Supervised learning for automated feature selection in road network generalization
Izabela Karsznia
Department of Geoinformatics, Cartography and Remote Sensing, Faculty of Geography and Regional Studies, University of Warsaw, Poland
Albert Adolf
Department of Geoinformatics, Cartography and Remote Sensing, Faculty of Geography and Regional Studies, University of Warsaw, Poland
Stefan Leyk
Department of Geography, University of Colorado Boulder, USA
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Abstr. Int. Cartogr. Assoc., 10, 1, https://doi.org/10.5194/ica-abs-10-1-2025, https://doi.org/10.5194/ica-abs-10-1-2025, 2025
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Iga Ajdacka and Izabela Karsznia
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Abstr. Int. Cartogr. Assoc., 7, 2, https://doi.org/10.5194/ica-abs-7-2-2024, https://doi.org/10.5194/ica-abs-7-2-2024, 2024
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Johannes H. Uhl, Dominic Royé, Keith Burghardt, José A. Aldrey Vázquez, Manuel Borobio Sanchiz, and Stefan Leyk
Earth Syst. Sci. Data, 15, 4713–4747, https://doi.org/10.5194/essd-15-4713-2023, https://doi.org/10.5194/essd-15-4713-2023, 2023
Short summary
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Historical, fine-grained geospatial datasets on built-up areas are rarely available, constraining studies of urbanization, settlement evolution, or the dynamics of human–environment interactions to recent decades. In order to provide such historical data, we used publicly available cadastral building data for Spain and created a series of gridded surfaces, measuring age, physical, and land-use-related features of the built environment in Spain and the evolution of settlements from 1900 to 2020.
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Abstr. Int. Cartogr. Assoc., 6, 125, https://doi.org/10.5194/ica-abs-6-125-2023, https://doi.org/10.5194/ica-abs-6-125-2023, 2023
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Johannes H. Uhl, Stefan Leyk, Caitlin M. McShane, Anna E. Braswell, Dylan S. Connor, and Deborah Balk
Earth Syst. Sci. Data, 13, 119–153, https://doi.org/10.5194/essd-13-119-2021, https://doi.org/10.5194/essd-13-119-2021, 2021
Short summary
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Fine-grained geospatial data on the spatial distribution of human settlements are scarce prior to the era of remote-sensing-based Earth observation. In this paper, we present datasets derived from a large, novel building stock database, enabling the spatially explicit analysis of 200 years of land development in the United States at an unprecedented spatial and temporal resolution. These datasets greatly facilitate long-term studies of socio-environmental systems in the conterminous USA.