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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Albert Adolf and Izabela Karsznia
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
Bérénice Le Mao, Iga Ajdacka, Izabela Karsznia, Guillaume Touya, and Albert Adolf
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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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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.
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Population data are essential for studies on human–nature relationships, disaster or environmental health. Several global and continental gridded population data have been produced but have never been systematically compared. This article fills this gap and critically compares these gridded population datasets. Through the lens of the
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