{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T14:00:57Z","timestamp":1787061657640,"version":"build-2736575974"},"reference-count":25,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2017,3,2]],"date-time":"2017-03-02T00:00:00Z","timestamp":1488412800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The use of classification results as topographic map data requires cartographic enhancement and checking of the geometric accuracy. Urban areas are of special interest. The conversion of the classification result into topographic map data of high thematic and geometric quality is subject of this contribution. After reviewing the existing literature on this topic, a methodology is presented. The extraction of point clouds belonging to line segments is solved by the Hough transform. The mathematics for deriving polygons of orthogonal, parallel and general line segments by least squares adjustment is presented. A unique solution for polylines, where the Hough parameters are optimized, is also given. By means of two data sets land cover maps of six classes were produced and then enhanced by the proposed method. The classification used the decision tree method applying a variety of attributes including object heights derived from imagery. The cartographic enhancement is carried out with two different levels of quality. The user\u2019s accuracies for the classes \u201cimpervious surface\u201d and \u201cbuilding\u201d were above 85% in the \u201cLevel 1\u201d map of Example 1. The geometric accuracy of building corners at the \u201cLevel 2\u201d maps is assessed by means of reference data derived from ortho-images. The obtained root mean square errors (RMSE) of the generated coordinates (x, y) were RMSEx = 1.2 m and RMSEy = 0.7 m (Example 1) and RMSEx = 0.8 m and RMSEy = 1.0 m (Example 2) using 31 and 62 check points, respectively. All processing for Level 1 (raster data) could be carried out with a high degree of automation. Level 2 maps (vector data) were compiled for the classes \u201cbuilding\u201d and \u201croad and parking lot\u201d. For urban areas with numerous classes and of large size, universal algorithms are necessary to produce vector data fully automatically. The recent progress in sensors and machine learning methods will support the generation of topographic map data of high thematic and geometric accuracy.<\/jats:p>","DOI":"10.3390\/rs9030224","type":"journal-article","created":{"date-parts":[[2017,3,2]],"date-time":"2017-03-02T10:31:17Z","timestamp":1488450677000},"page":"224","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Generating Topographic Map Data from Classification Results"],"prefix":"10.3390","volume":"9","author":[{"given":"Joachim","family":"H\u00f6hle","sequence":"first","affiliation":[{"name":"Department of Development and Planning, Aalborg University, 9000 Aalborg, Denmark"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2017,3,2]]},"reference":[{"key":"ref_1","first-page":"20","article-title":"The status of the topographic mapping in the world\u2014A UNGGIM_ISPRS Project 2012-2015","volume":"141","author":"Konecny","year":"2016","journal-title":"Z. 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