Knowledge-guided intelligent recognition of the scale for fragmented raster topographic maps

doi:10.11947/j.AGCS.2024.20230005

Abstract

Abstract: Determining the topographic map scale is a critical basis for assessing the degree of confidentiality of topographic maps. In this study, we propose a solution to the challenge of estimating the scale of fragmented raster topographic maps by leveraging a priori knowledge of scale-related features, constructing an expert knowledge image pyramid dataset (EKIPD) under guided expert knowledge, and applying deep convolutional neural network algorithms to create a hybrid intelligent model that synergistically combines knowledge, data, and algorithm. The EKIPD dataset captures a representative sample distribution of fragmented topographic maps of varying sizes, which enables us to statistically determine the optimal recognition size (ORS) for sub-map recognition. The ORS then serves as a stepping threshold to partition the topographic maps into recognizable sub-maps. Each sub-map is independently processed through the model to obtain individual predictions, which are subsequently integrated to infer the map scale. Experimental validation shows that this method achieves an accuracy of approximately 97%, demonstrating its efficacy.

Key words: intelligentized surveying and mapping, expert knowledge, hybrid intelligence, raster topographic map, scale recognition, deep convolutional neural network

CLC Number:

P258

REN Jiaxin, LIU Wanzeng, CHEN Jun, ZHANG Lan, TAO Yuan, ZHU Xiuli, ZHAO Tingting, LI Ran, ZHAI Xi, WANG Haiqing, ZHOU Xiaoguang, HOU Dongyang, WANG Yong. Knowledge-guided intelligent recognition of the scale for fragmented raster topographic maps[J]. Acta Geodaetica et Cartographica Sinica, 2024, 53(1): 146-157.

