scholarly journals Minimizing uncertainty in complete automation of Douglas-Peucker Algorithm for geospatial mapping

2021 ◽  
Author(s):  
Rong Wang ◽  
Muhammad Shafeeque ◽  
Haowen Yan ◽  
Lu Xiaoming
Keyword(s):  
Quadratic Function ◽  
Distance Threshold ◽  
Spatial Similarity ◽  
Geographic Feature ◽  
Optimal Distance ◽  
Relationship Model ◽  
Complete Automation ◽  
Map Scale ◽  
Shape Characteristics ◽  
Geographical Feature

Abstract It is qualitatively evident that the greater the map scale change, the greater the optimal distance threshold of the Douglas-Peucker Algorithm, which is used in polyline simplification. However, no specific quantitative relationships between them are known by far, causing uncertainties in complete automation of the algorithm. To fill this gap, the current paper constructs quantitative relationships based on the spatial similarity theories of polylines. A quantitative spatial similarity relationship model was proposed and evaluated by setting two groups of control experiments and taking <C, T> as coordinates. In order to realize the automatic generalization of the polyline, we verified whether these quantitative relationships could be fitted using the same function with the same coefficients. The experiments revealed that the unary quadratic function is the best, whether the polylines were derived from different or the same geographical feature area(s). The results also show that using the same optimal distance threshold is unreasonable to simplify all polylines from different geographical feature areas. On the other hand, the same geographical feature area polylines could be simplified using the same optimal distance threshold. The uncertainties were assessed by evaluating the automated generalization results for position and geometric accuracy perspectives using polylines from the same geographic feature areas. It is demonstrated that in addition to maintaining the geographical features, the proposed model maintains the shape characteristics of polylines. Limiting the uncertainties would support the realization of completely automatic generalization of polylines and the construction of vector map geodatabases.

scholarly journals Spatial Distribution and Morphological Identification of Regional Urban Settlements Based on Road Intersections

2021 ◽  
Vol 10 (4) ◽  
pp. 201
Author(s):  
Liang Kong ◽  
Zhengwei He ◽  
Zhongsheng Chen ◽  
Mingliang Luo ◽  
Zhong Du ◽  
...  
Keyword(s):  
Urban Expansion ◽  
Vital Role ◽  
Morphological Identification ◽  
Spatial Form ◽  
Rapid Urbanization ◽  
Distance Threshold ◽  
Optimal Distance ◽  
Expansion Curve ◽  
Urban Settlements ◽  
Road Intersections

To measure and present urban size urban spatial forms, in solving problems in the rapid urbanization of China, urban territorial scope identification is essential. Although current commonly used methods can quantitatively identify urban territorial scopes to a certain extent, the results are displayed using a continuous and closed curve with medium- and low-resolution images. This makes the acquisition and interpretation of data challenging. In this paper, by extracting discretely distributed urban settlements, road intersections in OpenStreetMap (OSM), electronic maps, and urban expansion curve based on fractal thoughts have been used to present urban territorial scope and spatial form. Guangzhou, Chengdu, Nanjing, and Shijiazhuang cities were chosen as the identification targets. The results showed that the distance threshold corresponding to the principal curvature point of the urban expansion curve plays a vital role in the extraction of urban settlements. Moreover, from the analysis, the optimal distance thresholds of urban settlements in Guangzhou, Chengdu, Nanjing, and Shijiazhuang were 132 m, 204 m, 157 m, and 124 m, respectively, and the corresponding areas of urban territorial scopes were 1099.36 km2, 1076.78 km2, 803.07 km2, and 353.62 km2, respectively. These metrics are consistent with those for the built-up areas.

scholarly journals Building Plane Segmentation Based on Point Clouds

2021 ◽  
Vol 14 (1) ◽  
pp. 95
Author(s):  
Zhonghua Su ◽  
Zhenji Gao ◽  
Guiyun Zhou ◽  
Shihua Li ◽  
Lihui Song ◽  
...  
Keyword(s):  
Point Cloud ◽  
Region Growing ◽  
Point Clouds ◽  
Plane Boundary ◽  
Point Cloud Data ◽  
Distance Threshold ◽  
Cloud Data ◽  
Optimal Distance ◽  
Building Plane ◽  
Ransac Algorithm

Planes are essential features to describe the shapes of buildings. The segmentation of a plane is significant when reconstructing a building in three dimensions. However, there is a concern about the accuracy in segmenting plane from point cloud data. The objective of this paper was to develop an effective segmentation algorithm for building planes that combines the region growing algorithm with the distance algorithm based on boundary points. The method was tested on point cloud data from a cottage and pantry as scanned using a Faro Focus 3D laser range scanner and Matterport Camera, respectively. A coarse extraction of the building plane was obtained from the region growing algorithm. The coplanar points where two planes intersect were obtained from the distance algorithm. The building plane’s optimal segmentation was then obtained by combining the coarse extraction plane points and the corresponding coplanar points. The results show that the proposed method successfully segmented the plane points of the cottage and pantry. The optimal distance thresholds using the proposed method from the uncoarse extraction plane points to each plane boundary point of cottage and pantry were 0.025 m and 0.030 m, respectively. The highest correct rate and the highest error rate of the cottage’s (pantry’s) plane segmentations using the proposed method under the optimal distance threshold were 99.93% and 2.30% (98.55% and 2.44%), respectively. The F1 score value of the cottage’s and pantry’s plane segmentations using the proposed method under the optimal distance threshold reached 97.56% and 95.75%, respectively. This method can segment different objects on the same plane, while the random sample consensus (RANSAC) algorithm causes the plane to become over-segmented. The proposed method can also extract the coplanar points at the intersection of two planes, which cannot be separated using the region growing algorithm. Although the RANSAC-RG method combining the RANSAC algorithm and the region growing algorithm can optimize the segmentation results of the RANSAC (region growing) algorithm and has little difference in segmentation effect (especially for cottage data) with the proposed method, the method still loses coplanar points at some intersection of the two planes.

Automated high-resolution microscopy: the approaches so far

1987 ◽  
Vol 45 ◽  
pp. 58-61
Author(s):  
W. O. Saxton
Keyword(s):  
High Resolution ◽  
Early Stage ◽  
Automatic Recording ◽  
Microprocessor Control ◽  
Alternative Systems ◽  
Electron Image ◽  
Complete Automation ◽  
Analysis System ◽  
High Resolution Microscopy

Recent commercial microscopes with internal microprocessor control of all major functions have already demonstrated some of the benefits anticipated from such systems, such as continuous magnification, rotation-free diffraction and magnification, automatic recording of mutually registered focal series, and fewer control knobs. Complete automation of the focusing, stigmating and alignment of a high resolution microscope, allowing focal series to be recorded at preselected focus values as well, is still imminent rather than accomplished, however; some kind of image pick-up and analysis system, fed with the electron image via a TV camera, is clearly essential for this, but several alternative systems and algorithms are still being explored. This paper reviews the options critically in turn, and stresses the need to consider alignment and focusing at an early stage, and not merely as an optional extension to a basic proposal.

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