A Transformer model for building polygon simplification in map generalization

doi:10.11947/j.AGCS.2026.20250262

Abstract

Abstract:

Addressing the issues in building polygon simplification within map generalization—such as reliance on manual rules, low automation, and difficulty in reusing existing simplification results—this paper proposes a building polygon simplification model based on the Transformer mechanism. The model begins by mapping building polygons into a grid space of a certain scale, representing coordinate strings of the polygons as grid sequences. This process allows the acquisition of Token sequences before and after simplification, thereby constructing paired datasets of building polygon simplification samples. Utilizing the Transformer architecture, the model learns dependencies between point sequences through its masked self-attention mechanism, ultimately generating new simplified polygons point by point to achieve building polygon simplification. During training, the model employs structured sample data and incorporates a cross-entropy loss function that ignores specific indices to enhance simplification quality. The experimental design consists of two parts: a main experiment and a generalization validation. The main experiment, based on the Los Angeles 1∶2000 building dataset, encodes polygons using three grid sizes—0.2, 0.3, and 0.5 mm—to achieve simplifications at target scales of 1∶5000 and 1∶10 000. Results indicate that the model performs optimally with a grid size of 0.3 mm, achieving a consistency rate exceeding 92.0% with manual annotations on the validation set. A generalization experiment on building polygon data from parts of Beijing further verified the model's transferability. Comparative analysis with an LSTM model, under similar parameter scales, showed that the LSTM model failed to converge effectively and could not produce usable results. This study confirms the potential of Transformer in handling spatial geometric sequence tasks and demonstrates its ability to effectively reuse existing simplification samples. The proposed approach offers a new, engineering-practical pathway for intelligent building polygon simplification.

Key words: map generalization, buildings polygon simplification, Tokenization, Transformer model, context engineering

CLC Number:

P208

Pengcheng LIU, Xiaoqiang CHENG, Tianyuan XIAO, Min YANG, Tinghua AI. A Transformer model for building polygon simplification in map generalization[J]. Acta Geodaetica et Cartographica Sinica, 2026, 55(1): 124-137.

