Incorporating ideas of structure and meaning in interactive multi scale mapping environments

References

• Abadie, N. (2009). Schema matching based on attribute values and background ontology. In 12th International Conference on Geographic Information Science (AGILE'09). Hanovre.
   Google Scholar
• Balley, S., Baella, B., Christophe, S., Pla, M., Regnauld, N., & Stoter, J. (2014). Map specifications and user requirements. In D. Burghardt, C. Duchêne, & W. Mackaness (Eds.), Abstracting geographic information in a data rich world (pp. 17–52). Springer International Publishing. https://doi.org/10.1007/978-3-319-00203-3_2
   Google Scholar
• Basaraner, M., & Selcuk, M. (2008). A structure recognition technique in contextual generalisation of buildings and built-up areas. The Cartographic Journal, 45(4), 274–285. https://doi.org/10.1179/174327708x347773
   Web of Science ®Google Scholar
• Bertin, J. (1967). Sémiologie graphique. Gauthier-Villars.
   Google Scholar
• Blackburn, S. (2008). Structuralism. In Oxford dictionary of philosophy (2nd rev. ed., p. 353). Oxford University Press.
   Google Scholar
• Board, C. (1981). Cartographic communication. Cartographica, 18(2), 42–78. https://doi.org/10.3138/8R07-2125-L843-0767
   Google Scholar
• Burghardt, D., Duchêne, C., & Mackaness, W. (Eds.). (2014). Abstracting geographic information in a data rich world. Springer. https://doi.org/10.1007/978-3-319-00203-3
   Google Scholar
• Buttenfield, B. P., Stanislawski, L. V., & Brewer, C. A. (2011). Adapting generalization tools to physiographic diversity for the United States national hydrography dataset. Cartography and Geographic Information Science, 38(3), 289–301. https://doi.org/10.1559/15230406382289
   Web of Science ®Google Scholar
• Carral, D., Scheider, S., Janowicz, K., Vardeman, C., Krisnadhi, A., & Hitzler, P. (2013). An ontology design pattern for cartographic map scaling. In P. Cimiano, O. Corcho, V. Presutti, L. Hollink, & S. Rudolph (Eds.), The semantic web: Semantics and big data (Vol. 7882, pp. 76–93). Springer. https://doi.org/10.1007/978-3-642-38288-8_6
   Google Scholar
• Castner, H. W. (Ed.). (1990). Seeking new horizons: A perceptual approach to geographic education. McGill-Queen's University Press.
   Google Scholar
• Chaudhry, O. Z., & Mackaness, W. A. (2006). Creation of fiat boundaries in higher order phenomenon. In 9th ICA Workshop on Generalisation and Multiple Representation. ICA.
   Google Scholar
• Chen, Y., Carlinet, E., Chazalon, J., Mallet, C., Duménieu, B., & Perret, J. (2021). Combining deep learning and mathematical morphology for historical map segmentation. In Discrete Geometry and Mathematical Morphology, First International Joint Conference, DGMM, May 24–27, 2021, Proceedings (Vol. 12708, pp. 79–92). https://doi.org/10.1007/978-3-030-76657-3_5
   Google Scholar
• Christophe, S., & Ruas, A. (2002). Detecting building structures for generalisation purposes. In Proceedings of 10th International Symposium on Spatial Data Handling (SDH'02).
   Google Scholar
• Clarke, S. (1978). The origins of Levi-Strauss's structuralism. Sociology, 12(3), 405–439. https://doi.org/10.1177/003803857801200301
   Google Scholar
• Çöltekin, A., Fabrikant, S., & Lacayo, M. (1978). Exploring the efficiency of users visual analytics strategies based on sequence analysis of eye movement recordings. International Journal of Geographical Information Science, 24(10), 1559–1575.
