Long time-series analysis of urban development based on effective building extraction

Shuai Yuan; Runmin Dong; Juepeng Zheng; Wenzhao Wu; Lixian Zhang; Weijia Li; Haohuan Fu

doi:10.1117/12.2558125

21 April 2020 Long time-series analysis of urban development based on effective building extraction

Shuai Yuan, Runmin Dong, Juepeng Zheng, Wenzhao Wu, Lixian Zhang, Weijia Li, Haohuan Fu

Proceedings Volume 11398, Geospatial Informatics X; 113980M (2020) https://doi.org/10.1117/12.2558125
Event: SPIE Defense + Commercial Sensing, 2020, Online Only

Abstract

The effective detection of urban development is the basis of understanding urban sustainability. Although various studies concentrated on long-time-series analysis on urban development, the resolution of images was too low to focus on a single object. In this paper, we provide a long-time-series analysis of built-up areas at an annual frequency in Beijing, China, from 2000 to 2015, based on the automatic building extraction and high-resolution satellite images. We propose a deeplearning based method to extract buildings, and employ an ensemble learning method to improve the localization of boundaries. The time-series results of built-up areas are analyzed based on two schemes, i.e., change detection over the past fifteen years and evaluation of the whole region in three selected years. Our proposed method achieves an average overall accuracy (OA) of 93%. The results reveal that Beijing developed more rapidly during 2001-2008 than other periods in terms of the density and the number of buildings.

Conference Presentation

Citation Download Citation

Shuai Yuan, Runmin Dong, Juepeng Zheng, Wenzhao Wu, Lixian Zhang, Weijia Li, and Haohuan Fu "Long time-series analysis of urban development based on effective building extraction", Proc. SPIE 11398, Geospatial Informatics X, 113980M (21 April 2020); https://doi.org/10.1117/12.2558125

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available