Building block level urban land-use information retrieval based on Google Street View images

Abstract

Land-use maps are important references for urban planning and urban studies. Given the heterogeneity of urban land-use types, it is difficult to differentiate different land-use types based on overhead remotely sensed data. Google Street View (GSV) images, which capture the façades of building blocks along streets, could be better used to judge the land-use types of different building blocks based on their façade appearances. Recently developed scene classification algorithms in computer vision community make it possible to categorize different photos semantically based on various image feature descriptors and machine-learning algorithms. Therefore, in this study, we proposed a method to derive detailed land-use information at building block level based on scene classification algorithms and GSV images. Three image feature descriptors (i.e., scale-invariant feature transform-Fisher, histogram of oriented gradients, GIST) were used to represent GSV images of different buildings. Existing land-use maps were used to create training datasets to train support vector machine (SVM) classifiers for categorizing GSV images. The trained SVM classifiers were then applied to case study areas in New York City, Boston, and Houston, to predict the land-use information at building block level. Accuracy assessment results show that the proposed method is suitable for differentiating residential buildings and nonresidential buildings with an accuracy of 85% or so. Since the GSV images are publicly accessible, this proposed method would provide a new way for building block level land-use mapping in future.