## 1 Introduction  
Land cover classification (LCC) is able to reflect the potential natural and social process in urban development so that the vital information can be extracted to key stakeholders [1, 2]. Earth observation satellite, one of the most significant platforms, is widely applied for LCC due to their customized sensors which are able to provide extensive geographical coverage while with an affordable cost for spatial and temporal land use/cover mapping [3]. In particular, LCC using remote sensing images of high spatial/spectral resolutions is playing a paramount role in urban planning, land resource management, green infrastructure monitoring, disaster management, and agricultural applications [4, 5, 6, 7]. In China, the largest developing country, rapid urbanization has been changing its geographic characteristics, particularly for urban areas where the balance of environment and urban infrastructures is gradually being impaired. Therefore, land cover classification for urban areas is of great importance to assess its changes for its sustainable development [6]. With the advent of various earth observation satellites, image quality in terms of spatial, spectral, and temporal resolutions is constantly improving and so the LCC performance can be guaranteed. Among the freely accessible satellites, the newly launched Sentinel-2 series satellite composed of Sentinel-2A and Sentinel-2B possesses the best spatial, spectral, and temporal resolutions [8, 9], which are a key part of the Global Monitoring for Environment and Security program supported by the European Space Agency (ESA). Its Multi-Spectral Instrument (MSI) features 13 bands from visible bands to short waved infrared (SWIR) bands. In addition, three different spatial resolutions (e.g., 10 m, 20 m, and 60 m) are provided for various tailored tasks [10]. A number of qualitative and quantitative studies have been done for Sentinel-2A satellite on land management, urban planning, ecosystem monitoring, and smart farming [11, 12, 5, 7, 3, 5]. Sentinel-2B was launched on 7 March 2017 to complement Sentinel-2A for a better temporal resolution. Therefore, in this study, Sentinel-2A/B satellites are selected to provide the high-resolution remote sensing images for the purpose of urban land cover classification. On the other hand, it is widely acknowledged that the selection of classification method can significantly affect the land use/cover mapping performance [3]. The ever-increasing computation power and the advanced classification algorithms are making the land use/cover classification more accurate than ever before, where the commonly used algorithms may include the Support Vector Machine (SVM), k-Nearest Neighbors (KNN), Decision Tree (DT), Artificial Neural Networks (ANN), and Random forest (RF) [5, 13, 14, 15]. Machine learning based classifiers, such as SVM, ANN, and RF, are able to cope with unbalanced and noisy datasets in LCC, yielding better classification performance over traditional parametric approaches [16]. However, in these machine learning methods, model hyperparameters should be appropriately set in order to get satisfying classification results. In conventional approaches, hyperparameters are usually set empirically or tuned manually. As a result, these manually set hyperparameters are insufficient to obtain accurate and reliable land cover classification performance and therefore alternative approaches should be sought. Consequently, it is desirable to develop an automated and systematic approach to determine the model hyperparameters before a reliable and accurate classifier being realized. Bayesian optimization is a promising method for parameter tuning/optimization; however, until now, very few studies have been available to apply it for model hyperparameter tuning, especially for land cover classification with satellite images. Therefore, Bayesian optimization is adopted to tune the hyperparameters of the widely used RF classifier for land cover classification.