Continuously Generalizing Buildings to Built-up Areas by Aggregating and Growing

Abstract : To enable smooth zooming, we propose a method to continuously generalize buildings from a given start map to a smaller-scale goal map, where there are only built-up area polygons instead of individual building polygons. We name the buildings on the start map original buildings. For an intermediate scale, we aggregate the original buildings that will become too close by adding bridges. We grow (bridged) original buildings based on buffering, and simplify the grown buildings. We take into account the shapes of the buildings both at the previous map and goal map to make sure that the buildings are always growing. The running time of our method is in O(n 3), where n is the number of edges of all the original buildings. The advantages of our method are as follows. First, the buildings grow continuously and, at the same time, are simplified. Second, right angles of buildings are preserved during growing: the merged buildings still look like buildings. Third, the distances between buildings are always larger than a specified threshold. We do a case study to show the performances of our method.
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Dongliang Peng, Guillaume Touya. Continuously Generalizing Buildings to Built-up Areas by Aggregating and Growing. 3rd ACM SIGSPATIAL Workshop on Smart Cities and Urban Analytics (UrbanGIS'17), Nov 2017, Redondo Beach, CA, United States. pp.10, ⟨10.1145/3152178.3152188⟩. ⟨hal-02095104⟩



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