Abstract
For many years, the adaptation of the historical maps to those used today has been one of the fields of study of cartography and geodesy. In this process, the first problem is that the coordinate system in historical maps differs from the modern coordinate systems. For such problems, many mathematical methods have been developed and implemented for the georeferencing process. The second problem, more complicated than the first, is how to resolve the situation when the mapped area was drawn on more than one sheet of paper and without a scale. This study focuses on the map drawn in 1521 by the Ottoman admiral, Piri Reis, of the Nile, which gave life to Egypt, the cradle of civilizations. The Nile was drawn from Cairo to Rosetta on four map sheets in five parts without a coordinate reference and scale factor. The method that has performed well in solving problems that cannot be expressed mathematically is the artificial neural network (ANN) technique which is widely used in solving engineering problems. The current study reports on the use of the Back Propagation Artificial Neural Network (BPANN). The map obtained by BPANN was compared with a current map of the Nile, and the result was that the ANN technique can also be used in the absence of a scale.
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References
Affek A (2013) Georeferencing of historical maps using gis, as exemplified by the austrian military surveys of Galicia. Geogr Polon 86:375–390
Baiocchi V, Lelo K (2005) Georeferencing the historical maps of rome between the seventeenth and eighteenth centuries. In: CIPA XX International symposium, Torino, Italy
Beale MH, Hagan MT, Demuth HB (2010) Neural network toolbox 7 users guide. The MathWorks Inc, Natick
Bishop C (1995) Neural networks for pattern recognition. Oxford University Press, Oxford
Brigante R, Radicioni F (2014) Georeferencing of historical maps: gis technology for urban analysis. Geogr Tech 9(1):10–19
Bromberg KD, Bertness MD (2005) Reconstructing new england salt marsh losses using historical maps. Estuaries Coasts 28(6):823–832
Fausett L (1994) Fundamentals of neural networks. Prentice Hall, Upper Saddle River
Graupe D (2007) Principles of artifical neural networks. World Scientific Publishing, Singapore
Gullu M (2010) Coordinate transformation by radial basis function neural network. Sci Res Essays 5(20):3141–3146
Gullu M, Yılmaz I (2010) Outlier detection for geodetic nets using ADALINE learning algorithm. Sci Res Essays 5(5):440–447
Gullu M, Yılmaz I, Yılmaz M, Turgut B (2011) An alternative method for estimating densification point velocity based on back propagation artificial neural networks. Stud Geophys Geod 55(1):73–86
Haykin S (1999) Neural networks: a comprehensive foundation. Prentice Hall, Upper Saddle River
Korte M, Constable C (2011) Improving geomagnetic field reconstructions for 0–3 ka. Phys Earth Planet Inter 188(3–4):247–259
Liu Y, Starzyk JA (2008) Optimized approximation algorithm in neural networks without overfitting. IEEE Trans Neural Netw 19(6):983–995
McIntosh GC (2000) The piri reis map of 1513. The University of Georgia Press, Athens, London
Novak D, Milinaric D, Lapaine M (2004) Description of Croatian islands and harbours from Dubrovnik to Umag in Istria by Piri Reis in the Kitab-ı Bahriye. In: Proceedings of the international Piri Reis symposium, Istanbul, September 27–29, pp. 64–91
Podobnikar T (2009) Georeferencing and quality assessment of josephine survey maps for the mountainous region in the triglav national park. Acta Geod Geophys Hung 44(1):49–66
Ripley BD (1996) Pattern recognition and neural networks. Cambridge University Press, Cambridge
Soucek S (1992) Islamic charting in the Mediterranean. In: Harley JB, Woodward D (eds) The history of cartography, vol. 2, Book 1. Cartography in the Traditional Islamic and South Asian Societies. University of Chicago Press, Chicago
Timar G, Szaleky B, Molnar G, Ferencz C, Kern A, Galambos C, Gercsak L, Zentai L (2008) Combination of historical maps and satellite images of the banat region re-appearance of an old wetland area. Global Planet Change 62:29–38
Valach F, Hejda P, Bochnick J (2007) Geoeffectiveness of XRA events associated with RSP II and/or RSP IV estimated using the artificial neural network. Stud Geophys Geod 51(4):551–562
Yılmaz I, Gullu M (2012) Georeferencing of historical maps using back propagation artificial neural network. Experimental Techniques 36(5):15–19
Yılmaz M, Gullu M (2014) A comparative study for the estimation of geodetic point velocity by artificial neural networks. J Earth Syst Sci 123(4):791–808
Yılmaz I, Gullu M, Yılmaz M, Dereli MA (2010) Compass roses on the book of navigation (Kitab-ı Bahriye) declination data source for geomagnetic field models. Phys Earth Planet Inter 182(3):170–174
Yılmaz M, Turgut B, Gullu M, Yılmaz I (2017) Application of artificial neural networks to height transformation. Teh Vjesn 24(2):443–448
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Gullu, M., Narin, O.G. Georeferencing of the Nile River in Piri Reis 1521 map, Using Artificial Neural Network Method. Acta Geod Geophys 54, 387–401 (2019). https://doi.org/10.1007/s40328-019-00255-7
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DOI: https://doi.org/10.1007/s40328-019-00255-7