References

[1] HE H, WANG S, WANG S,et al.A road extraction method for remote sensing image based on encoder-de-coder network[J].Journal of Geodesy and Geoinformation Science, 2020,3(02):16-25.
[2] ZUO Z, ZHANG W, ZHANG D. A remote sensing image semantic segmentation method by combining deformable convolution with conditional random fields[J]. Journal of Geodesy and Geoinformation Science, 2020,3(3):39-49.
[3] WANG Yanjun, LI Shaochun, WANG Mengjie, et al.A simple deep learning network for classification of 3D mobile LiDAR point clouds[J]. Journal of Geodesy and Geoinformation Science, 2021, 4(3):49-59.
[4] CHEN Chen, LI Zhilin, LI Songnian, et al. From digitalized to intelligentized surveying and mapping: fundamental is-sues and research agenda[J]. Journal of Geodesy and Geoinformation Science, 2022, 5(2):148-160.
[5] 任加新, 刘万增, 李志林, 等. 利用卷积神经网络进行“问题地图” 智能检测[J].武汉大学学报(信息科学版), 2021, 46(4): 570-577. REN Jiaxin, LIU Wanzeng, LI Zhilin, et al. Intelligent detection of “problematic map” using convolutional neural network[J]. Geomatics and Information Science of Wuhan University, 2021, 46(4): 570-577.
[6] 何海威, 钱海忠, 谢丽敏, 等. 立交桥识别的CNN卷积神经网络法[J]. 测绘学报, 2018, 47(3):385-395.DOI: 10.11947/j.AGCS.2018.20170265. HE Haiwei, QIAN Haizhong, XIE Limin, et al. Interchange recognition method based on CNN[J]. Acta Geodaetica et Cartographica Sinica, 2018, 47(3):385-395.DOI: 10.11947/j.AGCS.2018.20170265.
[7] 左溪冰, 刘冰, 余旭初, 等. 高光谱影像小样本分类的图卷积网络方法[J]. 测绘学报, 2021, 50(10):1358-1369.DOI: 10.11947/j.AGCS.2021.20200155. ZUO Xibing, LIU Bing, YU Xuchu, et al. Graph convolutional network method for small sample classification of hyperspectral images[J]. Acta Geodaetica et Cartographica Sinica, 2021, 50(10):1358-1369.DOI: 10.11947/j.AGCS.2021.20200155.
[8] 成飞飞, 付志涛, 黄亮, 等. 结合自适应PCNN的非下采样剪切波遥感影像融合[J]. 测绘学报, 2021, 50(10):1380-1389. DOI: 10.11947/j.AGCS.2021.20200589. CHENG Feifei, FU Zhitao, HUANG Liang, et al. Non-subsampled shearlet transform remote sensing image fusion combined with parameter-adaptive PCNN[J]. Acta Geodaetica et Cartographica Sinica, 2021, 50(10):1380-1389. DOI: 10.11947/j.AGCS.2021.20200589.
[9] 陈军, 刘万增, 武昊, 等. 智能化测绘的基本问题与发展方向[J]. 测绘学报, 2021, 50(8): 995-1005.DOI: 10.11947/j.AGCS.2021.20210235. CHEN Jun, LIU Wanzeng, WU Hao, et al. Smart surveying and mapping: fundamental issues and research agenda[J]. Acta Geodaetica et Cartographica Sinica, 2021, 50(8): 995-1005.DOI: 10.11947/j.AGCS.2021.20210235.
[10] 刘万增, 陈军, 任加新, 等. 基于混合智能的地图自动审核技术框架[J]. 武汉大学学报(信息科学版), 2022, 47(12): 2038-2046. LIU Wanzeng, CHEN Jun, REN Jiaxin, et al. Hybrid intelligence-based framework for automatic map inspecting technology[J]. Geomatics and Information Science of Wuhan University, 2022, 47(12): 2038-2046.
[11] 陈军, 刘万增, 武昊, 等. 基础地理知识服务的基本问题与研究方向[J]. 武汉大学学报(信息科学版), 2019, 44(1): 38-47. CHEN Jun, LIU Wanzeng, WU Hao, et al. Basic issues and research agenda of geospatial knowledge service[J]. Geomatics and Information Science of Wuhan University, 2019, 44(1): 38-47.
[12] 刘万增, 陈军, 翟曦, 等. 时空知识中心的研究进展与应用[J]. 测绘学报, 2021, 50(9): 1183-1193. DOI: 10.11947/j.AGCS.2021.20210160. LIU Wanzeng, CHEN Jun, ZHAI Xi, et al. Research progress and application of spatiotemporal knowledge center[J]. Acta Geodaetica et Cartographica Sinica, 2021, 50(9): 1183-1193.DOI: 10.11947/j.AGCS.2021.20210160.
[13] ZHANG H, CISSE M, DAUPHIN Y N, et al. Mixup: beyond empirical risk minimization[C]//Proceedings of 2018 International Conference on Learning Representations. Stockholm: IEEE, 2018.
[14] CHOLLET F. Xception: deep learning with depthwise separable convolutions[C]//Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition. Honolulu: IEEE, 2017.
[15] TAN Mingxing, LE Q V. EfficientNet: rethinking model scaling for convolutional neural networks[EB/OL].[2019-12-10]. https://arxiv.org/abs/1905.11946.pdf.
[16] 向红梅. 基于地形图数据库的跨比例尺级别缩编更新方法研究[J]. 测绘通报, 2021(2): 153-156, 160. XIANG Hongmei. Research on automatic downsizing and updating of cross scale level based on topographic map database[J]. Bulletin of Surveying and Mapping, 2021(2): 153-156, 160.
[17] HEWITT R J, SHADMAN ROODPOSHTI M, BRYAN B A. There's no best model! Addressing limitations of land-use scenario modelling through multi-model ensembles[J].International Journal of Geographical Information Science, 2022, 36(12): 2352-2385.
[18] HE Kaiming, ZHANG Xiangyu, REN Shaoqing, et al. Deep residual learning for image recognition[C]//Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition.Las Vegas:IEEE, 2016.
[19] TALEBI H, MILANFAR P. Learning to resize images for computer vision tasks[EB/OL].[2021-10-22]. https://arxiv.org/abs/2103.09950.pdf.
[20] TOUVRON H, VEDALDI A, DOUZE M, et al. Fixing the train-test resolution discrepancy[EB/OL][2019-11-12].https://arxiv.org/abs/1906.06423.pdf.
[21] TOUVRON H, VEDALDI A, DOUZE M, et al. Fixing the train-test resolution discrepancy: fix efficient net[EB/OL].[2020-10-23].https://arxiv.org/abs/2003.08237.pdf.
[22] CHU X, CHEN L, CHEN C, et al.Revisiting global statistics aggregation for improving image restoration[EB/OL].[2021-10-20].http://arxiv.org/abs/2112.04491v1.
[23] DASARATHY B V, SHEELA B V. A composite classifier system design: concepts and methodology[J]. Proceedings of the IEEE, 1979, 67(5): 708-713.
[24] 徐继伟, 杨云. 集成学习方法: 研究综述[J]. 云南大学学报(自然科学版), 2018, 40(6):1082-1092. XU Jiwei,YANG Yun. A survey of ensemble learning approaches[J]. Journal of Yunnan University (Natural Sciences Edition), 2018, 40(6):1082-1092.
[25] 蔡毅, 朱秀芳, 孙章丽, 等. 半监督集成学习综述[J]. 计算机科学, 2017, 44(S1): 7-13. CAI Yi, ZHU Xiufang, SUN Zhangli,et al. Semi-supervised and ensemble learning: a review[J]. Computer Science, 2017, 44(S1): 7-13.
[26] 张春霞, 张讲社. 选择性集成学习算法综述[J]. 计算机学报, 2011, 34(8):1399-1410. ZHANG Chunxia, ZHANG Jiangshe. A survey of selective ensemble learning algorithms[J]. Chinese Journal of Computers, 2011, 34(8):1399-1410.
[27] HANSEN L K, SALAMON P. Neural network ensembles[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1990, 12(10): 993-1001.
[28] DIETTERICH T G. Ensemble methods in machine learning[C]//Proceedings of 2000 Multiple Classifier Systems Conference. Berlin: Springer, 2000: 1-15.
[29] BOTTOU L. Large-scale machine learning with stochastic gradient descent[C]//Proceedings of 2010 COMPSTAT Conference. Heidelberg: Springer, 2010: 177-186.