Classifying the Shapes of Buildings by Combining Distance Field Enhancement and a Convolution Neural Network

Abstract

The shape classification of building objects is crucial in fields such as map generalization and spatial queries. Recently, convolutional neural networks (CNNs) have been used to capture high-level features and classify building shape patterns based on raster representations. However, this raster-based deep learning method binarizes the areas into building and non-building zones and does not account for the distance information between these areas, potentially leading to the loss of shape feature information. To address this limitation, this study introduces a building shape classification method that incorporates distance field enhancement with a CNN. In this approach, the distance from various pixels to the building boundary is fused into the image data through distance field enhancement computation. The CNN model, specifically InceptionV3, is then employed to learn and classify building shapes using these enhanced images. The experimental results indicate that the accuracy of building shape classification improved by more than 2.5% following distance field enhancement. Notably, the classification accuracies for F-shaped and T-shaped buildings increased significantly by 4.34% and 11.76%, respectively. Moreover, the proposed method demonstrated a strong performance in classifying other building datasets, suggesting its substantial potential for enhancing shape classification in various applications.

Keywords

• building shape
• classification
• distance field
• distance field enhancement
• convolutional neural network