Semantic Scholar Open Access 2018 340 sitasi

Building Instance Classification Using Street View Images

Jian Kang Marco Körner Yuanyuan Wang H. Taubenböck Xiaoxiang Zhu

Lihat Sumber DOI

Abstrak

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 facade 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.

Topik & Kata Kunci

Computer Science Geography Engineering

Penulis (5)

Jian Kang

Marco Körner

Yuanyuan Wang

H. Taubenböck

Xiaoxiang Zhu

Format Sitasi

APA MLA BibTeX

Kang, J., Körner, M., Wang, Y., Taubenböck, H., Zhu, X. (2018). Building Instance Classification Using Street View Images. https://doi.org/10.1016/j.isprsjprs.2018.02.006

Akses Cepat

Lihat di Sumber doi.org/10.1016/j.isprsjprs.2018.02.006

Informasi Jurnal

Tahun Terbit: 2018
Bahasa: en
Total Sitasi: 340×
Sumber Database: Semantic Scholar
DOI: 10.1016/j.isprsjprs.2018.02.006
Akses: Open Access ✓