   Google Scholar
• Corcoran, P., Jilani, M., Mooney, P., & Bertolotto, M. (2015). Inferring semantics from geometry: The case of street networks. In Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems (pp. 1–10). Association for Computing Machinery. https://doi.org/10.1145/2820783.2820822
   Google Scholar
• Corry, L. (2004). Nicolas Bourbaki: Theory of structures. In Modern algebra and the rise of mathematical structures (Second revised Ed., (pp. 289–338). Birkhäuser. https://doi.org/10.1007/978-3-0348-7917-0_8
   Google Scholar
• Cotnoir, A. J., & Varzi, A. C. (2021). Mereology. Oxford University Press. https://doi.org/10.1093/oso/9780198749004.001.0001
   Google Scholar
• Couclelis, H. (2010). Ontologies of geographic information. International Journal of Geographical Information Science, 24(12), 1785–1809. https://doi.org/10.1080/13658816.2010.484392
   Web of Science ®Google Scholar
• Courtial, A., Touya, G., & Zhang, X. (2021). Can graph convolution networks learn spatial relations? Abstracts of the ICA, 3, 1–2. https://doi.org/10.5194/ica-abs-3-60-2021
   Google Scholar
• Courtial, A., Touya, G., & Zhang, X. (2022). Representing vector geographic information as a tensor for deep learning based map generalisation. In E. Parseliunas, A. Mansourian, P. Partsinevelos, & J. Suziedelyte-Visockiene (Eds.), AGILE GIScience Series (Vol. 3, pp. 32). Copernicus Publications. https://doi.org/10.5194/agile-giss-3-32-2022
   Google Scholar
• Crampton, J. W. (2001). Maps as social constructions: Power, communication and visualization. Progress in Human Geography, 25(2), 235–252. https://doi.org/10.1191/030913201678580494
   Web of Science ®Google Scholar
• Deng, M., Tang, J., Liu, Q., & Wu, F. (2018). Recognizing building groups for generalization: A comparative study. Cartography and Geographic Information Science, 45(3), 187–204. https://doi.org/10.1080/15230406.2017.1302821
   Web of Science ®Google Scholar
• Dobson, M. W. (1985). The future of perceptual cartography. Cartographica, 22(2), 27–43. https://doi.org/10.3138/E482-2512-732Q-2T11
   Google Scholar
• Duchowski, A. (2007). Eye tracking methodology: Theory and practice. (2nd ed.). Springer-Verlag. https://doi.org/10.1007/978-1-84628-609-4
   Google Scholar
• Dutton, G., & Edwardes, A. (2006). Ontological modeling of geographical relationships for map generalization. In 9th ICA Workshop on Generalisation and Multiple Representation.
   Google Scholar
• Elias, B. (2003). Extracting landmarks with data mining methods. In W. Kuhn, M. F. Worboys, & S. Timpf (Eds.), Spatial information theory. Foundations of geographic information science (Vol. 2825, pp. 375–389). Springer. https://doi.org/10.1007/978-3-540-39923-0_25
   Google Scholar
• Fairbairn, D., Gartner, G., & Peterson, M. P. (2021). Epistemological thoughts on the success of maps and the role of cartography. International Journal of Cartography, 7(3), 317–331. https://doi.org/10.1080/23729333.2021.1972909
   Google Scholar
• Gangemi, A., Guarino, N., Masolo, C., Oltramari, A., & Schneider, L. (2002). Sweetening ontologies with DOLCE. In A. Gómez-Pérez & V. Benjamins (Eds.), Knowledge engineering and knowledge management: Ontologies and the semantic web (Vol. 2473, pp. 223–233). Springer. https://doi.org/10.1007/3-540-45810-7_18
   Google Scholar
• Garlandini, S., & Fabrikant, S. I.. (2009). Evaluating the effectiveness and efficiency of visual variables for geographic information visualization. In K. S. Hornsby, C. Claramunt, M. Denis., & G. Ligozat (Eds.), Spatial information theory (pp. 195–211). Springer. https://doi.org/10.1007/978-3-642-03832-7_12
   Google Scholar
• Georgousis, S., Kenning, M. P., & Xie, X. (2021). Graph deep learning: State of the art and challenges. IEEE Access, 9, 22106–22140. https://doi.org/10.1109/ACCESS.2021.3055280
   Web of Science ®Google Scholar
• Gould, N., & Chaudhry, O. Z. (2012). An ontological approach to on-demand mapping. In Proceedings of 15th ICA Generalisation Workshop.
   Google Scholar
• Gould, N., & Mackaness, W. (2016). From taxonomies to ontologies: Formalizing generalization knowledge for on-demand mapping. Cartography and Geographic Information Science, 43(3), 208–222. https://doi.org/10.1080/15230406.2015.1072737
   Web of Science ®Google Scholar
• Gould, N., Mackaness, W. A., Touya, G., & Hart, G. (2014). Collaboration on an ontology for generalisation. In Proceedings of 17th ICA Workshop on Generalisation and Multiple Representation ICA.
