Urban Land Use Classification Based on Aerial and Ground Images

Abstract

Urban land use is key to rational urban planning and management. Traditional land use classification methods rely heavily on domain experts, which is both expensive and inefficient. In this paper, we explore to utilise deep neural network based approaches to label urban land use at pixel level using high-resolution aerial images and ground-level street images. We use a deep neural network to extract semantic features from sparsely distributed street images and interpolate them in the spatial domain to match the spatial resolution of the aerial images, which are then fused together through a deep neural network for classifying land use categories. We test our methods on a large publicly available aerial and street images dataset of New York City, and the results show that using aerial images alone can achieve relatively high classification accuracy and the ground-level street views contain useful information for urban land use classification. Fusing street image features with aerial images can improve classification accuracy to some extent but the improvement is somewhat limited.