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Contextual Building Selection Based on a Genetic Algorithm in Map Generalization
Article

Machine Learning Classification of Buildings for Map Generalization

1
Department of Civil and Environmental Engineering, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Korea
2
Institute of Construction and Environmental Engineering, Seoul National University, 1, Gwanak-ro, Gwanak-gu, Seoul 08826, Korea
*
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2017, 6(10), 309; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi6100309
Received: 3 August 2017 / Revised: 7 October 2017 / Accepted: 11 October 2017 / Published: 18 October 2017
(This article belongs to the Special Issue Machine Learning for Geospatial Data Analysis)
A critical problem in mapping data is the frequent updating of large data sets. To solve this problem, the updating of small-scale data based on large-scale data is very effective. Various map generalization techniques, such as simplification, displacement, typification, elimination, and aggregation, must therefore be applied. In this study, we focused on the elimination and aggregation of the building layer, for which each building in a large scale was classified as “0-eliminated,” “1-retained,” or “2-aggregated.” Machine-learning classification algorithms were then used for classifying the buildings. The data of 1:1000 scale and 1:25,000 scale digital maps obtained from the National Geographic Information Institute were used. We applied to these data various machine-learning classification algorithms, including naive Bayes (NB), decision tree (DT), k-nearest neighbor (k-NN), and support vector machine (SVM). The overall accuracies of each algorithm were satisfactory: DT, 88.96%; k-NN, 88.27%; SVM, 87.57%; and NB, 79.50%. Although elimination is a direct part of the proposed process, generalization operations, such as simplification and aggregation of polygons, must still be performed for buildings classified as retained and aggregated. Thus, these algorithms can be used for building classification and can serve as preparatory steps for building generalization. View Full-Text
Keywords: aggregation; building generalization; classification; elimination; map generalization; machine learning aggregation; building generalization; classification; elimination; map generalization; machine learning
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MDPI and ACS Style

Lee, J.; Jang, H.; Yang, J.; Yu, K. Machine Learning Classification of Buildings for Map Generalization. ISPRS Int. J. Geo-Inf. 2017, 6, 309. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi6100309

AMA Style

Lee J, Jang H, Yang J, Yu K. Machine Learning Classification of Buildings for Map Generalization. ISPRS International Journal of Geo-Information. 2017; 6(10):309. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi6100309

Chicago/Turabian Style

Lee, Jaeeun, Hanme Jang, Jonghyeon Yang, and Kiyun Yu. 2017. "Machine Learning Classification of Buildings for Map Generalization" ISPRS International Journal of Geo-Information 6, no. 10: 309. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi6100309

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