Urban Zoning Using Higher-Order Markov Random Fields on Multi-View Imagery Data.

Tian Feng, Quang-Trung Truong, Duc Thanh Nguyen, Jing Yu Koh, Lap-Fai Yu, Alexander Binder, Sai-Kit Yeung

Abstract:

Urban zoning enables various applications in land use anal-
ysis and urban planning. As cities evolve, it is important to constantly
update the zoning maps of cities to reflect urban pattern changes. This
paper proposes a method for automatic urban zoning using higher-order
Markov random fields (HO-MRF) built on multi-view imagery data
including street-view photos and top-view satellite images. In the pro-
posed HO-MRF, top-view satellite data is segmented via a multi-scale
deep convolutional neural network (MS-CNN) and used in lower-order
potentials. Street-view data with geo-tagged information is augmented in
higher-order potentials. Various feature types for classifying street-view
images were also investigated in our work. We evaluated the proposed
method on a number of famous metropolises and provided in-depth anal-
ysis on technical issues.