数据与认知双驱动的建筑物群制图综合结果与尺度一致性识别

doi:10.11947/j.AGCS.2025.20240490

摘要/Abstract

摘要：

制图综合结果与综合尺度的一致性是制图综合结果质量评价的重要内容。评价过程涉及数量特征、结构特征、认知特征等多维度因素。传统方法在多种指标组合评价时存在量化指标难以确定的问题，且不易融合空间认知等领域知识。基于此，本文提出一种基于空域图卷积神经网络的建筑物群制图综合结果与尺度一致性的识别模型。该模型采用数据驱动与认知驱动相结合的策略，从整体结构、局部结构和个体特征3个空间认知层次度量建筑物群综合前后的特征变化，并利用多尺度综合成果数据进行训练。试验结果表明，本文模型能有效识别制图综合结果与目标尺度的一致性。

关键词: 建筑物群, 制图综合, 深度学习, 质量评价, 空间结构

Abstract:

The consistency between cartographic generalization results and their target scales constitutes a critical aspect of generalization quality evaluation. This process involves multidimensional factors including quantitative features, structural characteristics, and cognitive attributes. Traditional methods face difficulties in determining quantitative metrics when combining multiple evaluation indicators and encounter challenges in integrating domain knowledge such as spatial cognition. To address these limitations, this paper proposes a dilated graph convolutional network (DGCNN)-based recognition model for assessing scale consistency between building groups and their generalization results. The model adopts a dual data-driven and cognition-driven strategy to measure feature changes before and after generalization across three spatial-cognitive levels: global structure, local structure, and individual characteristics. It leverages multi-scale generalized datasets for training. Experimental results demonstrate that the proposed model effectively recognizes consistency between cartographic generalization outcomes and target scales.

Key words: building group, cartographic generalization, deep learning, quality assessment, spatial structure

中图分类号:

P208

孟妮娜, 李凤梅, 周校东. 数据与认知双驱动的建筑物群制图综合结果与尺度一致性识别[J]. 测绘学报, 2025, 54(7): 1318-1331.

Nina MENG, Fengmei LI, Xiaodong ZHOU. Data and cognition dual-driven building group generalization results and scale consistency assessment[J]. Acta Geodaetica et Cartographica Sinica, 2025, 54(7): 1318-1331.

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分类	特征名称	度量方法	形式化表示或计算公式	说明
个体特征	形状	紧密度	Tig（m）=4πArea（m）/P²（m）	Area（m）和P（m）分别为建筑物m的面积和周长
	尺寸	建筑物面积	Area（m）	Area（m）和P（m）分别为建筑物m的面积和周长
	位置	建筑物几何中心		P（x_i，y_i）为建筑物顶点坐标
局部特征	方向	建筑物最小外接矩形的最长边与x轴正轴之间的夹角	Direct（m）=Angle（SMBR（m），x）	SMBR（m）为建筑物最小外接矩形的最长边
	方向关系	相邻建筑物的Voronoi方向关系	Dir＿voronoi（m_i，m_j）	m_i、m_j表示相邻的两个建筑物
	距离关系	相邻建筑物的邻近距离	Distance（m_i，m_j）
	中线长	邻近建筑物形心连线中点与建筑物群中心的距离	L（m_i，m_j）
	中线夹角	两个相邻中线构成的夹角	α（L_p，L_q）	L_p、L_q为相邻的中线
	群轮廓	建筑物群凸壳的面积	Area（group）	V（x_i，y_i）为建筑物群凸壳的顶点
	群中心	建筑物群凸壳的几何中心		V（x_i，y_i）为建筑物群凸壳的顶点
整体特征	群平均面积	建筑物群的平均面积		m_i表示群内的每个建筑物，n表示建筑物数量
	群平均周长	建筑物群的平均周长
	群密度	群内建筑物数量与群凸壳面积的比值	Density（group）=n/Area（group）

分类	特征名称	表示	计算公式	说明
个体变化特征	形状变化	C_s	C_s=｜Tig（V）－Tig（O）｜	V表示综合后的建筑物，O表示综合前的建筑物
	尺寸变化	C_a	C_a=｜Area（V）－Area（O）｜
	位置变化	C_p	C_p=｜Loacat（V）－Loacat（O）｜
	方向变化	C_d	C_d=｜Dierct（V）－Direct（O）｜，C_d∈[0°，180°]
局部变化特征	方向关系变化	L_v	L_v=｜Dir＿voronoi（V_i，V_j）－Dir＿voronoi（O_i，O_j）｜，L_v∈[0°，180°]	V_i、V_j表示综合后相邻的建筑物，O_i、O_j表示综合前相邻的建筑物
	距离关系变化	L_d	L_d=｜Distance（V_i，V_j）－Distance（O_i，O_j）｜	V_i、V_j表示综合后相邻的建筑物，O_i、O_j表示综合前相邻的建筑物
	初始距离	L_o	Distance（O_i，O_j）	VL_p、VL_q表示综合后相邻的中线，OL_p、OL_q表示综合前相邻的中线
	中线长变化	L_l	L_l=｜L（V_i，V_j）－L（O_i，O_j）｜
	相邻中线夹角变化	L_α	L_α=｜α（VL_p，VL_q）－α（OL_p，OL_q）｜
整体变化特征	群轮廓变化	G_l	G_l=｜Area（group_v）－Area（group_O）｜	group_v表示综合后的建筑物群组，group_O表示综合前的建筑物群组
	群中心变化	G_c	G_c=｜Center（group_v）－Center（group_O）｜
	群平均面积变化	G_a	G_a=｜Area＿mean（group_v）－Area＿mean（group_O）｜
	群平均周长变化	G_p	G_p=｜Perim＿mean（group_v）－Perim＿mean（group_O）｜
	群分布密度变化	G_d	G_d=｜Density（group_v）－Density（group_O）｜

名称	配置信息
操作系统	Ubuntu 20.04子系统、Windows 11系统
开发语言	Python 3.8.10
框架	Py Torch 1.13.1
GPU	NVIDIA GeForce GTX 1050Ti
CPU	AMD Ryzen 5 2600

建筑物群匹配对的类型		One-hot编码
综合前	综合后	One-hot编码
1∶10 000	1∶20 000	[1，0，0]
1∶10 000	1∶25 000	[0，1，0]
1∶10 000	1∶30 000	[0，0，1]

测试集样本	预测：[1，0，0]	预测：[0，1，0]	预测：[0，0，1]
标签：[1，0，0]	65	3	4
标签：[0，1，0]	4	65	2
标签：[0，0，1]	5	4	57