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Automated thinning of road networks and road labels for multiscale design of The National Map of the United States

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References

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2010A Road Network Selection Process Based on Data Enrichment and Structure Detection120 cited
2009Feature pruning by upstream drainage area to support automated generalization of the United States National Hydrography Dataset61 cited
2011A typology of operators for maintaining legible map designs at multiple scales55 cited
2012A comparative study of various strategies to concatenate road segments into strokes for map generalization50 cited
2009Specifying Map Requirements for Automated Generalization of Topographic Data44 cited
2011Adapting Generalization Tools to Physiographic Diversity for the United States National Hydrography Dataset38 cited
2009Mastering map scale: balancing workloads using display and geometry change in multi-scale mapping34 cited

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2014Generalisation Operators22 cited
2014Evaluation in Generalisation17 cited
2021Multimodal travel‐time maps with formally correct and schematic isochrones

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14 cited

2016Centrality-based hierarchy for street network generalization in multi-resolution maps14 cited

2017Partial polygon pruning of hydrographic features in automated generalization12 cited

2023Multiple representations in geospatial databases, the brain’s spatial cells, and deep learning algorithms6 cited

2023A raster-based method for the hierarchical selection of river networks based on stream characteristics5 cited

2018Methodology of the automatic generalization of buildings, road networks, forests and surface waters: a case study based on the Topographic Objects Database in Poland5 cited

2017A Representation Method for Complex Road Networks in Virtual Geographic Environments4 cited

2021Discrepancies in the spatial extent of common GIS highway networks3 cited