Abstract
The extraction of urban building information has important practical implications for the dynamic monitoring of urban land use, urban planning, and construction. Modern remote sensing technology provides the capacity and methods to effectively meet these goals. At present, image classification is an important means of extracting building features but often requires the manual selection of samples. The process is complex as well as time- and labor-consuming, and is somewhat subjective. For these reasons, this paper used points of interest (POI) data from electronic map as samples to develop a method for extracting urban buildings without manual training samples for supervised classification. The experimental results showed that POI data-supervised classification is an effective method for extracting urban buildings. The classification accuracy was similar to that of the manual sampling classification method and far superior to the unsupervised classification results. Further testing for sample size selection showed that the classification accuracy could be maintained at about 80% with 100 or more POI samples. Finally, the method adopted in this paper did not require manual interpretation, was low-cost, and had both high and objective classification accuracy.
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Wang, Y., Wang, H., Cao, Y. (2019). Building Information Extraction Based on Electronic Map Points of Interest. In: Xie, Y., Zhang, A., Liu, H., Feng, L. (eds) Geo-informatics in Sustainable Ecosystem and Society. GSES 2018. Communications in Computer and Information Science, vol 980. Springer, Singapore. https://doi.org/10.1007/978-981-13-7025-0_46
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DOI: https://doi.org/10.1007/978-981-13-7025-0_46
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