Urban Land Use Information Retrieval Based on Scene Classification of Google Street View Images

Land use maps are very important references for the urban planning and management. However, it is difficult and time-consuming to get high-resolution urban land use maps. In this study, we propose a new method to derive land use information at building block level based on machine learning and geo-tagged street-level imagery – Google Street View images. Several commonly used generic image features (GIST, HoG, and SIFT-Fisher) are used to represent street-level images of different cityscapes in a case study area of New York City. Machine learning is further used to categorize different images based on the calculated image features of different street-level images. Accuracy assessment results show that the method developed in this study is a promising method for land use mapping at building block level in future.