Abstract
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.
Highlights
MethodsMany classification methods have been developed in the past
Big progress has been achieved in the classification of aerial and satellite images by means of machine learning methods
The recent progress in sensors and machine learning methods will support the generation of topographic map data of high thematic and geometric accuracy
Summary
Many classification methods have been developed in the past. Besides the generation of land cover maps with several classes, the extraction of single objects is subject of many studies. The extraction of building boundaries using high resolution images and lidar data has recently been published in [8]. Lidar data are used to produce a coarse boundary, which is refined by means of edges extracted from stereo images. Precise 3D boundaries of buildings are obtained by this methodology. In [9], 2D building outlines are generated by means of lidar data using elevations and intensities
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