FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2991566686> ?p ?o ?g. }

• W2991566686 endingPage "968" @default.
• W2991566686 startingPage "947" @default.
• W2991566686 abstract "Road intersection data have been used across a range of geospatial analyses. However, many datasets dating from before the advent of GIS are only available as historical printed maps. To be analyzed by GIS software, they need to be scanned and transformed into a usable (vector-based) format. Because the number of scanned historical maps is voluminous, automated methods of digitization and transformation are needed. Frequently, these processes are based on computer vision algorithms. However, the key challenges to this are (1) the low conversion accuracy for low quality and visually complex maps, and (2) the selection of optimal parameters. In this paper, we used a region-based deep convolutional neural network-based framework (RCNN) for object detection, in order to automatically identify road intersections in historical maps of several cities in the United States of America. We found that the RCNN approach is more accurate than traditional computer vision algorithms for double-line cartographic representation of the roads, though its accuracy does not surpass all traditional methods used for single-line symbols. The results suggest that the number of errors in the outputs is sensitive to complexity and blurriness of the maps, and to the number of distinct red-green-blue (RGB) combinations within them." @default.
• W2991566686 created "2019-12-05" @default.
• W2991566686 creator A5005453674 @default.
• W2991566686 creator A5090417785 @default.
• W2991566686 date "2019-11-28" @default.
• W2991566686 modified "2023-10-06" @default.
• W2991566686 title "Automatic extraction of road intersection points from USGS historical map series using deep convolutional neural networks" @default.
• W2991566686 cites W1535289548 @default.
• W2991566686 cites W1536680647 @default.
• W2991566686 cites W1565784015 @default.
• W2991566686 cites W1805640668 @default.
• W2991566686 cites W1849277567 @default.
• W2991566686 cites W1861492603 @default.
• W2991566686 cites W1954200810 @default.
• W2991566686 cites W2031489346 @default.
• W2991566686 cites W2032060170 @default.
• W2991566686 cites W2037437396 @default.
• W2991566686 cites W2050957655 @default.
• W2991566686 cites W2057912161 @default.
• W2991566686 cites W2058386549 @default.
• W2991566686 cites W2066624635 @default.
• W2991566686 cites W2088049833 @default.
• W2991566686 cites W2097117768 @default.
• W2991566686 cites W2099601760 @default.
• W2991566686 cites W2102605133 @default.
• W2991566686 cites W2108900252 @default.
• W2991566686 cites W2114060521 @default.
• W2991566686 cites W2124537036 @default.
• W2991566686 cites W2147844621 @default.
• W2991566686 cites W2159882774 @default.
• W2991566686 cites W2194775991 @default.
• W2991566686 cites W2209743375 @default.
• W2991566686 cites W2248723555 @default.
• W2991566686 cites W2515526268 @default.
• W2991566686 cites W2557728737 @default.
• W2991566686 cites W2559001317 @default.
• W2991566686 cites W2560473673 @default.
• W2991566686 cites W2570343428 @default.
• W2991566686 cites W2613825824 @default.
• W2991566686 cites W2616266455 @default.
• W2991566686 cites W2755255259 @default.
• W2991566686 cites W2769649475 @default.
• W2991566686 cites W2774356095 @default.
• W2991566686 cites W2778830079 @default.
• W2991566686 cites W2782762915 @default.
• W2991566686 cites W2792998389 @default.
• W2991566686 cites W2793442917 @default.
• W2991566686 cites W2794012889 @default.
• W2991566686 cites W2794301933 @default.
• W2991566686 cites W2795061970 @default.
• W2991566686 cites W2798146972 @default.
• W2991566686 cites W2799703355 @default.
• W2991566686 cites W2801461883 @default.
• W2991566686 cites W2803867573 @default.
• W2991566686 cites W2804356489 @default.
• W2991566686 cites W2807037009 @default.
• W2991566686 cites W2810358824 @default.
• W2991566686 cites W2884238566 @default.
• W2991566686 cites W2894989261 @default.
• W2991566686 cites W2963150697 @default.
• W2991566686 cites W2963980515 @default.
• W2991566686 cites W2964095005 @default.
• W2991566686 cites W2964350391 @default.
• W2991566686 cites W2979869928 @default.
• W2991566686 cites W3098357269 @default.
• W2991566686 cites W3104341624 @default.
• W2991566686 cites W3104668471 @default.
• W2991566686 cites W3106250896 @default.
• W2991566686 cites W4248193320 @default.
• W2991566686 cites W639708223 @default.
• W2991566686 cites W7746136 @default.
• W2991566686 doi "https://doi.org/10.1080/13658816.2019.1696968" @default.
• W2991566686 hasPublicationYear "2019" @default.
• W2991566686 type Work @default.
• W2991566686 sameAs 2991566686 @default.
• W2991566686 citedByCount "32" @default.
• W2991566686 countsByYear W29915666862020 @default.
• W2991566686 countsByYear W29915666862021 @default.
• W2991566686 countsByYear W29915666862022 @default.
• W2991566686 countsByYear W29915666862023 @default.
• W2991566686 crossrefType "journal-article" @default.
• W2991566686 hasAuthorship W2991566686A5005453674 @default.
• W2991566686 hasAuthorship W2991566686A5090417785 @default.
• W2991566686 hasBestOaLocation W29915666862 @default.
• W2991566686 hasConcept C115961682 @default.
• W2991566686 hasConcept C124101348 @default.
• W2991566686 hasConcept C136764020 @default.
• W2991566686 hasConcept C153180895 @default.
• W2991566686 hasConcept C154945302 @default.
• W2991566686 hasConcept C181672929 @default.
• W2991566686 hasConcept C199360897 @default.
• W2991566686 hasConcept C200518788 @default.
• W2991566686 hasConcept C205649164 @default.
• W2991566686 hasConcept C2777904410 @default.
• W2991566686 hasConcept C2779308522 @default.
• W2991566686 hasConcept C2780615836 @default.
• W2991566686 hasConcept C31972630 @default.
• W2991566686 hasConcept C41008148 @default.

• next