期刊
INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE
卷 36, 期 11, 页码 2296-2321出版社
TAYLOR & FRANCIS LTD
DOI: 10.1080/13658816.2022.2055036
关键词
Filling gap; cartographic polylines; spatial data quality; encoder-decoder model
类别
资金
- National Natural Science Foundation of China [42071442]
- Fundamental Research Funds for the Central Universities, China University of Geosciences (Wuhan) [CUG170640]
This paper proposes an effective framework for vector-structured polyline completion using a generative model, which can fill missing points in geospatial data with high perceptual quality. Experimental results show that the model can adaptively handle different gaps and produce semantically plausible predictions.
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.
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