Keeping walls straight: data model and training set size matter for deep learning in building generalization

References

• Ai, T., & van Oosterom, P. (2001). A map generalization model based on algebra mapping transformation. In Proceedings of the Ninth ACM International Symposium on Advances in Geographic Information Systems – GIS ’01 (pp. 21). ACM Press. https://doi.org/10.1145/512163.512167
   Google Scholar
• Barrault, M., Regnauld, N., Duchêne, C., Haire, K., Baeijs, C., Demazeau, Y., & Weibel, R. (2001). Integrating multi-agent, object-oriented, and algorithmic techniques for improved automated map generalisation. In 20th international cartographic conference, Beijing, China. (Vol. 3, pp. 2110–2116). International Cartography Association (ICA).
   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
• Brassel, K. E., & Weibel, R. (1988). A review and conceptual framework of automated map generalization. International Journal of Geographical Information System, 2(3), 229–244.
   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), 338. https://doi.org/10.3390/ijgi9050338
   Web of Science ®Google Scholar
• Courtial, A., Touya, G., & Zhang, X. (2022a). 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). Deriving map images of generalised mountain roads with generative adversarial networks. International Journal of Geographical Information Science, 37(3), 499–528. https://doi.org/10.1080/13658816.2022.2123488
   Web of Science ®Google Scholar
• Courtial, A., Touya, G., & Zhang, X. (2022c). Representing vector geographic information as a tensor for deep learning based map generalisation. AGILE: GIScience Series, 3, 1–8. https://doi.org/10.5194/agile-giss-3-32-2022
   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
• 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). 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
• Harrie, L., & Weibel, R. (2007). Modelling the overall process of generalisation. In Generalisation of Geographic Information (pp. 67–87). Elsevier Science BV.
   Google Scholar
• Hertzmann, A., Jacobs, C. E., Oliver, N., Curless, B., & Salesin, D. H. (2001). Image analogies. In Proceedings of the 28th Annual Conference on Computer Graphics and Interactive Techniques – SIGGRAPH ’01 (Vol. 2001, pp. 327–340). ACM Press. https://doi.org/10.1145/383259.383295
   Google Scholar
• He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 770–778). IEEE. https://doi.org/10.1109/CVPR.2016.90
   Google Scholar
• Isola, P., Zhu, J.-Y., Zhou, T., & Efros, A. A. (2017). Image-to-image translation with conditional adversarial networks. In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (Vol. 2017-Janua, pp. 5967–5976). IEEE. https://doi.org/10.1109/CVPR.2017.632
   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. The 23rd International Research Symposium on Cartography, ( 2019). (pp. 1–6). https://linkinghub.elsevier.com/retrieve/pii/S0924271619300437
   Google Scholar
• Karsznia, I., & Weibel, R. (2018). Improving settlement selection for small-scale maps using data enrichment and machine learning. Cartography and Geographic Information Science, 45(2), 111–127. https://doi.org/10.1080/15230406.2016.1274237
   Web of Science ®Google Scholar
• Kipf, T. N., & Welling, M. (2016). Semi-supervised classification with graph convolutional networks. In 5th International Conference on Learning Representations, ICLR 2017 – Conference Track Proceedings (pp. 1–14). http://arxiv.org/abs/1609.02907
   Google Scholar
• Lecordix, F., Plazanet, C., & Lagrange, J. P. (1997). A platform for research in generalization: Application to caricature. GeoInformatica, 1(2), 161–182. https://doi.org/10.1023/A:1009736628698
   Google Scholar
• Lecun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444. https://doi.org/10.1038/nature14539
   PubMed Web of Science ®Google Scholar
• Lee, J., Jang, H., Yang, J., & Yu, K. (2017). Machine learning classification of buildings for map generalization. ISPRS International Journal of Geo-Information, 6(10), 309. https://doi.org/10.3390/ijgi6100309
