Angela Mayhua, Erick Gomez Nieto, Jeffrey Heer, Jorge Poco
{"title":"Extracting Visual Encodings from Map Chart Images with Color-Encoded Scalar Values","authors":"Angela Mayhua, Erick Gomez Nieto, Jeffrey Heer, Jorge Poco","doi":"10.1109/SIBGRAPI.2018.00025","DOIUrl":null,"url":null,"abstract":"Map charts are used in diverse domains to show geographic data (e.g., climate research, oceanography, business analysis, etc.). These charts can be found in news articles, scientific papers, and on the Web. However, many map charts are available only as bitmap images, hindering machine interpretation of the visualized data for indexing and reuse. We propose a pipeline to recover both the visual encodings and underlying data from bitmap images of geographic maps with color-encoded scalar values. We evaluate our results using map images from scientific documents, achieving high accuracy along each step of our proposal. In addition, we present two applications: data extraction and map reprojection to enable improved visual representations of map charts.","PeriodicalId":208985,"journal":{"name":"2018 31st SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 31st SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SIBGRAPI.2018.00025","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 6
Abstract
Map charts are used in diverse domains to show geographic data (e.g., climate research, oceanography, business analysis, etc.). These charts can be found in news articles, scientific papers, and on the Web. However, many map charts are available only as bitmap images, hindering machine interpretation of the visualized data for indexing and reuse. We propose a pipeline to recover both the visual encodings and underlying data from bitmap images of geographic maps with color-encoded scalar values. We evaluate our results using map images from scientific documents, achieving high accuracy along each step of our proposal. In addition, we present two applications: data extraction and map reprojection to enable improved visual representations of map charts.