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References 27

[1]	王家耀, 孙群, 王光霞, 等. 地图学原理与方法[M]. 2版. 北京: 科学出版社, 2014.
	WANG Jiayao, SUN Qun, WANG Guangxia, et al. Principle and method of cartography[M]. 2nd ed. Beijing: Science Press, 2014.
[2]	WANG Zeshen, LEE D. Building simplification based on pattern recognition and shape analysis[C]//Proceedings of the 9th International Symposium on Spatial Data Handling. Beijing: [s.n.], 2000: 58-72.
[3]	尹烁, 闫小明, 晏雄锋. 基于特征边重构的建筑物化简方法[J]. 测绘学报, 2020, 49(6): 703-710. DOI: . doi: 10.11947/j.AGCS.2020.20190299
	YIN Shuo, YAN Xiaoming, YAN Xiongfeng. Simplification method of building polygon based on feature edges reconstruction[J]. Acta Geodaetica et Cartographica Sinica, 2020, 49(6): 703-710. DOI: . doi: 10.11947/j.AGCS.2020.20190299
[4]	SESTER M. Generalization based on least squares adjustment[C]//Proceedings of 2000 International Archives of Photogrammetry and Remote Sensing. Amsterdam: International Society for Photogrammetry & Remote Sensing, 2000: 931G938.
[5]	刘鹏程, 艾廷华, 邓吉芳. 基于最小二乘的建筑物多边形的化简与直角化[J]. 中国矿业大学学报, 2008, 37(5): 699-704.
	LIU Pengcheng, AI Tinghua, DENG Jifang. Simplification and rectangularity of building polygon based on least squares adjustment[J]. Journal of China University of Mining and Technology, 2008, 37(5): 699-704.
[6]	许文帅, 龙毅, 周侗, 等. 基于邻近四点法的建筑物多边形化简[J]. 测绘学报, 2013, 42(6): 929-936.
	XU Wenshuai, LONG Yi, ZHOU Tong, et al. Simplification of building polygon based on adjacent four point method[J]. Acta Geodaetica et Cartographica Sinica, 2013, 42(6): 929-936.
[7]	李俐俐, 李成名, 卢小平, 等. 顾及邻近五点的建筑物多边形化简方法[J]. 测绘通报, 2019(3): 41-45.
	LI Lili, LI Chengming, LU Xiaoping, et al. Simplification of building polygon based on adjacent five-point method[J]. Bulletin of surveying and mapping, 2019(3): 41-45.
[8]	RAINSFORD D, MACKANESS W. Template matching in support of generalization of rural buildings[M]//RICHARDSON D E, VAN OOSTEROM P. Advances in spatial data handling. Berlin: Springer, 2002: 137-151.
[9]	刘鹏程, 艾廷华, 胡晋山, 等. 基于原型模板形状匹配的建筑多边形化简[J]. 武汉大学学报(信息科学版), 2010, 35(11): 1369-1371.
	LIU Pengcheng, AI Tinghua, HU Jinshan, et al. Building-polygon simplification based on shape matching of prototype template[J]. Geomatics and Information Science of Wuhan University, 2010, 35(11): 1369-1372.
[10]	AI Tinghua, CHENG Xiaoqiang, LIU Pengcheng, et al. A shape analysis and template matching of building features by the Fourier transform method[J]. Computers, Environment and Urban Systems, 2013, 41: 219-233.
[11]	YAN X, AI T, ZHANG X. Template matching and simplification method for building features based on shape cognition[J]. International Journal of Geo-Information, 2017, 6(8): 250.
[12]	晏雄锋, 艾廷华, 杨敏. 居民地要素化简的形状识别与模板匹配方法[J]. 测绘学报, 2016, 45(7): 874-882. DOI: . doi: 10.11947/j.AGCS.2016.20150162
	YAN Xiongfeng, AI Tinghua, YANG Min. A simplification of residential feature by the shape cognition and template matching method[J]. Acta Geodaetica et Cartographica Sinica, 2016, 45(7): 874-882. DOI: . doi: 10.11947/j.AGCS.2016.20150162
[13]	郭仁忠, 艾廷华. 制图综合中建筑物多边形的合并与化简[J]. 武汉测绘科技大学学报, 2000, 25(1), 25-30.
	GUO Renzhong, AI Tinghua. Simplification and aggregation of building polygon automatic map generalization[J]. Journal of Wuhan Technical University of surveying and Mapping, 2000, 25(1): 25-30.
[14]	王辉连, 武芳, 张琳琳, 等. 数学形态学和模式识别在建筑多边形化简中的应用[J]. 测绘学报, 2005, 34(3): 269-276.
	WANG Huilian, WU Fang, ZHANG Linlin, et al. The application of mathematical morphology and pattern recognition to building polygon simplification[J]. Acta Geodaetica et Cartographica Sinica, 2005, 34(3): 269-276.
[15]	武芳, 杜佳威, 钱海忠, 等. 地图综合智能化研究的发展与思考[J]. 武汉大学学报(信息科学版), 2022, 47(10): 1675-1687.
	WU Fang, DU Jiawei, QIAN Haizhong, et al. Overview of research progress and reflections in intelligent map generalization[J]. Geomatics and Information Science of Wuhan University, 2022, 47(10), 1675-1687.
[16]	YAN X, YANG M, AI T. Deep learning in automatic map generalization: achievements and challenges[J]. Geo-Spatial Information Science, 2025: 1-22.
[17]	FENG Y, THIEMANN F, SESTER M. Learning cartographic building generalization with deep convolutional neural networks[J]. International Journal of Geo-Information, 2019, 8(6): 258.
[18]	YAN X, YANG M. A deep learning approach for polyline and building simplification based on graph autoencoder with flexible constraints[J]. Cartography and Geographic Information Science, 2024, 51(1), 79-96.
[19]	YANG M, YUAN T, YAN X, et al. A hybrid approach to building simplification with an evaluator from a backpropagation neural network[J]. International Journal of Geographical Information Science, 2022, 36(2): 280-309.
[20]	晏雄锋, 袁拓, 杨敏, 等. 建筑物形状特征分析表达与自适应化简方法[J]. 测绘学报, 2022, 51(2): 269-278. DOI: . doi: 10.11947/j.AGCS.2020.20190299
	YAN Xiongfeng, YUAN Tuo, YANG Min, et al. An adaptive building simplification approach based on shape analysis and representation[J]. Acta Geodaetica et Cartographica Sinica, 2022, 51(2): 269-278. DOI: . doi: 10.11947/j.AGCS.2020.20190299
[21]	HU Y, GOODCHILD M, YUAN M, et al. A five-year milestone: reflections on advances and limitations in GeoAI research[J]. Annals of GIS, 2024, 30(1): 1-14. DOI: . doi: 10.1080/19475683.2024.2309866
[22]	吴华意, 沈张骁, 侯树洋, 等. 大语言模型驱动的GIS分析：方法、应用与展望[J]. 测绘学报, 2025, 54(4): 621-635. DOI: . doi: 10.11947/j.AGCS.2025.20240468
	WU Huayi, SHEN Zhangxiao, HOU Shuyang, et al. Large language model-driven GIS analysis: methods, applications, and prospects[J]. Acta Geodaetica et Cartographica Sinica, 2025, 54(4): 621-635. DOI: . doi: 10.11947/j.AGCS.2025.20240468
[23]	VASWANI A, SHAZEER N, PARMAR N, et al. Attention is all you need[C]//Proceedings of the 31st International Conference on Neural Information Processing Systems. Long Beach: Curran Associates Inc., 2017: 5998-6008.
[24]	ZHU G, TANG M, MA Y, et al. LandGPT: a multimodal large language model for parcel-level land use classification with multi-source data[J]. International Journal of Geographical Information Science, 2025: 1-24.
[25]	LI H, XU C, YANG W, et al. Unsupervised multiview UAV image geo-localization via iterative rendering[J]. IEEE Transactions on Geoscience and Remote Sensing, 2025, 63: 1-15.
[26]	DU J, WU F, YIN J, et al. Polyline simplification based on the artificial neural network with constraints of generalization knowledge[J]. Cartography and Geographic Information Science, 49(4): 313-337.
[27]	HUANG F, LÜ J R, LI G L, et al. SPOK: tokenizing geographic space for enhanced spatial reasoning in GeoAI[J]. International Journal of Geographical Information Science, 2025: 1-41. https://doi.org/10.1080/13658816.2025.2497810.