   Google Scholar
• Griffin, A. L. (2017). Cartography, visual perception and cognitive psychology. In The Routledge handbook of mapping and cartography (chapter 3). https://doi.org/10.4324/9781315736822.ch3
   Google Scholar
• Gruber, T. R. (1995). Toward principles for the design of ontologies used for knowledge sharing? International Journal of Human-Computer Studies, 43(5-6), 907–928. https://doi.org/10.1006/ijhc.1995.1081
   Web of Science ®Google Scholar
• He, Y., Ai, T., Yu, W., & Zhang, X. (2017). A linear tessellation model to identify spatial pattern in urban street networks. International Journal of Geographical Information Science, 31(8), 1541–1561. https://doi.org/10.1080/13658816.2017.1298768
   Web of Science ®Google Scholar
• He, X., Deng, M., & Luo, G. (2020). Recognizing linear building patterns in topographic data by using two new indices based on delaunay triangulation. ISPRS International Journal of Geo-Information, 9(4), 231. Retrieved June 14, 2022, from https://www.mdpi.com/2220-9964/9/4/231. https://doi.org/10.3390/ijgi9040231
   Web of Science ®Google Scholar
• He, X., Zhang, X., & Xin, Q. (2018,February). Recognition of building group patterns in topographic maps based on graph partitioning and random forest. ISPRS Journal of Photogrammetry and Remote Sensing, 136, 26–40. https://doi.org/10.1016/j.isprsjprs.2017.12.001
   Web of Science ®Google Scholar
• Heinzle, F., & Anders, K. H.. (2007). Characterising space via pattern recognition techniques: Identifying patterns in road Networks. In W. A. Mackaness, A. Ruas, & T. Sarjakoski (Eds.), The generalisation of geographic information: Models and applications. Elsevier.
   Google Scholar
• Heinzle, F., Anders, K. H., & Sester, M.. (2005). Graph based approaches for recognition of patterns and implicit information in road networks. In International Cartographic Conference. ICA.
   Google Scholar
• Heinzle, F., Anders, K. H., & Sester, M. (2006). Pattern Recognition in road networks on the example of circular road detection. In M. Raubal, H. J. Miller, A. U. Frank, & M. F. Goodchild (Eds.), Geographic, information science (Vol. 4197, pp. 153–167). Springer. https://doi.org/10.1007/11863939_11
   Google Scholar
• Hillier, B., & Hanson, J. (1984). Frontmatter. In The social logic of space (pp. i-vi). Cambridge University Press.
   Google Scholar
• Hu, Y., Liu, C., Li, Z., Xu, J., Han, Z., & Guo, J. (2022). Few-Shot building footprint shape classification with relation network. ISPRS International Journal of Geo-Information, 11(5), 311. https://doi.org/10.3390/ijgi11050311
   Web of Science ®Google Scholar
• Iddianozie, C., & McArdle, G. (2020). Improved graph neural networks for spatial networks using structure-aware sampling. ISPRS International Journal of Geo-Information, 9(11), 674. https://doi.org/10.3390/ijgi9110674
   Web of Science ®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
• Iddianozie, C., & Mcardle, G. (2021). Transferable graph neural networks for inferring road type attributes in street networks. IEEE Access, 9, 158331–158339. https://doi.org/10.1109/ACCESS.2021.3128839
   Google Scholar
• Iosifescu, I., & Hurni, L. (2007). Towards cartographic ontologies or – how computers learn cartography– towards cartographic ontologies or how computers learn cartography. In Proceedings of 23rd International Cartographic Conference. ICA.
   Google Scholar
• Jenny, B., Heitzler, M., Singh, D., Farmakis-Serebryakova, M., Liu, J., & Hurni, L. (2020). Cartographic relief shading with neural networks. IEEE Transactions on Visualization and Computer Graphics, 27(2), 1225–1235. https://doi.org/10.1109/TVCG.2020.3030456
   Web of Science ®Google Scholar
• Kent, A. J. (2018). Form follows feedback: Rethinking cartographic communication. Westminster Papers in Communication and Culture, 13(2). 96–112.https://doi.org/10.16997/wpcc.296
   Web of Science ®Google Scholar
• Kent, A. J., & Vujakovic, P. (2011). Cartographic language: Towards a new paradigm for understanding stylistic diversity in topographic maps. The Cartographic Journal, 48(1), 21–40. https://doi.org/10.1179/1743277411Y.0000000004
   Web of Science ®Google Scholar
• Keskin, M., & Kettunen, P. (2021). Possibilities of combining AI and camera-based eye tracking to guide geoexploration.
   Google Scholar
• Keskin, M., Ooms, K., Dogru, A. O., & De Maeyer, P. (2018). Digital sketch maps and eye tracking statistics as instruments to obtain insights into spatial cognition. Journal of Eye Movement Research, 11(3)https://doi.org/10.16910/jemr.11.3.4
   PubMed Web of Science ®Google Scholar
• Keyes, L., Winstanley, A., & Healy, P. (2003). Comparing learning strategies for topographic object classification. In IGARSS 2003. IEEE International Geoscience and Remote Sensing Symposium. Proceedings (IEEE Cat. No.03CH37477) (Vol. 6, pp. 3468–3470).
