Urban green space forms an integral part of urban ecosystems. Quantifying urban green space is of substantial importance for urban planning and development. Considering the drawbacks of previous urban green space index models, which established either through a grid method or green distribution, and the difficulty of the validation process of earlier urban green space index models, this study exploits the advantages of multisource high-resolution remote sensing data to establish a Building Neighborhood Green Index (BNGI) model. The model which analyzes the spatial configuration of built-up areas and the vegetation is based on the building-oriented method and considers four parameters - Green Index (GI), proximity to green, building sparsity, and building height. Comparing BNGI with GI in different types of characteristic building regions, it was found that BNGI evaluates urban green space more sensitively. It was also found that high-rise low-sparsity area has a lower mean value of BNGI (0.56) as compared with that of low-rise low-sparsity neighborhood (0.62), whereas mean GI (0.24) is equal for both neighborhoods. Taking characteristics of urban building and green types into consideration, BNGI model can be effectively used in many fields such as land suitability analysis and urban planning.