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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.
Highlights
Land cover is a key environmental information for a variety of social needs, which range from natural resource management, urban planning, climate change modelling to sustainable development [Chen, Chen, Liao et al (2015); Chen, Li, Wu et al (2017)]
We propose a novel approach for land cover mapping using Web maps
Linear features are first utilised to delineate parcels, which are considered basic units for land cover mapping. It is followed by constructing spatial factors from point features and polygon features
Summary
Land cover is a key environmental information for a variety of social needs, which range from natural resource management, urban planning, climate change modelling to sustainable development [Chen, Chen, Liao et al (2015); Chen, Li, Wu et al (2017)]. Remote sensing is considered a reliable tool to generate land cover maps because of the wide availability, high spatial and spectral resolution and automatic classification process [Rogan and Chen (2004); Wulder, White, Goward et al (2008); Giri, Pengra, Long et al (2013); Hansen, Potapov, Moore et al (2013)]. The different approaches of land cover mapping are primarily conducted in two perspectives: pixel-based and object-based classification. As only coarse levels of spatial distribution and semantic features from ground components can be monitored, remote sensing usually concentrates on capturing physical properties instead of human activity, which is one of the main causes of land cover changes [Hu, Yang, Li et al (2016); Liu, He, Yao et al (2017)]. Considering the limitations in mining land cover information, it is necessary to focus on the investigation of more effective data sources that enable capturing social activities, which are required for land cover mapping
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