Filling gaps of cartographic polylines by using an encoder-decoder model

Geospatial studies must address spatial data quality, especially in data-driven research. An essential concern is how to fill spatial data gaps (missing data), such as for cartographic polylines. Recent advances in deep learning have shown promise in filling holes in images with semantically plausib...

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Bibliographic Details

Published in:	International journal of geographical information science : IJGIS Vol. 36; no. 11; pp. 2296 - 2321
Main Authors:	Yu, Wenhao, Chen, Yujie
Format:	Journal Article
Language:	English
Published:	Abingdon Taylor & Francis 02.11.2022 Taylor & Francis LLC
Subjects:	cartographic polylines Cartography Coders Deep learning encoder-decoder model Filling gap Missing data Spatial data spatial data quality Vector processing (computers)
ISSN:	1365-8816, 1362-3087, 1365-8824
Online Access:	Get full text
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