参数	网格尺寸
参数	0.2 mm	0.3 mm	0.5 mm
点位平均偏移/m	0.26	0.39	0.49
词汇表尺寸	250 002	110 891	40 002
模型参数量	2.9×10⁸	1.5×10⁸	8×10⁷

参数	目标比例尺	网格尺寸			目标比例尺	网格尺寸
参数	目标比例尺	0.2 mm	0.3 mm	0.5 mm	目标比例尺	0.2 mm	0.3 mm	0.5 mm
训练时间/min		2650	2130	1980		2730	2440	1802
训练损失值		0.43	0.035	0.039		0.54	0.13	0.13
验证损失值	1∶5000	0.45	0.038	0.043	1∶10 000	0.54	0.14	0.14
一致率/（%）	1∶5000	32.0	92.3	90.6	1∶10 000	34.3	94.3	92.1
面积增量/（%）		－0.14	0.14	0.23		－0.20	0.21	0.32
直角率/（%）		93.3	96.7	97.0		89.8	97.2	97.8

序号	一致性评价	序号	一致性评价
1	Y Y	11	N Y
2	Y Y	12	N Y
3	Y Y	13	N Y
4	Y Y	14	N Y
5	Y Y	15	N Y
6	Y Y	16	N N
7	Y Y	17	N N
8	Y Y	18	N N
9	Y Y	19	N N
10	Y Y	20	N N

序号	面积差百分比/（%）	序号	面积差百分比/（%）
1	－1.2	6	1.0
2	2.6	7	－3.0
3	－1.4	8	3.4
4	0.5	9	0.8
5	2.8	10	1.7