Learning from vector data: enhancing vector-based shape encoding and shape classification for map generalization purposes

References

• Arulkumaran, K., Deisenroth, M. P., Brundage, M., & Bharath, A. A. (2017). Deep reinforcement learning: A brief survey. IEEE Signal Processing Magazine, 34(6), 26–38. https://doi.org/10.1109/MSP.2017.2743240
   Web of Science ®Google Scholar
• Basaraner, M., & Cetinkaya, S. (2017). Performance of shape indices and classification schemes for characterising perceptual shape complexity of building footprints in gis. International Journal of Geographical Information Science, 31(10), 1952–1977. https://doi.org/10.1080/13658816.2017.1346257
   Web of Science ®Google Scholar
• Blana, N., & Tsoulos, L. (2022). Generalization of linear and area features incorporating a shape measure. ISPRS International Journal of Geo-Information, 11(9), 489. https://doi.org/10.3390/ijgi11090489
   Web of Science ®Google Scholar
• Buttenfield, B. 1991, 1). A rule for describing line feature geometry. In B. Buttenfield & R. B. McMaster. (Eds.), Map generalization: Making rules for knowledge representation (pp. 150–171). Longman Scientific & Technical London.
   Google Scholar
• Courtial, A., El Ayedi, A., Touya, G., & Zhang, X. (2020). Exploring the potential of deep learning segmentation for mountain roads generalisation. ISPRS International Journal of Geo-Information, 9(5). https://doi.org/10.3390/ijgi9050338
   Web of Science ®Google Scholar
• Courtial, A., Touya, G., & Zhang, X. (2021). Generative adversarial networks to generalise urban areas in topographic maps. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLIII-B4-2021, 15–22. https://doi.org/10.5194/isprs-archives-XLIII-B4-2021-15-2021
   Google Scholar
• Courtial, A., Touya, G., & Zhang, X. (2022a, May 7). Constraint-based evaluation of map images generalized by deep learning. Journal of Geovisualization and Spatial Analysis, 6(1), 13. https://doi.org/10.1007/s41651-022-00104-2
   Google Scholar
• Courtial, A., Touya, G., & Zhang, X. (2022b). Representing vector geographic information as a tensor for deep learning based map generalisation. AGILE: GIScience Series 3, 32. https://doi.org/10.5194/agile-giss-3-32-2022
   Google Scholar
• Duchêne, C., Touya, G., Taillandier, P., Gaffuri, J., Ruas, A., & Renard, J. (2018, January). Multi-agents systems for cartographic generalization: Feedback from past and on-going research (Research Report). IGN (Institut National de l’Information Géographique et Forestière); LaSTIG, équipe COGIT. https://hal.archives-ouvertes.fr/hal-01682131
   Google Scholar
• Du, J., Wu, F., Xing, R., Gong, X., & Yu, L. (2022). Segmentation and sampling method for complex polyline generalization based on a generative adversarial network. Geocarto International, 37(14), 4158–4180. https://doi.org/10.1080/10106049.2021.1878288
   Web of Science ®Google Scholar
• Du, J., Wu, F., Yin, J., Liu, C., & Gong, X. (2022). Polyline simplification based on the artificial neural network with constraints of generalization knowledge. Cartography and Geographic Information Science, 49(4), 313–337. https://doi.org/10.1080/15230406.2021.2013944
   Web of Science ®Google Scholar
• Fan, H., Zhao, Z., & Li, W. (2021). Towards measuring shape similarity of polygons based on multiscale features and grid context descriptors. ISPRS International Journal of GeoInformation, 10(5), 279. https://doi.org/10.3390/ijgi10050279
   Web of Science ®Google Scholar
• Feng, Y., Thiemann, F., & Sester, M. (2019). Learning cartographic building generalization with deep convolutional neural networks. ISPRS International Journal of Geo-Information, 8(6), 258. https://doi.org/10.3390/ijgi8060258
   Web of Science ®Google Scholar
• García Balboa, J. L., & Ariza López, F. J. (2008, September 1). Generalization-oriented road line classification by means of an artificial neural network. GeoInformatica, 12(3), 289–312. https://doi.org/10.1007/s10707-007-0026-z.
