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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Vydané v:	International journal of geographical information science : IJGIS Ročník 36; číslo 11; s. 2296 - 2321
Hlavní autori:	Yu, Wenhao, Chen, Yujie
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Abingdon Taylor & Francis 02.11.2022 Taylor & Francis LLC
Predmet:	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
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