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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Abstract 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 plausible and context-aware details. In this paper, we propose an effective framework for vector-structured polyline completion using a generative model. The model is trained to generate the contents of missing polylines of different sizes and shapes conditioned on the contexts. Specifically, the generator can compute the content of the entire polyline sample globally and produce a plausible prediction for local gaps. The proposed model was applied to contour data for validation. The experiments generated gaps of random sizes at random locations along with the polyline samples. Qualitative and quantitative evaluations show that our model can fill missing points with high perceptual quality and adaptively handle a range of gaps. In addition to the simulation experiment, two case studies with map vectorization and trajectory filling illustrate the application prospects of our model.
AbstractList 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 plausible and context-aware details. In this paper, we propose an effective framework for vector-structured polyline completion using a generative model. The model is trained to generate the contents of missing polylines of different sizes and shapes conditioned on the contexts. Specifically, the generator can compute the content of the entire polyline sample globally and produce a plausible prediction for local gaps. The proposed model was applied to contour data for validation. The experiments generated gaps of random sizes at random locations along with the polyline samples. Qualitative and quantitative evaluations show that our model can fill missing points with high perceptual quality and adaptively handle a range of gaps. In addition to the simulation experiment, two case studies with map vectorization and trajectory filling illustrate the application prospects of our model.
Author Yu, Wenhao
Chen, Yujie
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Snippet Geospatial studies must address spatial data quality, especially in data-driven research. An essential concern is how to fill spatial data gaps (missing data),...
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SubjectTerms cartographic polylines
Cartography
Coders
Deep learning
encoder-decoder model
Filling gap
Missing data
Spatial data
spatial data quality
Vector processing (computers)
Title Filling gaps of cartographic polylines by using an encoder-decoder model
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