A complex building shape recognition method integrating vector structural features

doi:10.11947/j.AGCS.2026.20250445

Abstract

Abstract:

Building shape recognition and classification serve as pivotal components in urban spatial analysis, intelligent cartography, and 3D city modeling, playing a critical role in high-precision map construction and smart city governance. However, existing approaches still face significant challenges when dealing with the complex and diverse morphologies of buildings. Methods based on handcrafted geometric features often suffer from limited generalization capability, while rasterization-based approaches tend to introduce geometric distortions and struggle to preserve the topological structure and fine details of vectorized building outlines. To address these issues, this paper proposes an end-to-end automatic feature learning framework tailored for vectorized building contours. The method represents building outlines as corner-point graph structures to achieve a structured representation of building shapes. Furthermore, a dedicated structural feature module is integrated into a graph convolutional network (GCN) to effectively capture morphological characteristics such as concave-convex transitions and branching patterns, thereby enhancing the model's discriminative power for complex geometric structures. Experimental results demonstrate that the proposed method achieves classification accuracies of 99.20% and 99.03% on public and extended datasets, respectively, with Kappa coefficients exceeding 0.989, indicating superior performance. Error analysis reveals that misclassifications primarily occur between geometrically or topologically highly similar categories (e.g., E/U-shaped or X/O-shaped buildings), suggesting room for improvement in fine-grained structural modeling and global semantic integration. This study provides a robust, fully automatic, and generalizable solution for high-precision recognition of vectorized building contours without reliance on manual feature engineering.

Key words: building shape classification, vector outline, graph neural network, automatic feature learning, urban spatial analysis

CLC Number:

P208

Xusheng ZHOU, Yongbin TAN, Zhonghai YU, Wei WANG, Qingyun XIAO. A complex building shape recognition method integrating vector structural features[J]. Acta Geodaetica et Cartographica Sinica, 2026, 55(5): 838-849.

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形状	数量	形状	数量
E形	500	U形	500
F形	500	Y形	500
H形	500	Z形	500
I形	500	K形	507
L形	500	X形	532
O形	500	++形	500
T形	500

真实	公共	扩展	真实	公共	扩展	真实	公共	扩展
L	I	\	U	L	\	K	\	++
E	F	U	E	\	U	T	\	F
Z	L	\	E	\	U	Y	\	T
O	I	I	L	\	F	H	\	T
O	U	\	E	\	Z	L	\	U
Y	H	\	++	\	X	X	\	O
Y	U	F	I	\	O	K	\	F
U	E	\	++	\	X	L	\	I
I	L	\	X	\	O	E	\	F

模型	公共数据集				扩展数据集
模型	准确率/（%）	精确率/（%）	F₁值/（%）	Kappa系数	准确率/（%）	精确率/（%）	F₁值/（%）	Kappa系数
本文模型	99.20	99.20	99.20	0.991 1	99.03	99.05	99.03	0.989 5
GCN^[25]	92.13	92.15	92.12	0.912 6	95.11	95.18	95.12	0.947 0
GAT^[26]	93.21	93.26	93.20	0.922 8	94.58	94.63	94.59	0.939 8
GraphSAGE^[27]	94.33	94.34	94.32	0.937 0	95.77	95.87	95.78	0.954 2
GIN^[28]	94.07	94.10	94.07	0.934 1	95.72	95.78	95.73	0.953 6
SGC^[29]	91.27	91.40	91.25	0.903 0	92.81	92.83	92.80	0.922 1
GFNN^[30]	91.67	91.71	91.66	0.909 6	92.76	92.89	92.77	0.921 6
DeepGCN^[31]	93.60	93.69	93.60	0.928 9	94.70	94.71	94.68	0.942 6
DisenSemi^[32]	90.27	90.54	90.31	0.891 9	92.05	92.20	92.07	0.913 9
DiffPool^[33]	77.07	79.00	77.23	0.745 2	78.80	80.26	78.71	0.770 3
SemiGraph^[34]	91.20	91.24	91.17	0.902 2	94.70	94.78	94.71	0.942 6
GIUnet^[35]	80.87	81.02	80.85	0.787 4	85.98	86.16	85.96	0.848 2
GMCAP^[36]	87.33	87.27	87.22	0.859 3	91.08	91.16	91.08	0.903 4
GCKM^[37]	67.87	69.45	68.09	0.643 0	71.05	72.55	70.95	0.686 4
MLP	74.47	74.64	74.44	0.716 3	80.33	80.29	80.24	0.786 9

模块组合	公共数据集				扩展数据集
模块组合	准确率/（%）	精确率/（%）	F₁值/（%）	Kappa系数	准确率/（%）	精确率/（%）	F₁值/（%）	Kappa系数
本文模型	99.20	99.20	99.20	0.991 1	99.03	99.05	99.03	0.989 5
无特征映射	98.67	98.68	98.67	0.985 2	98.98	98.99	98.98	0.989 0
无节点特征	97.60	97.64	97.60	0.973 3	98.62	98.64	98.62	0.985 1
无结构特征	95.67	95.75	95.68	0.951 9	95.82	95.87	95.87	0.958 7
无平均聚合	98.73	98.74	98.73	0.985 9	98.83	98.84	98.83	0.987 3
无最大聚合	98.13	98.16	98.14	0.979 3	97.96	97.98	97.96	0.977 9
无ResNet	97.00	97.05	97.00	0.966 7	97.60	97.61	97.61	0.974 0

卷积层数	公共数据集				扩展数据集
卷积层数	准确率/（%）	精确率/（%）	F₁值/（%）	Kappa系数	准确率/（%）	精确率/（%）	F₁值/（%）	Kappa系数
1	95.60	95.63	95.60	0.951 1	96.43	96.46	96.43	0.961 3
2	97.47	97.51	97.47	0.971 9	97.81	97.82	97.81	0.976 3
3	98.80	98.81	98.80	0.986 7	98.67	98.69	98.68	0.985 6
4	99.20	99.20	99.20	0.991 1	99.03	99.05	99.03	0.989 5
5	99.13	99.14	99.13	0.990 4	98.93	98.95	98.93	0.988 4