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M. Saati, A.Hashemi, J. Amini a Department of Geomatics Engineering, Faculty of Engineering, University of Tehran, Tehran, Iran, P.O.Box:11365-4563, Tel: +98218008841, E mail:- Saati@geomatics.ut.ac.ir Ar_m_hashemi@yahoo.com jamini@ut.ac.ir S. Sadeghian Research Institute of National Cartographic Center (NCC), Tehran, Iran, P.O.Box:13185-1684 E mail:- Sadeghian@ncc.neda.net.ir J. Noori Faculty of Civil Engineering, IAU of Estahban, Estahban, Iran, P.O.Box:74515-111, Tel: +987324225001, E mail:- jam_1350@yahoo.com ABSTRACT: In this paper, we developed an approach for DEM generation from topographic maps and height points. The method used a neural network with back propagation algorithm to determine heights for grid points based on the quad tree structure. The height points were extracted randomly from topographic maps 1:2000. These points structured under the quad tree structure and for each constructed region used a trained network to determine a height for each point on a regular grid. About 80% of points in each region are used for training the network. Results of the method were compared with the approaches in the PCI Geomatica software. 1. INTRODUCTION DEM Definition: Digital elevation model (DEM) data consist of a sampled array of regularly spaced elevation values referenced horizontally either to a Universal Transverse Mercator (UTM) projection or to a geographic coordinate system (USGS definition). The grid cells are spaced at regular intervals along south to north profiles that are ordered from west to east. DEM provides the basis in modeling and analysis of spatio-topographic information, therefore DEM reconstruction is the important task for 3D data users. Geo-spatial Information Systems (GIS) are becoming increasingly popular for visualizing spatial data. Most systems layer patterns or colors, which depict data such as soil type, roads, and the like, over a two-dimensional map. As technology continues to improve, users increasingly expect to view such data in three dimensional systems. DEM Applications: there are many applications from DEM in different science.
A (Triangulation Irregular Network) TIN can also get converted to a grid by the interpolation of points on the TIN. An advantage over the TIN is that it is easier to find the height at a given location. Only a simple calculation is required to locate the nearest grid points and interpolate between them. In this paper, we used some of conventional methods and the proposed method based on Artificial Neural Network (ANN) for preparing DEM. 2. INTERPOLATION During height points assessment, the interpolation technique used to grid DEM generation. There are two approaches for this purpose that illustrated as following sections. 2.1 Conventional interpolation In many science specially earth's science, spatial interpolation used for evaluation of physical data in continuity surface. Each used method is suitable for a type of dataset.in other hand maybe a method for a dataset can be present good result but it is no suitable for other dataset [2]. Numbers of conventional interpolation have been shown in table 1. 
In this paper the PCI Geomatica V8.1 software is supported any conventional method; we used this software for data point interpolation for DEM generation. For this purpose we need to height data that we used the digital topographic map consist of height data points and major and minor contours. Figure 1. Shows the part of a topographic map and Figure 2 shows all of DEM generated with conventional methods .  Fig.1- the part of topographic map  (a)  (b)  (c)  (d) Fig.2- reconstructed DEM with: a. nearest neighbor method, b. PCI Geomatica, c. weighted inverse distance and d. simple inverse distance
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