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New Geomatics Technologies and Applications

4. Conclusion
The main goal of this research is to develop and evaluate a least squares-based approach for simplifying features with the
purpose of reducing the vertical distance between lines. To fit a line, the regression was based on least squares. As a result, the

first-order equation was employed. Since all the studied features are not single-line and are also in the form of multi-lines or
curves, the approximation of other lines was also extracted and by connecting the pieces of the simplified line, the final multi-
lines were created. Furthermore, most generalization methods use thresholds like angle, Euclidean distance, and synchronous
Euclidean distance to maintain the final form within a range of the original shape. Similar to previous studies, in this study, in
addition to approximating the lines based on the least squares, the proposed model tries to maintain the summarized output at
the threshold level of the original shape geometry. The results of the implementation on the Zerivar Lake shoreline show that the

proposed model's results are of high quality in terms of area preservation criteria, similarity of angle changes, and average
curvature, which are more similar to the original function. However, it was not possible to compare with other approaches in this
study. Therefore, it is suggested that other regression methods be considered in the field of fitting multiple lines and simplifying
features.

5. References

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[2] He, X., Zhang, X., Yan, J., “ Progressive Amalgamation of Building Clusters for Map Generalization Based on Scaling

Subgroups”, ISPRS Int. J. Geo-Inf. , 7, 116, 2018.
[3] Ying, S., Li, L., “A FRAMEWORK OF MODEL-ORIENTED MAP GENERALIZATION AND ITS IMPLEMENTATION”.
International Cartographic Conference (ICC) Durban, South Africa, 2003.
[4] Visvalingam, M., “The Visvalingam Algorithm: Metrics, Measures and Heuristics”, The Cartographic Journal, pp. 1–11,
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[5] Ma, D., Zhao, Zh., Zheng, Y., Guo, R., Zhu, W., “PolySimp: A Tool for Polygon Simplification Based on the Underlying
Scaling Hierarchy”, ISPRS Int. J. Geo-Inf., 9(10), 594, 2020.
[6] Shi, W., Cheung, Ch., “ Performance Evaluation of Line Simplification Algorithms for Vector Generalization”, The

Cartographic Journal, The World of Mapping, 43(1), 2006.
[7] White, E. R., “Assessment of Line-Generalization Algorithms Using Characteristic Points”, The American Cartographer,
12(1), 1985.
[8] Clayton, V. H., “A review of feature simplification and systematic point elimination algorithms”. NOAA technical report
NOS 112, 1985.

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