   Google Scholar
• Kiefer, P., Giannopoulos, I., & Raubal, M. (2014). Where am I? Investigating map matching during self-localization with mobile eye tracking in an urban environment. Transactions in GIS, 18(5), 660–686. https://doi.org/10.1111/tgis.12067
   Web of Science ®Google Scholar
• Kiefer, P., Giannopoulos, I., Raubal, M., & Duchowski, A. (2017). Eye tracking for spatial research: Cognition, computation, challenges. Spatial Cognition & Computation, 17(1-2), 1–19. https://doi.org/10.1080/13875868.2016.1254634
   Web of Science ®Google Scholar
• Koláčný, A. (1969). Cartographic information–a fundamental concept and term in modern cartography. The Cartographic Journal, 6(1), 47–49. https://doi.org/10.1179/caj.1969.6.1.47
   Google Scholar
• Krassanakis, V., & Cybulski, P. (2021). Eye tracking research in cartography: Looking into the future. ISPRS International Journal of Geo-Information, 10(6), 411. https://doi.org/10.3390/ijgi10060411
   Web of Science ®Google Scholar
• Kulik, L., Duckham, M., & Egenhofer, M. J. (2005). Ontology-driven map generalization. Journal of Visual Languages and Computing, 16(3), 245–267. https://doi.org/10.1016/j.jvlc.2005.02.001
   Google Scholar
• Lüscher, P., Burghardt, D., & Weibel, R. (2007). Ontology-driven enrichment of spatial databases. In Proceedings of 10th ICA Workshop on Generalisation and Multiple Representation.
   Google Scholar
• Lüscher, P., & Weibel, R. (2010). Semantics Matters: cognitively plausible delineation of city centres from point of interest data. In 13th Workshop of the ICA commission on Generalisation and Multiple Representation. ICA.
   Google Scholar
• Lüscher, P., Weibel, R., & Burghardt, D. (2009). Integrating ontological modelling and Bayesian inference for pattern classification in topographic vector data. Computers, Environment and Urban Systems, 33(5), 363–374. https://doi.org/10.1016/j.compenvurbsys.2009.07.005
   Web of Science ®Google Scholar
• Lüscher, P., Weibel, R., & Mackaness, W. A. (2008). Where is the terraced house? On the use of ontologies for recognition of urban concepts in cartographic databases. In A. Ruas & C. Gold (Eds.), Headway in spatial data handling (pp. 449–466). https://doi.org/10.1007/978-3-540-68566-1_26
   Google Scholar
• Lapaine, M., Midtbø, T., Gartner, G., Bandrova, T., Wang, T., & Shen, J. (2021). Definition of the map. Advances in Cartography and GIScience of the ICA, 3, 1–6.https://doi.org/10.5194/ica-adv-3-9-2021
   Google Scholar
• Laurini, R. (2007). Pre-consensus ontologies and urban databases. In J. Teller, J. R. Lee, & C. Roussey (Eds.), Ontologies for urban development (pp. 27–36). Springer. https://doi.org/10.1007/978-3-540-71976-2_3
   Google Scholar
• Laurini, R. (2009). Towards visual summaries of geographic databases based on chorems. In G. Navratil (Ed.), Research trends in geographic information science (pp. 219–234). Springer. https://doi.org/10.1007/978-3-540-88244-2_15
   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
• Lemmens, R. (2008). Lost and found, the importance of modelling map content semantically. In M. Peterson (Ed.), International perspectives on maps and the internet (pp. 377–396). Springer. https://doi.org/10.1007/978-3-540-72029-4_24.
   Google Scholar
• Li, C., Liu, X., Wu, W., & Wu, P. (2019). An automated method for the selection of complex railway lines that accounts for multiple feature constraints. Transactions in GIS, 23(6), 1296–1316. https://doi.org/10.1111/tgis.12575
   Google Scholar
• Li, C., Zhang, H., Wu, P., Yin, Y., & Liu, S. (2020). A complex junction recognition method based on googLeNet model. Transactions in GIS, 24(6), 1756–1778. https://doi.org/10.1111/tgis.12681
   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
• Ma, L., Seipel, S., Brandt, S. A., & Ma, D. (2022). A new graph-based fractality index to characterize complexity of urban form. ISPRS International Journal of Geo-Information, 11(5), 287. https://doi.org/10.3390/ijgi11050287
   Web of Science ®Google Scholar
• MacEachren, A. M. (2004). How maps work: Representation, visualization, and design (1st ed.). The Guilford Press.