   Web of Science ®Google Scholar
• Ha, D., & Eck, D. (2017). A neural representation of sketch drawings. arXiv. https://doi.org/10.48550/ARXIV.1704.03477
   Google Scholar
• Iddianozie, C., & McArdle, G. (2021). Towards robust representations of spatial networks using graph neural networks. Applied Sciences, 11(15), 6918. https://doi.org/10.3390/app11156918
   Google Scholar
• Kang, Y., Rao, J., Wang, W., Peng, B., Gao, S., & Zhang, F. (2020). Towards cartographic knowledge encoding with deep learning: A case study of building generalization. In Autocarto 2020, the 23rd International Research Symposium on Cartography and Giscience. https://cartogis.org/docs/autocarto/2020/docs/abstracts/2b%20Towards%20Cartographic%20Knowledge%20Encoding%20with%20Deep%20Learning%20A.pdf
   Google Scholar
• Karsznia, I., & Sielicka, K. (2020). When traditional selection fails: How to improve settlement selection for small-scale maps using machine learning. ISPRS International Journal of GeoInformation, 9(4), 230. https://doi.org/10.3390/ijgi9040230
   Web of Science ®Google Scholar
• Karsznia, I., Weibel, R., & Leyk, S. (2022). May ai help you? Automatic settlement selection for small-scale maps using selected machine learning models. https://cartogis.org/docs/autocarto/2022/docs/abstracts/Session3Karsznia5316.pdf
   Google Scholar
• Liao, S., Bai, Z., & Bai, Y. (2012, December 1). Errors prediction for vector-to-raster conversion based on map load and cell size. Chinese Geographical Science, 22(6), 695–704. https://doi.org/10.1007/s11769-012-0544-y.
   Web of Science ®Google Scholar
• Liu, C., Hu, Y., Li, Z., Xu, J., Han, Z., & Guo, J. (2021). Triangleconv: A deep point convolutional network for recognizing building shapes in map space. ISPRS International Journal of Geo-Information, 10(10), 687. https://doi.org/10.3390/ijgi10100687
   Web of Science ®Google Scholar
• Li, Z., Yan, H., Ai, T., & Chen, J. (2004). Automated building generalization based on urban morphology and gestalt theory. International Journal of Geographical Information Science, 18(5), 513–534. https://doi.org/10.1080/13658810410001702021
   Web of Science ®Google Scholar
• Mackaness, W., Burghardt, D., & Duchêne, C. (2014). Map generalisation: Fundamental to the modelling and understanding of geographic space. In D. Burghardt, C. Duchêne, & W. Mackaness, (Eds.), Abstracting geographic information in a data rich world: Methodologies and applications of map generalisation. (pp. 1–15). Springer International Publishing. https://doi.org/10.1007/978-3-319-00203-3_1
   Google Scholar
• Mai, G., Janowicz, K., Hu, Y., Gao, S., Yan, B., Zhu, R., L. Cai, & Lao, N. (2022). A review of location encoding for geoai: Methods and applications. International Journal of Geographical Information Science, 36(4), 639–673. https://doi.org/10.1080/13658816.2021.2004602
   Web of Science ®Google Scholar
• Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen T., Lin Z. ,Gimelshein N. ,Antiga L. ,Desmaison A. ,Köpf A. ,Yang E. ,DeVito Z. ,Raison M. ,Tejani A. ,Chilamkurthy S. ,Steiner B. ,Fang L, … Chintala, S. (2019). Pytorch: An imperative style, high-performance deep learning library. arXiv. https://doi.org/10.48550/ARXIV.1912.01703
   Google Scholar
• Qi, C. R., Su, H., Mo, K., & Guibas, L. J. (2016). Pointnet: Deep learning on point sets for 3d classification and segmentation. arXiv. https://doi.org/10.48550/ARXIV.1612.00593
   Google Scholar
• Rainsford, D., & Mackaness, W. (2002). Template matching in support of generalisation of rural buildings. In D. E. Richardson & P. van Oosterom (Eds.), Advances in spatial data handling (pp. 137–151). Springer.
   Google Scholar
• Ruthotto, L., & Haber, E. (2021). An introduction to deep generative modeling. CoRr Abs/2103.05180, 44(2). https://doi.org/10.48550/arXiv.2103.05180
   Google Scholar
• Samsonov, T. E., & Yakimova, O. P. (2017). Shape-adaptive geometric simplification of heterogeneous line datasets. International Journal of Geographical Information Science, 31(8), 1485–1520. https://doi.org/10.1080/13658816.2017.1306864
   Web of Science ®Google Scholar
• Sester, M. (2020 12). Cartographic generalization. Journal of Spatial Information Science, (21), 5–11. https://doi.org/10.5311/JOSIS.2020.21.716
   Web of Science ®Google Scholar
• Stoter, J., Zhang, X., Stigmar, H., & Harrie, L. (2014). Evaluation in generalisation. In D. Burghardt, C. Duchêne, & W. Mackaness (Eds.), Abstracting geographic information in a data rich world: Methodologies and applications of map generalisation (pp. 259–297). Springer International Publishing.
