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Investigation of Improvement in Georeferencing of Urban Analog Maps using the Geometric Elements and Genetic Algorithm
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Hani Rezayan *   , Nasser Pourahmad , Javad Sadidi |
| Kharazmi Univeristy |
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Abstract: (2119 Views) |
| A large number of civil registries are available as analog maps that require georeferencing. Besides the quality of the analog maps and control points, the algorithm used for georeferencing also affects the accuracy of the map and the carried out geometrical calculations which indicates the need to study modern Georeferencing Methods. This requires the adoption of georeferencing optimization models and functions so that the required attention should be paid to the determination of the properties limit. Because of the impossibility of geometrical interpretation, we cannot give fixed values to the parameters of affine function for all images. Therefore, this study attempts to set optimized parameters for Affine function using genetic algorithm and considering the geometrical elements of cadastre maps including the area of the pieces of land, length, angle, and the stretch of their constituent lines. To study the proposed method, two scanned, registered cadastre maps related to a part of Tehran as input data and the control points acquired by GPS have been used. To calculate the accuracy and sensitivity of the method, comparisons like RMSE without applying geometrical elements, RMSE with the application of one of the geometrical elements (level, length, angle, stretch), and RMSE with the application of all geometrical elements have been made. The results of the study suggest that the proposed method with the average RMSE of 2.50205 m has a lower accuracy than the common method with the RMSE of 2.4175. However, in the proposed method, the error rate of geometrical elements of level, length, angle, and stretch has decreased by 40 percent compared to the common method which is a remarkable improvrment. |
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| Keywords: Georeference, Analog maps, Cadastre, Genetic Algorithm, Geometric Elements. |
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Full-Text [PDF 3725 kb]
(626 Downloads)
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Type of Study: Research |
Subject:
GIS
Received: 2019/11/13 | Accepted: 2021/05/5 | Published: 2021/07/22
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