   Web of Science ®Google Scholar
• Lin, T., Goyal, P., Girshick, R., He, K., & Dollár, P. (2017). Focal loss for dense object detection. In Proceedings of the IEEE International Conference on Computer Vision (pp. 2980–2988). http://arxiv.org/abs/1708.02002
   Google Scholar
• Long, J., Shelhamer, E., & Darrell, T. (2015). Fully convolutional networks for semantic segmentation. In 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 3431–3440). IEEE. https://doi.org/10.1109/CVPR.2015.7298965
   Google Scholar
• Mai, G., Janowicz, K., Hu, Y., Gao, S., Yan, B., Zhu, R., Cai, L., & Lao, N. (2021). A review of location encoding for GeoAI: Methods and applications. International Journal of Geographical Information Science, 1–35. https://doi.org/10.1080/13658816.2021.2004602
   Web of Science ®Google Scholar
• Mai, G., Jiang, C., Sun, W., Zhu, R., Xuan, Y., Cai, L., Janowicz, K., Ermon, S., & Lao, N. (2023). Towards general-purpose representation learning of polygonal geometries. GeoInformatica, 27(2), 289–340. https://doi.org/10.1007/s10707-022-00481-2
   Web of Science ®Google Scholar
• Oktay, O., Schlemper, J., Folgoc, L., Lee, M., Heinrich, M., Misawa, K., Mori, K., McDonagh, S., Hammerla, N. Y.,Kainz, B.,Glocker, B., & Rueckert, D. (2018). Attention U-Net: Learning where to look for the pancreas, (Midl). http://arxiv.org/abs/1804.03999
   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. In H. Wallach, H. Larochelle, A. Beygelzimer, F. D\textquotesingle Alché-Buc, E. Fox, & R. Garnett (Eds.), Advances in neural information processing systems (Vol. 32). Curran Associates, Inc. https://proceedings.neurips.cc/paper/2019/file/bdbca288fee7f92f2bfa9f7012727740-Paper.pdf
   Google Scholar
• Plazanet, C., Bigolin, N. M., & Ruas, A. (1998). Experiments with learning techniques for spatial model enrichment and line generalization. GeoInformatica, 2(4), 315–333.
   Google Scholar
• Powitz, B. M. (1993). Computer-assisted generalization – An important software-tool in GIS. International Archives of Photogrammetry and Remote Sensing, 29, 664–672.
   Google Scholar
• Ronneberger, O., Fischer, P., & Brox, T. (2015). U-Net: Convolutional networks for biomedical image segmentation. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 9351, 234–241. https://doi.org/10.1007/978-3-319-24574-4_28
   Google Scholar
• Ruas, A., & Duchêne, C. A. (2007). A prototype generalisation system based on the multi-agent system paradigm. Generalisation of Geographic Information. https://doi.org/10.1016/B978-008045374-3/50016-8
   Google Scholar
• Sester, M., Feng, Y., & Thiemann, F. (2018). Building generalization using deep learning. ISPRS - International Archives of the Photogrammetry Remote Sensing and Spatial Information Sciences, XLII–4(4), 565–572. https://doi.org/10.5194/isprs-archives-XLII-4-565-2018
   Google Scholar
• Shang, W., Sohn, K., Almeida, D., & Lee, H. (2016). Understanding and improving convolutional neural networks via concatenated rectified linear units. In 33rd International Conference on Machine Learning, ICML 2016 (Vol. 5, pp. 3276–3284). http://arxiv.org/abs/1603.05201
   Google Scholar
• Shen, Y., Ai, T., Li, W., Yang, M., & Feng, Y. (2019). A polygon aggregation method with global feature preservation using superpixel segmentation. Computers, Environment and Urban Systems, 75(January), 117–131. https://doi.org/10.1016/j.compenvurbsys.2019.01.009
   Google Scholar
• Steiniger, S., Lange, T., Burghardt, D., & Weibel, R. (2008). An approach for the classification of urban building structures based on discriminant analysis techniques. Transactions in GIS, 12(1), 31–59. https://doi.org/10.1111/j.1467-9671.2008.01085.x
   Google Scholar
• Steiniger, S., Taillandier, P., & Weibel, R. (2010). Utilising urban context recognition and machine learning to improve the generalisation of buildings. International Journal of Geographical Information Science, 24(2), 253–282. https://doi.org/10.1080/13658810902798099
