Generating Topographic Map Data from Classification Results

Abstract

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.

Building detection and regularisation using DSM and imagery information

“…If the polygon edges are adjusted subsequently, the topological relationships between such edges will be lost (Avbelj, ). Many of the existing procedures tend to find the dominant building direction, which then is used as the basis to adjust the other polygon edges accordingly (Arefi, ; Awrangjeb, ; Höhle, ). However, the challenge is to adjust all parameters simultaneously (Avbelj, ).…”

Deep multi-task learning for a geographically-regularized semantic segmentation of aerial images

“…Since spatial structures are learned and encoded directly in the output map, we believe our pipeline is a step towards systems yet based on machine learning, but not requiring extensive manual post-processing (e.g. local class filtering, spatial corrections, map generalization, fusion and vectorization Crommelinck et al (2016); Höhle (2017)), at the same time employing domain knowledge and data specific regularization, tailoring it to specific application domain and softening black-box effects. Specifically, the contributions of this paper are: -A detailed explanation on our multi-task CNN, building on top of a pretrained network (VGG); -A strategy to transform semantic boundaries probabilities to superpixels and hierarchical regions; -A CRF encoding the desired space-scale relationships between segments; -The combination of different energy terms accounting for multiple input-output relationships, combining bottom-up (outputs and features of the CNN) and topdown (multi-modal clues about spatial arrangement) into local and pairwise relationships.…”

Precise Monitoring of Temporal Topographic Change Detection via Unmanned Air Vehicle

“…In recent years, space-borne and airborne remote sensing technologies have been developing rapidly and monitoring of topographic displacements and deformations, depending on construction activities or natural disasters has become possible by temporal change detection analysis. With the development of airborne laser scanning (ALS) technology, threedimensional (3D) description of the topographic surface became easier by means of very high resolution (VHR), rapid achievable and accurate point clouds that could not been provided by previous remote sensing technologies (Deng et al, 2007;Darwin et al, 2014;Höhle, 2017;Manfreda et al, 2018a). Considering high surface description potential, point cloud thought was adapted to photogrammetric image processing following ALS (Teizer et al, 2005;Rosnell and Honkavaara, 2012).…”