Open Access

ARTICLE

Inferring Spatial Distribution Patterns in Web Maps for Land Cover Mapping

Yang Liu¹, Zeying Lan⁴, Hanfa Xing^2,3,*

1 Guangzhou Urban Planning & Design Survey Research Institute, Guangzhou, 510060, China.
2 College of Geography and Environment, Shandong Normal University, Jinan, 250300, China .
3 School of Geography Sciences, South China Normal University, Guangzhou, 510631, China.
4 School of management, Guangdong University of Technology, Guangzhou, 510520, China.

* Corresponding Author: Hanfa Xing. Email: .

(This article belongs to the Special Issue: Beyond the Hypes of Geospatial Big Data: Theories, Methods, Analytics, and Applications)

Computer Modeling in Engineering & Sciences 2019, 119(2), 311-330. https://doi.org/10.32604/cmes.2019.04284

Abstract

Web maps represent an effective source for land cover mapping in capturing human activities. However, due to spatial heterogeneity, previous research has mainly focused on generating land cover maps in partial areas. Inferring spatial distribution patterns in Web maps may provide an alternative perspective on improving map production on a larger scale. This paper represents a novel approach to investigating the spatial distribution in Web maps for land cover mapping. First, linear features from Web maps are utilised to delineate parcels with insufficient Web map data for classification. Then, spatial factors are constructed from point and polygon features to identify the spatial variety of Web maps, with an artificial neural network classifier being adopted to classify land cover automatically. Land cover mapping is finally proposed by combining classified parcels and existing polygon features. The proposed method is applied in Guangzhou, Guangdong Province, using a Web map from AutoNavi. The results show an approximately 88% classification accuracy and an overall mapping accuracy of 85.06%. The results indicate that the proposed approach has the potential to be utilised in land cover mapping, and the constructed spatial factors are effective at characterising land cover information.

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