Grid patterns recognition in urban road networks using multi-level mesh features and VAE-PNN model

doi:10.11947/j.AGCS.2024.20230026

Abstract

Abstract: As one of explicit patterns widely existing in urban road network, grid patterns contain a large amount of abundant information about urban spatial pattern. Recognizing road grid patterns is also the key prerequisite for realizing automatic and intelligent map generalization. As existing methods of grid pattern recognition seldom consider multi-level mesh features and may be sensitive to the diversity of training samples, this paper proposes a novel approach for urban road grid pattern recognition based on multi-level mesh features and VAE-PNN model. Firstly, the original road network data are simplified by sophisticated DP algorithm. Then, we design a set of multi-level mesh features to measure grid patterns, including internal orthogonal function, grid shape descriptions and neighborhood correlation indicators. After that, variational auto-encoder (VAE) is used to enhance the diversity and size of training samples. Finally, probabilistic neural network (PNN) model is adopted to identify road grid patterns. The experimental results show that considering multi-level mesh features can help to identify different grid types and various grid patterns and demonstrate that introducing VAE model achieves better performance on grid pattern recognition than not.

Key words: grid patterns recognition, multi-level mesh feature, variational auto encoder, probabilistic neural network

CLC Number:

P208

ZHANG Yunfei, QIU Zehang. Grid patterns recognition in urban road networks using multi-level mesh features and VAE-PNN model[J]. Acta Geodaetica et Cartographica Sinica, 2024, 53(1): 189-198.

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