融合骨架与轮廓特征的建筑物形状编码模型

doi:10.11947/j.AGCS.2026.20260175

测绘学报 ›› 2026, Vol. 55 ›› Issue (9): 1679-1694.doi: 10.11947/j.AGCS.2026.20260175

融合骨架与轮廓特征的建筑物形状编码模型

鲁谢春¹^,²^,³(), 侯培阳¹, 马凯¹^,⁴, 陈占龙²^,³^,⁵, 徐永洋⁵()

^1.三峡大学计算机与信息学院，湖北　宜昌　443002
^2.自然资源信息管理与数字孪生工程软件教育部工程研究中心，湖北　武汉　430074
^3.中国地质大学（武汉）智能地学信息处理湖北省重点实验室，湖北　武汉　430074
^4.三峡大学三峡数智研究院，湖北　宜昌　443002
^5.中国地质大学（武汉）计算机学院，湖北　武汉　430078

收稿日期:2026-05-11 修回日期:2026-08-31 出版日期:2026-10-28 发布日期:2026-10-28
通讯作者: 徐永洋 E-mail:luxiechun@ctgu.edu.cn;yongyangxu@cug.edu.cn
作者简介:鲁谢春（1992—），男，讲师，研究方向为智能化地图制图、地图综合、时空大数据分析处理。　E-mail：luxiechun@ctgu.edu.cn
基金资助:
国家自然科学基金(42601756; 42471475; 42371454);湖北省自然科学基金(2025AFB107);中国地质大学（武汉）计算机学院科研平台联合开放基金(PTLH2024-B-09)

Building shape encoding model integrating skeleton and contour features

Xiechun Lu¹^,²^,³(), Peiyang Hou¹, Kai Ma¹^,⁴, Zhanlong Chen²^,³^,⁵, Yongyang Xu⁵()

^1.College of Computer and Information Technology, China Three Gorges University, Yichang 443002, China
^2.Engineering Research Center of Natural Resource Information Management and Digital Twin Engineering Software, Ministry of Education, Wuhan 430074, China
^3.Hubei Key Laboratory of Intelligent Geo-Information Processing, China University of Geosciences, Wuhan 430074, China
^4.Three Gorges Digital Intelligence Institute, China Three Gorges University, Yichang 443002, China
^5.School of Computer Science, China University of Geoscience, Wuhan 430078, China

Received:2026-05-11 Revised:2026-08-31 Online:2026-10-28 Published:2026-10-28
Contact: Yongyang Xu E-mail:luxiechun@ctgu.edu.cn;yongyangxu@cug.edu.cn
About author:Lu Xiechun (1992—), male, PhD, lecturer, majors in intelligent cartography, cartographic generalization, spatio-temporal big data analysis. E-mail: luxiechun@ctgu.edu.cn
Supported by:
The National Natural Science Foundation of China(42601756; 42471475; 42371454);The Natural Science Foundation of Hubei Province(2025AFB107);The Joint Open Fund of the Research Platforms of School of Computer Science, China University of Geosciences, Wuhan(PTLH2024-B-09)

摘要/Abstract

摘要：

建筑物形状在建筑物制图综合中起着重要作用，对建筑物要素选取、简化、形状保持及多尺度表达具有重要意义。现有方法多基于建筑物局部或区域特征进行建模，虽然能够刻画轮廓的局部几何变化，但缺乏对整体结构的有效约束，难以兼顾建筑物轮廓细节与全局形态特征，从而限制了对复杂建筑形态的整体表征能力。本文提出了一种融合建筑物骨架与轮廓特征的无监督形状表征学习网络模型SCSR-Net：通过构建骨架点集自编码器学习反映建筑物全局结构的骨架特征，结合图卷积自编码器学习反映建筑物局部细节的轮廓特征，最终通过联合编码与特征聚合机制，实现骨架与轮廓特征的协同表征，从而获取兼具建筑形状骨架与轮廓特征的形状编码。试验结果表明，SCSR-Net能够有效整合全局与局部信息，弥补单一特征编码的局限性，并在形状匹配与检索任务中取得优秀的性能表现，验证了本文模型的有效性。