   Google Scholar
• Touya, G., & Lokhat, I. (2020, April). Deep learning for enrichment of vector spatial databases: Application to highway interchange. ACM Transactions on Spatial Algorithms and Systems, 6(3), 1–21. https://doi.org/10.1145/3382080
   Web of Science ®Google Scholar
• Touya, G., Zhang, X., & Lokhat, I. (2019). Is deep learning the new agent for map generalization? International Journal of Cartography, 5(2–3), 142–157. https://doi.org/10.1080/23729333.2019.1613071
   Web of Science ®Google Scholar
• van der Maaten, L., & Hinton, G. (2008). Visualizing data using t-sne. Journal of Machine Learning Research, 9(86), 2579–2605. http://jmlr.org/papers/v9/vandermaaten08a.html
   Google Scholar
• Veer, R. V. T., Bloem, P., & Folmer, E. (2018). Deep learning for classification tasks on geospatial vector polygons. arXiv. https://doi.org/10.48550/ARXIV.1806.03857
   Google Scholar
• Vinyals, O., Fortunato, M., & Jaitly, N. 2017. Pointer networks. arXiv. https://doi.org/10.48550/arXiv.1506.03134
   Google Scholar
• Wang, X., & Burghardt, D. (2020). Using stroke and mesh to recognize building group patterns. International Journal of Cartography, 6(1), 71–98. https://doi.org/10.1080/23729333.2019.1574371
   Google Scholar
• Weibel, R., Keller, S., & Reichenbacher, T. (1995). Overcoming the knowledge acquisition bottleneck in map generalization: The role of interactive systems and computational intelligence. In A. U. Frank & W. Kuhn (Eds.), Spatial information theory a theoretical basis for gis (pp. 139–156). Springer.
   Google Scholar
• Yan, X., Ai, T., Yang, M., & Tong, X. (2021). Graph convolutional autoencoder model for the shape coding and cognition of buildings in maps. International Journal of Geographical Information Science, 35(3), 490–512. https://doi.org/10.1080/13658816.2020.1768260
   Web of Science ®Google Scholar
• Yan, X., Ai, T., Yang, M., & Yin, H. (2019). A graph convolutional neural network for classification of building patterns using spatial vector data. IS- PRS Journal of Photogrammetry and Remote Sensing 150, 259–273. https://doi.org/10.1016/j.isprsjprs.2019.02.010
   Web of Science ®Google Scholar
• Yan, X., Ai, T., & Zhang, X. (2017). Template matching and simplification method for building features based on shape cognition. ISPRS International Journal of Geo-Information, 6(8), 250. https://doi.org/10.3390/ijgi6080250
   Web of Science ®Google Scholar
• Yang, M., Huang, H., Zhang, Y., & Yan, X. (2022). Pattern recognition and segmentation of administrative boundaries using a one-dimensional convolutional neural network and grid shape context descriptor. ISPRS International Journal of Geo-Information, 11(9), 461. https://doi.org/10.3390/ijgi11090461
   Web of Science ®Google Scholar
• Yang, M., Yuan, T., Yan, X., Ai, T., & Jiang, C. (2022). A hybrid approach to building simplification with an evaluator from a backpropagation neural network. International Journal of Geographical Information Science, 36(2), 280–309. https://doi.org/10.1080/13658816.2021.1873998
   Web of Science ®Google Scholar
• Zheng, J., Gao, Z., Ma, J., Shen, J., & Zhang, K. (2021). Deep graph convolutional networks for accurate automatic road network selection. ISPRS International Journal of GeoInformation, 10(11), 768. https://doi.org/10.3390/ijgi10110768
   Web of Science ®Google Scholar
• Zhou, Z., Fu, C., & Weibel, R. (2022). Building simplification of vector maps using graph convolutional neural networks. Abstracts of the ICA, 5(86), 1–2. https://doi.org/10.5194/ica-abs-5-86-2022
   Google Scholar