   Google Scholar
• MacEachren, A., & Ganter, J. (1990). A pattern identification approach to cartographic visualization. Cartographica: The International Journal for Geographic Information and Geovisualization, 27(2), 64–81. https://doi.org/10.3138/M226-1337-2387-3007
   Google Scholar
• Mackaness, W. A., & Beard, K. M. (1993). Use of graph theory to support map generalization. Cartography and Geographic Information Science, 20(4), 210–221. https://doi.org/10.1559/152304093782637479
   Google Scholar
• Mackaness, W. A., & Edwards, G. (2002). The importance of modelling pattern and structure in automated map generalisation. In Proceedings of the Joint ISPRS/ICA Workshop on Multi-Scale Representations of Spatial Data (pp. 7–8).
   Google Scholar
• Mackaness, W. A., Gould, N., Bechhofer, S., Burghardt, D., Duchene, C., Stevens, R., & Touya, G. (2015). Thematic workshop on building an ontology of generalisation for on-demand mapping. In Proceedings of 4th UK Ontology Network Workshop Leeds.
   Google Scholar
• Mackaness, W. A., Ruas, A., & Sarjakoski, L. T. (Eds.). (2007). Generalisation of geographic information: Models and applications. Elsevier.
   Google Scholar
• Mai, G., Janowicz, K., Hu, Y., Gao, S., Yan, B., Zhu, R., & 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
• Manson, S. M., Kne, L., Dyke, K. R., Shannon, J., & Eria, S. (2012). Using eye-tracking and mouse metrics to test usability of web mapping navigation. Cartography and Geographic Information Science, 39(1), 48–60. https://doi.org/10.1559/1523040639148
   Web of Science ®Google Scholar
• Mc Cutchan, M., & Giannopoulos, I. (2022). Encoding geospatial vector data for deep learning: LULC as a use case. Remote Sensing, 14(12), 2812. https://doi.org/10.3390/rs14122812
   Web of Science ®Google Scholar
• Meng, L. (2005). Egocentric design of map-based mobile services. The Cartographic Journal, 42(1), 5–13. https://doi.org/10.1179/000870405X57275
   Web of Science ®Google Scholar
• Mennis, J., & Guo, D. (2009). Spatial data mining and geographic knowledge discovery–an introduction. Computers, Environment and Urban Systems, 33(6), 403–408. https://doi.org/10.1016/j.compenvurbsys.2009.11.001
   Web of Science ®Google Scholar
• Montello, D. R. (2002). Cognitive map-design research in the twentieth century: Theoretical and empirical approaches. Cartography and Geographic Information Science, 29(3), 283–304. https://doi.org/10.1559/152304002782008503
   Google Scholar
• Müller, J C. (1991). Generalization of spatial databases. In D. J. Maguire, M. Goodchild, & D. Rhind (Eds.), Geographical Information Systems (pp. 457–475). Longman Scientific.
   Google Scholar
• Nes, A. (2021). The impact of the ring roads on the location pattern of shops in town and city centres. A space syntax approach. Sustainability, 13(7), 3927. https://doi.org/10.3390/su13073927
   Web of Science ®Google Scholar
• Ooms, K., Dupont, L., & Lapon, L. (2017). Mixing methods and triangulating results to study the influence of panning on map users' attentive behaviour. The Cartographic Journal, 54(3), 196–213. https://doi.org/10.1080/00087041.2016.1213517
   Web of Science ®Google Scholar
• Petchenik, B. B. (1983). A map maker's perspective on map design research 1950–1980. In D. A. F. Taylor (Ed.), (pp. 33–68). John Wiley & Sons.
   Google Scholar
• Popelka, S., & Brychtova, A. (2013). Eye-tracking study on different perception of 2D and 3D terrain visualisation. The Cartographic Journal, 50(3), 240–246. https://doi.org/10.1179/1743277413Y.0000000058
   Web of Science ®Google Scholar
• Porta, S., Crucitti, P., & Latora, V. (2006). The network analysis of urban streets: A primal approach. Environment and Planning B: Planning and Design, 33(5), 705–725. https://doi.org/10.1068/b32045
   Web of Science ®Google Scholar
• Potié, Q., Mackaness, W. A., & Touya, G. (2022). When is a ring road a 'ring road'? A brief perceptual study. In (Vol. 3, p. 54). Copernicus Publications.
