Building instance classification using street view images

Land-use classification based on spaceborne or aerial remote sensing images has been extensively studied over the past decades. 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 utilit...

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Veröffentlicht in:ISPRS journal of photogrammetry and remote sensing Jg. 145; S. 44 - 59
Hauptverfasser: Kang, Jian, Körner, Marco, Wang, Yuanyuan, Taubenböck, Hannes, Zhu, Xiao Xiang
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier B.V 01.11.2018
Schlagworte:
Building instance classification
buildings
Canada
cities
CNN
data collection
land use
neural networks
OpenStreetMap
planning
population density
remote sensing
spatial data
Street view images
United States
urban population
Canada
United States
Street view images
CNN
OpenStreetMap
Building instance classification
ISSN:0924-2716, 1872-8235
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Zusammenfassung:Land-use classification based on spaceborne or aerial remote sensing images has been extensively studied over the past decades. 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 fine boundaries of individual buildings. In this paper, we proposed a general framework for classifying the functionality of individual buildings. The proposed method is based on Convolutional Neural Networks (CNNs) 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 was utilized to mask out individual buildings, and to associate the corresponding street view images. We created a benchmark dataset which was used for training and evaluating CNNs. In addition, the method was applied to generate building classification maps on both region and city scales of several cities in Canada and the US.
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ISSN:0924-2716
1872-8235
DOI:10.1016/j.isprsjprs.2018.02.006