Land‐Use Classification with Integrated Data
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The identification of the usage and coverage of the land is a major part of regional development. Crowdsourced geographic information systems provide valuable information about the land-use of different regions. Although these data sources lack reliability and possess some limitations, they are useful in deriving building blocks for the usage of the land, where the manual surveys are not up-to-date, costly and time-consuming. At present, in the context of Sri Lanka, there is a lack of reliable and updated land-use data. Moreover, there is a rapid growth in the construction industry, resulting in frequent changes in land-use and land-cover data. This paper presents a novel and an automated methodology based on learning models for identifying the usage and coverage of the land. The satellite imagery is used to identify the information related to land-cover. They are integrated with Foursquare venue data, which is a popular crowdsourced geographic information, thus enhancing the information level and the quality of land-use visualization. The proposed methodology has shown a kappa coefficient of 74.03%, showing an average land-use classification accuracy within a constrained environment.
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