest classifier against the random forest with default parameters and the classical support vector machine (SVM) classifier. In performance evaluation, RGB band features of Sentinel-2 satellite are firstly considered by employing three different methods. The results show that the optimized random forest classifier achieves the best performance with overall accuracy (OA: 0.879), kappa coefficient (0.8210), whereas SVM achieves a low OA and kappa value of 0.461 and 0.2526, respectively. Then, full band features are evaluated by the three methods, and it is shown that the optimized random forest still possesses the highest value of OA (0.983) and kappa value (0.9751). In addition, the classifiers with more useful band information outperform the ones with only RGB band information. Therefore, the developed random forest classifier with Bayesian hyperparameter optimization is expected to provide better urban land cover classification performance so that city managements can be achieved in a more precise manner. Meanwhile, with a suitable training dataset, this method can also find a wide range of applications in land resource management, green infrastructure monitoring, disaster management, and agricultural applications.  
Although the results in this study are quite promising, there is still much room for further improvement. For instance, due to the logistics issues, only a small set of training dataset is used to assess the performance of the algorithms. With the advent of a more labeled dataset, the performance can be evaluated in a more accurate manner. Moreover, this study is mainly focused on applying spectral information for land cover classification, and spatial information can also provide vital information. In addition to machine learning methods, the popular deep learning approaches such as a Convolutional Neural Network (CNN) can also be drawn to simultaneously learn the spectral and spatial information in an end-to-end manner and possible improved performance.  
Conceptualization, T.Z., J.S. and [PERSON]; methodology, [PERSON], [PERSON] and [PERSON]; software, [PERSON]; validation, [PERSON], [PERSON] and J.L.; formal analysis, [PERSON], [PERSON]; investigation, [PERSON], Z.X.; resources, [PERSON], [PERSON]; data curation, [PERSON]; writing--original draft preparation, [PERSON]; writing--review and editing, [PERSON], J.L., Z.X. and Y.L.; visualization, [PERSON], Y.L.; supervision, J.L.; project administration, J.L.; funding acquisition, J.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Fundamental Research Funds for the China Central Universities of USTB (FRF-DF-19-002), Scientific and Technological Innovation Foundation of Shunde Graduate School, USTB (BK20 BE014).  
Not applicable.  
No new data were created or analyzed in this study. Data sharing is not applicable to this article.  
The authors declare no conflict of interest.