Next Article in Journal
Assessment of Canopy Chlorophyll Content Retrieval in Maize and Soybean: Implications of Hysteresis on the Development of Generic Algorithms
Next Article in Special Issue
Fast Segmentation and Classification of Very High Resolution Remote Sensing Data Using SLIC Superpixels
Previous Article in Journal
Differences in Rate and Direction of Shifts between Phytoplankton Size Structure and Sea Surface Temperature
Previous Article in Special Issue
Scalable Bag of Subpaths Kernel for Learning on Hierarchical Image Representations and Multi-Source Remote Sensing Data Classification
Open AccessArticle

Generating Topographic Map Data from Classification Results

Department of Development and Planning, Aalborg University, 9000 Aalborg, Denmark
Academic Editors: Norman Kerle, Markus Gerke, Sébastien Lefèvre and Prasad S. Thenkabail
Remote Sens. 2017, 9(3), 224;
Received: 30 December 2016 / Accepted: 25 February 2017 / Published: 2 March 2017
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’s accuracies for the classes “impervious surface” and “building” were above 85% in the “Level 1” map of Example 1. The geometric accuracy of building corners at the “Level 2” 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 “building” and “road and parking lot”. 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. View Full-Text
Keywords: classification; machine learning; cartographic enhancement; assessment; accuracy; topographic mapping classification; machine learning; cartographic enhancement; assessment; accuracy; topographic mapping
Show Figures

Graphical abstract

MDPI and ACS Style

Höhle, J. Generating Topographic Map Data from Classification Results. Remote Sens. 2017, 9, 224.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

Back to TopTop