   Web of Science ®Google Scholar
• Touya, G., Lokhat, I., & Duchêne, C. (2019). CartAGen: An open source research platform for map generalization. In Proceedings of the ICA (Vol. 2, pp. 1–9). https://doi.org/10.5194/ica-proc-2-134-2019
   Google Scholar
• Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., & Polosukhin, I. (2017). Attention is all you need. In Proceedings of the 31st International Conference on Neural Information Processing Systems (pp. 6000–6010). Curran Associates Inc. https://doi.org/10.5555/3295222.3295349
   Google Scholar
• Wang, L., Ai, T., Burghardt, D., Shen, Y., & Yang, M. (2022). A hexagon-based method for polygon generalization using morphological operators. International Journal of Geographical Information Science, 1–30. https://doi.org/10.1080/13658816.2022.2108036
   Web of Science ®Google Scholar
• Weibel, R., & Dutton, G. (1998). Constraint-based automated map generalization. In Proceedings 8th international symposium on spatial data handling (pp. 214–224)
   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. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 988, 139–156. https://doi.org/10.1007/3-540-60392-1_10
   Google Scholar
• Wei, Z., Liu, Y., Cheng, L., & Ding, S. (2021). A progressive and combined building simplification approach with local structure classification and backtracking strategy. ISPRS International Journal of Geo-Information, 10(5), 302. https://doi.org/10.3390/ijgi10050302
   Web of Science ®Google Scholar
• Wu, A. N., & Biljecki, F. (2022). Ganmapper: Geographical data translation. International Journal of Geographical Information Science, 36(7), 1394–1422. https://doi.org/10.1080/13658816.2022.2041643
   Web of Science ®Google Scholar
• Xiao, T., Ai, T., Yu, H., Yang, M., & Liu, P. (2023). A point selection method in map generalization using graph convolutional network model. Cartography and Geographic Information Science, 1–21. https://doi.org/10.1080/15230406.2023.2187886
   Web of Science ®Google Scholar
• Yan, X., Ai, T., Yang, M., & Tong, X. (2020). 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., Tong, X., & Liu, Q. (2022). A graph deep learning approach for urban building grouping. Geocarto International, 37(10), 2944–2966. https://doi.org/10.1080/10106049.2020.1856195
   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. ISPRS Journal of Photogrammetry and Remote Sensing, 150(February), 259–273. https://doi.org/10.1016/j.isprsjprs.2019.02.010
   Google Scholar
• Ying, X. (2019). An overview of overfitting and its solutions. Journal of Physics: Conference Series, 1168(2), 022022. https://doi.org/10.1088/1742-6596/1168/2/022022
   Google Scholar
• Yu, W., & Chen, Y. (2022). Data-driven polyline simplification using a stacked autoencoder-based deep neural network. Transactions in GIS, 26(5), 2302–2325. https://doi.org/10.1111/tgis.12965
   Web of Science ®Google Scholar
• Zhang, Z., Liu, Q., & Wang, Y. (2018). Road extraction by deep Residual U-Net. IEEE Geoscience and Remote Sensing Letters, 15(5), 749–753. https://doi.org/10.1109/LGRS.2018.2802944
   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 Geo-Information, 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(September), 1–2. https://doi.org/10.5194/ica-abs-5-86-2022
   Google Scholar
• Zhou, Q., & Li, Z. (2016). Empirical determination of geometric parameters for selective omission in a road network. International Journal of Geographical Information Science, 30(2), 263–299. https://doi.org/10.1080/13658816.2015.1085538
   Web of Science ®Google Scholar
• Zhu, J.-Y., Park, T., Isola, P., & Efros, A. A. (2017). Unpaired image-to-image translation using cycle-consistent adversarial networks. In 2017 IEEE International Conference on Computer Vision (ICCV) (Vol. 2017-Octob, pp. 2242–2251). IEEE. https://doi.org/10.1109/ICCV.2017.244
   Google Scholar