关键词: 建筑物形状表征, 特征融合, 地图制图综合, 无监督学习

Abstract:

Building shape plays an important role in the cartographic generalization of buildings, and is essential for building feature selection, simplification, shape preservation, and multi-scale representation. Existing methods mostly perform modeling based on local or regional features of buildings. Although they can characterize local geometric variations in building contours, they lack effective constraints on global structure, making it difficult to simultaneously preserve contour details and global morphological characteristics. This limitation hinders the overall representation capability regarding complex building forms. This paper proposes an unsupervised shape representation learning network, skeleton-contour shape representation network model (SCSR-Net), which integrates building skeleton and contour features. By constructing a skeleton point-set autoencoder to learn skeleton features reflecting the global structure of buildings and a graph convolutional autoencoder to learn contour features reflecting local building details, the model achieves joint learning and fusion through a joint encoding and feature aggregation mechanism, which yields shape encodings that capture both skeleton and contour characteristics of building shapes. Experimental results demonstrate that the SCSR-Net effectively integrates global and local information, overcomes the limitations of single-feature encoding, and achieves excellent performance in shape matching and retrieval tasks, thereby validating the effectiveness of the proposed model.

Key words: building shape representation, feature fusion, cartographic generalization, unsupervised learning

中图分类号:

P208

鲁谢春, 侯培阳, 马凯, 陈占龙, 徐永洋. 融合骨架与轮廓特征的建筑物形状编码模型[J]. 测绘学报, 2026, 55(9): 1679-1694.

Xiechun Lu, Peiyang Hou, Kai Ma, Zhanlong Chen, Yongyang Xu. Building shape encoding model integrating skeleton and contour features[J]. Acta Geodaetica et Cartographica Sinica, 2026, 55(9): 1679-1694.

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链接本文: http://xb.chinasmp.com/CN/10.11947/j.AGCS.2026.20260175

http://xb.chinasmp.com/CN/Y2026/V55/I9/1679

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参考文献 39

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特征	指标	计算公式	含义
位置特征	横坐标		其中c_x表示建筑物中心点的横坐标；r表示建筑物最大空间跨度
位置特征	纵坐标		其中c_y表示建筑物中心点的纵坐标
结构特征	节点的度deg_i	-	骨架线顶点的度
轮廓关联特征	最近距离特征		其中ƏB表示轮廓点集
轮廓关联特征	次近距离特征		其中q′表示最近轮廓点

类型	E形	F形	H形	I形	L形	O形	T形	U形	Y形	Z形
模板
数据

形状匹配方法	FT	ST	DCG
转向函数（TF）	0.417	0.526	0.867
傅里叶描述符（FD）	0.726	0.894	0.965
图编辑距离（GED）	0.865	0.977	0.989
图卷积自编码器（GCAE）	0.868	0.934	0.986
LineStringNet模型	0.986	0.997	0.998
深度聚类形状自编码器（DCSAE）	0.923	0.982	0.995
SCSR-Net	0.968	0.995	0.998

融合方法	FT	ST	DCG	匹配准确率/（%）
门控融合（未映射）	0.835	0.966	0.985	91.12
注意力机制（未映射）	0.816	0.920	0.980	91.98
加权拼接（未映射）	0.891	0.982	0.989	94.34
门控融合（映射）	0.871	0.969	0.986	93.22
注意力机制（映射）	0.928	0.993	0.993	94.66
SCSR-Net（映射）	0.968	0.995	0.998	99.18

