Mapping Urban Areas in China Using Multisource Data With a Novel Ensemble SVM Method

The mapping of urban areas at regional to global scales is a crucial task due to its value for environmental monitoring, habitat and biodiversity conservation, and decision-making. In most current applications, two techniques (i.e., supervised classification and data fusion) are widely applied in large-scale urban mapping. However, the costly training sample collection, inadequate data-source descriptions, and diverse urban characteristics (e.g., shape, size, socioeconomic status, and physical environment) are challenging problems for the urban mapping approaches. In this context, aiming at effectively deriving accurate urban areas at a large scale, we propose a novel ensemble support vector machine (SVM) method which consists of three steps: 1) the automatic generation of training data to reduce labor costs; 2) the construction of an ensemble SVM model to effectively combine the multisource data (including remote sensing and socioeconomic data); and 3) an adaptive patch-based thresholding technique to tackle the diverse urban characteristics. The proposed method is employed to map urban areas of China in 2005 and 2010, and the resulting maps are compared with the existing urban maps for 287 prefecture-level cities. It is found that our results present a satisfactory superiority, especially in challenging small cities, with a significant improvement in median Kappa (0.174 for 2005 and 0.203 for 2010). When incorporating moderate-resolution imaging spectroradiometer multispectral data as an additional source, the Kappa coefficient can be further raised by 0.028 for 2010. In general, the proposed method shows great potential for accurately mapping urban areas at regional, continental, or even global scales in a cost-effective manner.