   Google Scholar
• Qin, Z., & Li, Z. (2017). Grouping rules for effective legend design. The Cartographic Journal, 54(1), 36–47. https://doi.org/10.1080/00087041.2016.1148105
   Web of Science ®Google Scholar
• Reda, G. (2016). Ferdinand de saussure in the era of cognitive linguistics. Language and Semiotic Studies, 2(2), 89–100. https://doi.org/10.1515/lass-2016-020203
   Google Scholar
• Regnauld, N. (2005). Spatial structures to support automatic generalisation. In International Cartographic Conference. ICA.
   Google Scholar
• Regnauld, N. (2007). Evolving from automating existing map production systems to producing maps on demand automatically. In 10th ICA Workshop on Generalisation and Multiple Representation. ICA.
   Google Scholar
• Regnauld, N., & Mackaness, W. A. (2006). Creating a hydrographic network from its cartographic representation: A case study using ordnance survey MasterMap data. International Journal of Geographical Information Science, 20(6), 611–631. https://doi.org/10.1080/13658810600607402
   Web of Science ®Google Scholar
• Renard, J., & Duchêne, C. (2014). Urban structure generalization in multi-agent process by use of reactional agents. Transactions in GIS, 18(2), 201–218. https://doi.org/10.1111/tgis.12018
   Web of Science ®Google Scholar
• Robinson, A. H., & Petchenik, B. B. (Eds.). (1976). The nature of maps. University of Chicago Press.
   Google Scholar
• Ronneberger, O., Fischer, P., & Brox, T. (2015). U-net: Convolutional networks for biomedical image segmentation. In (Vol. abs/1505.04597, pp. 234–241). https://arxiv.org/abs/1505.04597.
   Google Scholar
• Savino, S., Rumor, M., Canton, F., Langiù, G., & Reineri, M. (2011). Model generalization of the hydrography network in the CARGEN project. In A. Ruas (Ed.), Advances in cartography and GIScience (Vol. 1, pp. 439–457). Springer. https://doi.org/10.1007/978-3-642-19143-5_25
   Google Scholar
• Savino, S., & Touya, G. (2015). Automatic structure detection and generalization of railway networks. In Proceedings of 27th International Cartographic Conference. ICA.
   Google Scholar
• Sester, M. (2000). Knowledge acquisition for the automatic interpretation of spatial data. International Journal of Geographical Information Science, 14(1), 1–24. https://doi.org/10.1080/136588100240930
   Web of Science ®Google Scholar
• Shannon, C. E., & Weaver, W. (1949). The mathematical theory of communication (First Edition (US) First Printing ed.). The University of Illinois Press.
   Google Scholar
• Sinha, G., & Mark, D. M. (2010). Toward a foundational ontology of the landscape. In Proceedings of GIScience.
   Google Scholar
• Sinha, G., Mark, D., Kolas, D., Varanka, D., Romero, B. E., Feng, C C., & Sorokine, A. (2014). An ontology design pattern for surface water features. In M. Duckham, E. Pebesma, K. Stewart, & A. U. Frank (Eds.), Geographic information science (pp. 187–203). Springer International Publishing. https://doi.org/10.1007/978-3-319-11593-1_13
   Google Scholar
• Smith, B. (1998). The basic tools of formal ontology. In N. Guarino (Ed.), Formal ontology in information systems. Proceedings of FOIS'98 (pp. 19–28). IOS Press.
   Google Scholar
• Smith, B., & Grenon, P. (2005). The cornucopia of formal-ontological relations. Dialectica, 58(3), 279–296. https://doi.org/10.1111/j.1746-8361.2004.tb00305.x
   Web of Science ®Google Scholar
• Smith, B., & Mark, D. M. (2001). Geographical categories: An ontological investigation. International Journal of Geographical Information Science, 15(7), 591–612. https://doi.org/10.1080/13658810110061199
   Web of Science ®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
• Steinke, T. R. (1987). Eye movement studies in cartography and related fields. Cartographica: The International Journal for Geographic Information and Geovisualization, 24(2), 40–73. https://doi.org/10.3138/J166-635U-7R56-X2L1
   Google Scholar
• Sun, K., Zhu, Y., Pan, P., Hou, Z., Wang, D., Li, W., & Song, J. (2019). Geospatial data ontology: The semantic foundation of geospatial data integration and sharing. Big Earth Data, 3(3), 269–296. https://doi.org/10.1080/20964471.2019.1661662
   Web of Science ®Google Scholar
• Thomson, R. C. (2006). The 'stroke' concept in geographic network; generalization and analysis. In A. Riedl, W. Kainz, & G. A. Elmes (Eds.), Progress in Spatial Data Handling 12th International Symposium on Spatial Data Handling (pp. 681–697).
   Google Scholar
• Thomson, R. C., & Richardson, D. (1999). The ‘good continuation’ principle of perceptual organization applied to the generalization of road networks. In 19th International Cartographic Conference. ICA.
