Next Article in Journal
A Field Data Acquisition Method and Tools for Hazard Evaluation of Earthquake-Induced Landslides with Open Source Mobile GIS
Previous Article in Journal
A Quaternion-Based Piecewise 3D Modeling Method for Indoor Path Networks
Article Menu
Issue 2 (February) cover image

Export Article

Open AccessArticle
ISPRS Int. J. Geo-Inf. 2019, 8(2), 90; https://doi.org/10.3390/ijgi8020090

An Integrated Framework Combining Multiple Human Activity Features for Land Use Classification

1
School of Geography and Ocean Science, Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, Nanjing University, Nanjing 210023, China
2
Nanjing Municipal Commission of Development and Reform, Nanjing 210019, China
3
Collaborative Innovation Center of Novel Software Technology and Industrialization, Nanjing University, Nanjing 210023, China
*
Author to whom correspondence should be addressed.
Received: 8 January 2019 / Revised: 10 February 2019 / Accepted: 13 February 2019 / Published: 15 February 2019
Full-Text   |   PDF [5405 KB, uploaded 18 February 2019]   |  

Abstract

Urban land use information is critical to urban planning, but the increasing complexity of urban systems makes the accurate classification of land use extremely challenging. Human activity features extracted from big data have been used for land use classification, and fusing different features can help improve the classification. In this paper, we propose a framework to integrate multiple human activity features for land use classification. Features were fused by constructing a membership matrix reflecting the fuzzy relationship between features and land use types using the fuzzy c-means (FCM) clustering method. The classification results were obtained by the fuzzy comprehensive evaluation (FCE) method, which regards the membership matrix as the fuzzy evaluation matrix. This framework was applied to a case study using taxi trajectory data from Nanjing, and the outflow, inflow, net flow and net flow ratio features were extracted. A series of experiments demonstrated that the proposed framework can effectively fuse different features and increase the accuracy of land use classification. The classification accuracy achieved 0.858 (Kappa = 0.810) when the four features were fused for land use classification. View Full-Text
Keywords: big data; land use classification; human activity features; fuzzy comprehensive evaluation; fuzzy c-means big data; land use classification; human activity features; fuzzy comprehensive evaluation; fuzzy c-means
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Ge, P.; He, J.; Zhang, S.; Zhang, L.; She, J. An Integrated Framework Combining Multiple Human Activity Features for Land Use Classification. ISPRS Int. J. Geo-Inf. 2019, 8, 90.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
ISPRS Int. J. Geo-Inf. EISSN 2220-9964 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top