Abstract
We present a 2D triangle mesh simplification model, which is able to produce high quality approximations of any original planar mesh, regardless of the shape of the original mesh. This model is applied to reduce the urban concentration of a real geographical area, with the property to maintain the original shape of the urban area. We consider the representation of an urbanized area as a 2D triangle mesh, where each node represents a house. In this context, the neural network model can be applied to simplify the network, what represents a reduction of the urban concentration. A real example is detailed with the purpose to demonstrate the ability of the model to perform the task to simplify an urban network.
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Tortosa, L., Vicent, J.F., Zamora, A., Oliver, J.L. (2009). Reducing Urban Concentration Using a Neural Network Model. In: Palmer-Brown, D., Draganova, C., Pimenidis, E., Mouratidis, H. (eds) Engineering Applications of Neural Networks. EANN 2009. Communications in Computer and Information Science, vol 43. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03969-0_14
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DOI: https://doi.org/10.1007/978-3-642-03969-0_14
Publisher Name: Springer, Berlin, Heidelberg
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