   Google Scholar
• Torres, M., Quintero, R., Moreno-Ibarra, M., Menchaca-Mendez, R., & Guzman, G. (2011). GEONTO-MET: An approach to conceptualizing the geographic domain. International Journal of Geographical Information Science, 25(10), 1633–1657. https://doi.org/10.1080/13658816.2010.539183
   Web of Science ®Google Scholar
• Touya, G. (2007). River network selection based on structure and pattern recognition. In International Cartographic Conference. International Cartographic Association.
   Google Scholar
• Touya, G. (2010). A road network selection process based on data enrichment and structure detection. Transactions in GIS, 14(5), 595–614. Retrieved January 26, 2022, from https://onlinelibrary.wiley.com/doi/abs/10.1111/j.1467-9671.2010.01215.x.
   Web of Science ®Google Scholar
• Touya, G. (2019). Finding the oasis in the desert fog? Understanding multi-scale map reading.
   Google Scholar
• Touya, G. (2020). MapGenOnto: A shared ontology for map generalisation and multi-scale visualisation. In 23rd ICA Workshop on Map Generalisation and Multiple Representation. ICA.
   Google Scholar
• Touya, G. (2021). Multi-criteria geographic analysis for automated cartographic generalization. The Cartographic Journal, 0(0), 1–17. https://doi.org/10.1080/00087041.2020.1858608
   Google Scholar
• Touya, G., Antoniou, V., Christophe, S., & Skopeliti, A. (2017). Production of topographic maps with VGI: Quality management and automation. In G. Foody (Ed.), Mapping and the citizen sensor (pp. 61–91). Ubiquity Press. https://doi.org/10.5334/bbf.d
   Google Scholar
• Touya, G., Bucher, B., Falquet, G., Jaara, K., & Steiniger, S. (2014). Modelling geographic relationships in automated environments. In D. Burghardt, C. Duchêne, & W. Mackaness (Eds.), Abstracting geographic information in a data rich world (pp. 53–82). Springer. https://doi.org/10.1007/978-3-319-00203-3_3
   Google Scholar
• Touya, G., & Dumont, M. (2017). Progressive block graying and landmarks enhancing as intermediate representations between buildings and urban areas. In Proceedings of 20th ICA Workshop on Generalisation and Multiple Representation.
   Google Scholar
• Touya, G., & Lokhat, I. (2020). Deep learning for enrichment of vector spatial databases: Application to highway interchange. ACM Transactions on Spatial Algorithms and Systems, 6(3), 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
• Tripathy, P., Rao, P., Balakrishnan, K., & Malladi, T. (2021). An open-source tool to extract natural continuity and hierarchy of urban street networks. Environment and Planning B: Urban Analytics and City Science, 48(8), 2188–2205. https://doi.org/10.1177/2399808320967680
   Google Scholar
• Uschold, M., & Grüninger, M. (1996). Ontologies: Principles, methods, and applications. The Knowledge Engineering Review, 11(2), 93–136. https://doi.org/10.1017/S0269888900007797
   Web of Science ®Google Scholar
• Wagemans, J., Elder, J. H., Kubovy, M., Palmer, S. E., Peterson, M. A., Singh, M., & von der Heydt, R. (2012a). A century of gestalt psychology in visual perception: I perceptual grouping and figure-ground organization. Psychological Bulletin, 138(6), 1172–1217. https://doi.org/10.1037/a0029333
   PubMed Web of Science ®Google Scholar
• Wagemans, J., Feldman, J., Gepshtein, S., Kimchi, R., Pomerantz, J., P. van der Helm, & Leeuwen, C. (2012b). A century of gestalt psychology in visual perception: II. Conceptual and theoretical foundations. Psychological Bulletin, 138, 1218–1252. https://doi.org/10.1037/a0029334
   PubMed Web of Science ®Google Scholar
• Wang, X., & Burghardt, D. (2019). A mesh-based typification method for building groups with grid patterns. ISPRS International Journal of Geo-Information, 8(4), 168. https://doi.org/10.3390/ijgi8040168
   Web of Science ®Google Scholar
• Wei, Z., Guo, Q., Wang, L., & Yan, F. (2018). On the spatial distribution of buildings for map generalization. Cartography and Geographic Information Science, 45(6), 539–555. https://doi.org/10.1080/15230406.2018.1433068
   Web of Science ®Google Scholar
• Weinman, J., Chen, Z., Gafford, B., Gifford, N., Lamsal, A., & Niehus-Staab, L. (2019). Deep neural networks for text detection and recognition in historical maps. In International Conference on Document Analysis and Recognition (ICDAR) (pp. 902–909). ISSN: (pp. 2379–2140). https://doi.org/10.1109/ICDAR.2019.00149
   Google Scholar
• Weiten, W. (2007). Psychology: Themes and Variations. (7th ed.). Thomson/Wadsworth.
