Building instance classification using street view images

Kurzfassung

Land-use classification based on spaceborne or aerial remote sensing images has been extensively studied over the past few years. Such classification is usually a patch-wise or pixel-wise labeling over the whole image. But for many applications, such as urban population density mapping or urban utility planning, a classification map based on individual buildings is much more informative. However, such semantic classification still poses some fundamental challenges, for example, how to retrieve _ne boundaries of individual buildings. In this paper, we propose a general framework for classifying the functionality of individual buildings. The proposed method is based on Convolutional Neural Networks (CNN) which classify façade structures from street view images, such as Google StreetView, in addition to remote sensing images which usually only show roof structures. Geographic information is utilized to mask out individual buildings, and to associate the corresponding street view images. We build a benchmark dataset which is used for training and evaluating CNN. In addition, the method is applied to generate building classification maps on both region and city scales of several cities in Canada and the US.

Verfügbare Versionen dieses Eintrags

• Building instance classification using street view images. (deposited 13 Okt 2017 11:16) [Gegenwärtig angezeigt]
  • Building instance classification using street view images. (deposited 24 Mai 2018 13:55)
    • Building instance classification using street view images. (deposited 04 Dez 2018 13:08)