权重设计	α₁	α₂	α₃	FT	ST	DCG	匹配准确率/（%）
SCSR-Net	动态调整	动态调整	动态调整	0.968	0.995	0.998	99.18
固定权重组1	0.8	0.10	0.10	0.759	0.961	0.953	74.72
固定权重组2	0.7	0.15	0.15	0.738	0.892	0.950	71.78
固定权重组3	0.6	0.20	0.20	0.661	0.838	0.939	70.10
固定权重组4	0.4	0.30	0.30	0.582	0.740	0.911	58.12
固定权重组5	0.3	0.35	0.35	0.536	0.734	0.899	54.60

融合骨架与轮廓特征的建筑物形状编码模型

Building shape encoding model integrating skeleton and contour features

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参考文献 39

相关文章 15

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Metrics

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轮廓采样点数/骨架加密点数	FT	ST	DCG	匹配准确率/（%）
32/16	0.771	0.911	0.97	80.06
32/32	0.748	0.944	0.972	76.24
32/64	0.753	0.900	0.967	78.96
64/16	0.951	0.976	0.995	96.36
64/32	0.968	0.995	0.998	99.18
64/64	0.953	0.995	0.997	97.42
128/16	0.912	0.985	0.994	97.08
128/32	0.951	0.993	0.997	98.80
128/64	0.943	0.997	0.997	97.78

阶数	FT	ST	DCG	匹配准确率/（%）
一阶	0.847	0.960	0.986	95.12
二阶	0.968	0.995	0.998	99.18
三阶	0.855	0.954	0.987	91.56
四阶	0.827	0.938	0.982	89.24

λ₁	λ₂	匹配准确率/（%）
λ₁	λ₂	Y形	E形	F形	U形	T形	L形	I形	O形	H形	Z形
0.1	0.9	95.6	92.0	98.2	98.6	63.8	98.8	99.4	99.6	96.8	98.6
0.2	0.8	95.8	94.0	99.2	99.6	65.0	99.0	99.6	99.6	97.0	99.4
0.3	0.7	96.2	96.2	99.6	99.6	68.2	99.4	99.8	99.6	97.8	100.0
0.4	0.6	97.8	96.4	99.6	99.6	92.4	99.0	99.8	99.6	98.6	100.0
0.5	0.5	98.0	97.8	99.8	99.4	99.4	98.8	99.8	100.0	98.8	100.0
0.6	0.4	98.0	99.4	99.8	99.0	99.2	98.8	100.0	100.0	99.2	96.0
0.7	0.3	97.8	99.8	99.4	96.6	98.2	98.8	100.0	100.0	98.8	79.6
0.8	0.2	98.0	99.8	99.0	91.8	97.8	99.0	100.0	100.0	99.2	77.2
0.9	0.1	97.6	99.6	98.8	94.6	93.8	99.0	100.0	100.0	99.2	72.0

δ/K	FT	ST	DCG
0.001/5	0.873	0.957	0.986
0.001/10	0.892	0.962	0.989
0.001/20	0.861	0.951	0.984
0.0005/5	0.953	0.993	0.997
0.0005/10	0.968	0.995	0.998
0.0005/20	0.948	0.982	0.995
0.0001/5	0.927	0.980	0.994
0.0001/10	0.934	0.984	0.995
0.0001/20	0.916	0.979	0.993

缩放系数	Y形	L形	E形	U形	I形	F形	O形	T形	H形	Z形
1∶0.5	0.996 1	0.995 7	0.998 9	0.997 7	0.999 0	0.997 5	0.999 7	0.997 5	0.999 4	0.997 7
1∶2	0.990 6	0.999 0	0.998 6	0.998 1	0.999 5	0.996 9	0.999 6	0.999 0	0.999 2	0.998 2
1∶5	0.997 6	0.999 4	0.996 1	0.996 2	0.998 9	0.995 7	0.999 8	0.998 7	0.998 4	0.997 3
1∶10	0.998 7	0.999 7	0.995 1	0.996 3	0.999 1	0.998 3	0.999 7	0.997 6	0.998 8	0.997 2
1∶20	0.997 6	0.995 8	0.997 2	0.997 3	0.998 8	0.998 1	0.999 8	0.997 9	0.998 3	0.997 8

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