Urban Land Use Identification Based on User-Generated Content and Utilizing Deep Learning Classification

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Golipoor Ali; TALEAI MOHAMMAD; ALESHEIKH ALI ASGHAR; JAVADI GHASEM

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Journal Paper

Paper Information

Journal: ENGINEERING JOURNAL OF GEOSPATIAL INFORMATION TECHNOLOGY Year:2022 | Volume:9 | Issue:4 Page(s): 43-61

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Information Journal Paper

Title

Urban Land Use Identification Based on User-Generated Content and Utilizing Deep Learning Classification

Author(s)

Golipoor Ali | TALEAI MOHAMMAD | ALESHEIKH ALI ASGHAR | JAVADI GHASEM | Issue Writer Certificate

Keywords

Deep Learning

Land use

User-generated Content

Recurrent Neural Network

Twitter

Abstract

One of the necessary pieces of information for policy-making and urban management is an up-to-date Land use map, while the time and cost of producing and updating spatial information using traditional mapping methods and by national or private mapping organizations are too high. The advancement of technology such as smart phones, real-time positioning, and social network development has resulted in the mass production of User Generated Geographic Content (UGGC). The purpose of this study is to identify the Land use type of the parcels using UGGCs. In this research six categories of urban Land use types have been taken into cosideration: residential, commercial/shopping, office/service, mixed, entertainment/recreational, and the other ones,and the social network data of Twitter is used as User-generated Content. Deep Learning classification and Recurrent Neural Network (RNN) are utilized to analyze the user-generated data. To eliminate the imbalance of the input data, the Support Vector Machine (SVM) algorithm is utilized. Evaluation of the results of the proposed method demonstrates classification of urban Land uses with an overall accuracy of 64%. Among urban use classes, the residential one is the best with 77 percent accuracy. The area under the ROC curve is also 0. 88, which indicates the appropriate reliability of the proposed method. To eliminate data imbalance, comparing the results of the SVM algorithm with the random method of sampling, reveals that SVM presents higher accuracy.

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APA: Copy

Golipoor, Ali, TALEAI, MOHAMMAD, ALESHEIKH, ALI ASGHAR, & JAVADI, GHASEM. (2022). Urban Land Use Identification Based on User-Generated Content and Utilizing Deep Learning Classification. ENGINEERING JOURNAL OF GEOSPATIAL INFORMATION TECHNOLOGY, 9(4 ), 43-61. SID. https://sid.ir/paper/1051341/en

Vancouver: Copy

Golipoor Ali, TALEAI MOHAMMAD, ALESHEIKH ALI ASGHAR, JAVADI GHASEM. Urban Land Use Identification Based on User-Generated Content and Utilizing Deep Learning Classification. ENGINEERING JOURNAL OF GEOSPATIAL INFORMATION TECHNOLOGY[Internet]. 2022;9(4 ):43-61. Available from: https://sid.ir/paper/1051341/en

IEEE: Copy

Ali Golipoor, MOHAMMAD TALEAI, ALI ASGHAR ALESHEIKH, and GHASEM JAVADI, “Urban Land Use Identification Based on User-Generated Content and Utilizing Deep Learning Classification,” ENGINEERING JOURNAL OF GEOSPATIAL INFORMATION TECHNOLOGY, vol. 9, no. 4 , pp. 43–61, 2022, [Online]. Available: https://sid.ir/paper/1051341/en

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