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Local Perception-Based Intelligent Building Outline Aggregation Approach with Back Propagation Neural Network

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Abstract

With the analysis of the characteristics of back propagation neural network (BPNN) and map generalization of building outlines in both urban and suburban areas, a new approach that is based on local perception of map contexts for building aggregation has been studied. The method called the local perception-based intelligent building outline aggregation approach with BPNN technique consisted of two BPNNs. \(\text {BPNN}_{1}\) was developed to generate initial aggregated building outlines. A circular detector coupled with a set of mapping rules was designed to detect buildings from raster maps. Once trained, \(\text {BPNN}_{1}\) produced initial aggregated building outlines. Due to the existence of unwanted nodes forming small steps along the outlines, \(\text {BPNN}_{2}\) was created to remove the unwanted ones. Here, a square detector was designed and a set of refining rules formulated. Together, \(\text {BPNN}_{1}\) and \(\text {BPNN}_{2}\) intelligently delineated individual buildings or a group of buildings. The performance of the approach has been assessed and the generalized results were cartographically satisfactory.

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Acknowledgments

This research was partially funded by the National Natural Science Foundation of China under Grant No. 41071222 to the University of Electronic Science and Technology, China.

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Authors and Affiliations

  1. School of Resources and Environment, University of Electronic Science and Technology of China, No. 2006, Xiyuan Avenue, West Hi-Tech. Zone, Chengdu , 611731, Sichuan, People’s Republic of China

    Boyan Cheng, Qiang Liu & Xiaowen Li

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  1. Boyan Cheng
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  2. Qiang Liu
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  3. Xiaowen Li
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Correspondence to Boyan Cheng.

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Cheng, B., Liu, Q. & Li, X. Local Perception-Based Intelligent Building Outline Aggregation Approach with Back Propagation Neural Network. Neural Process Lett 41, 273–292 (2015). https://doi.org/10.1007/s11063-014-9345-x

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