   Google Scholar
• Wolf, E. B. (2009). Ontology-driven generalization of cartographic representations by aggregation and dimensional collapse. In A. Bernstein et al. (Eds.), The Semantic Web – ISWC 2009 (pp. 990–997). Springer. https://doi.org/10.1007/978-3-642-04930-9_65
   Google Scholar
• Xie, Y., Cai, J., Bhojwani, R., Shekhar, S., & Knight, J. (2020). A locally-constrained YOLO framework for detecting small and densely-distributed building footprints. International Journal of Geographical Information Science, 34(4), 777–801. https://doi.org/10.1080/13658816.2019.1624761
   Web of Science ®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., Tong, X., & Liu, Q. (2020). A graph deep learning approach for urban building grouping. Geocarto International, 0(0), 1–24. https://doi.org/10.1080/10106049.2020.1856195
   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, 259–273. https://doi.org/10.1016/j.isprsjprs.2019.02.010
   Web of Science ®Google Scholar
• Yan, J., Guilbert, E., & Saux, E. (2016). An ontology-driven multi-agent system for nautical chart generalization. Cartography and Geographic Information Science, 1–15. https://doi.org/10.1080/15230406.2015.1129648
   Google Scholar
• Yang, M., Jiang, C., Yan, X., Ai, T., Cao, M., & Chen, W. (2022). Detecting interchanges in road networks using a graph convolutional network approach. International Journal of Geographical Information Science, 1–21. https://doi.org/10.1080/13658816.2021.2024195
   Web of Science ®Google Scholar
• Yang, M., Kong, B., Dang, R., & Yan, X. (2022). Classifying urban functional regions by integrating buildings and points-of-interest using a stacking ensemble method. International Journal of Applied Earth Observation and Geoinformation, 108, 102753. https://doi.org/10.1016/j.jag.2022.102753
   Web of Science ®Google Scholar
• Yang, B., Luan, X., & Li, Q. (2010). An adaptive method for identifying the spatial patterns in road networks. Computers, Environment and Urban Systems, 34(1), 40–48. https://doi.org/10.1016/j.compenvurbsys.2009.10.002
   Web of Science ®Google Scholar
• Yarbus, A. L. (1967). Eye movements and vision. (Plenum Press ed.).
   Google Scholar
• Yu, H., Ai, T., Yang, M., Huang, L., & Yuan, J. (2022). A recognition method for drainage patterns using a graph convolutional network. International Journal of Applied Earth Observation and Geoinformation, 107, 102696. https://doi.org/10.1016/j.jag.2022.102696
   Web of Science ®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, C. (2008). An analysis of urban spatial structure using comprehensive prominence of irregular areas. International Journal of Geographical Information Science, 22(6), 675–686. https://doi.org/10.1080/13658810701602245
   Web of Science ®Google Scholar
• Zhang, X., Ai, T., Stoter, J., Kraak, M J., & Molenaar, M. (2013). Building pattern recognition in topographic data: Examples on collinear and curvilinear alignments. GeoInformatica, 17(1), 1–33. https://doi.org/10.1007/s10707-011-0146-3
   Web of Science ®Google Scholar
• Zhang, L., & Guilbert, E. (2013). Automatic drainage pattern recognition in river networks. International Journal of Geographical Information Science, 27(12), 2319–2342. https://doi.org/10.1080/13658816.2013.802794
   Web of Science ®Google Scholar
• Zhang, X., Zhao, X., Molenaar, M., Stoter, J., Kraak, M J., & Ai, T. (2012). Pattern classification approaches to matching building polygons at multiple scales. In ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences (Vol. I-2, pp. 19–24). Copernicus GmbH. ISSN; (pp. 2194–9042). https://doi.org/10.5194/isprsannals-I-2-19-2012
   Google Scholar
• Zhao, R., Ai, T., Yu, W., He, Y., & Shen, Y. (2020). Recognition of building group patterns using graph convolutional network. Cartography and Geographic Information Science, 47(5), 400–417. https://doi.org/10.1080/15230406.2020.1757512
   Web of Science ®Google Scholar
• Żyszkowska, W. (2015). Map perception: Theories and research in the second half of the twentieth century. Polish Cartographical Review, 47(4), 179–190. https://doi.org/10.1515/pcr-2015-0017
   Google Scholar