Urban land intensified utilization assessment based on artificial neural networks: a case of Lanzhou City

Pei-ji Shi; Ji-ping Gong

doi:10.1117/12.905837

28 October 2011 Urban land intensified utilization assessment based on artificial neural networks: a case of Lanzhou City

Pei-ji Shi, Ji-ping Gong

Author Affiliations +

Proceedings Volume 8205, 2011 International Conference on Photonics, 3D-Imaging, and Visualization; 82051V (2011) https://doi.org/10.1117/12.905837
Event: 2011 International Conference on Photonics, 3D-imaging, and Visualization, 2011, Guangzhou, China

Abstract

Land intensified utilization is an important part of land utilization research. Using the artificial neural network model (ANN) to evaluate a city's level of land utilization intensity, it will eliminate the negative impact of subjective factor which is used to assess and complex calculate. As a result, the assessment results would be more scientific and objectively. Based on intensity of land investment, land utilization intensity and land utilization efficiency, an intensive land conservation evaluation system is established with application of status of Lanzhou City's urban land. After training by testing data, BP artificial neural network was applied to evaluate the level of intensified utilization form 1988-2008 in Lanzhou. The results shown that the urban land utilization of Lanzhou is on the upward trend and that can be divided into three categories: low, moderate and intensive.

Citation Download Citation

Pei-ji Shi and Ji-ping Gong "Urban land intensified utilization assessment based on artificial neural networks: a case of Lanzhou City", Proc. SPIE 8205, 2011 International Conference on Photonics, 3D-Imaging, and Visualization, 82051V (28 October 2011); https://doi.org/10.1117/12.905837

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