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http://dx.doi.org/10.7780/kjrs.2021.37.4.7

Establishment of Priority Update Area for Land Coverage Classification Using Orthoimages and Serial Cadastral Maps  

Song, Junyoung (Department of Advanced Technology Fusion, Konkuk University)
Won, Taeyeon (Department of Advanced Technology Fusion, Konkuk University)
Jo, Su Min (Department of Civil and Environmental Engineering, Konkuk University)
Eo, Yang Dam (Department of Civil and Environmental Engineering, Konkuk University)
Park, Jin Sue (Project Development Division, ALLforLAND.Co.Ltd)
Publication Information
Korean Journal of Remote Sensing / v.37, no.4, 2021 , pp. 763-776 More about this Journal
Abstract
This paper introduces a method of selecting priority update areas for subdivided land cover maps by training orthoimages and serial cadastral maps in a deep learning model. For the experiment, orthoimages and serial cadastral maps were obtained from the National Spatial Data Infrastructure Portal. Based on the VGG-16 model, 51,470 images were trained on 33 subdivided classifications within the experimental area and an accuracy evaluation was conducted. The overall accuracy was 61.42%. In addition, using the differences in the classification prediction probability of the misclassified polygon and the cosine similarity that numerically expresses the similarity of the land category features with the original subdivided land cover class, the cases were classified and the areas in which the boundary setting was incorrect and in which the image itself was determined to have a problem were identified as the priority update polygons that should be checked by operators.
Keywords
Deep learning; Land cover map; Orthoimage; Serial cadastral map; VGG-16;
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Times Cited By KSCI : 1  (Citation Analysis)
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