Increasing the detail of European land use/cover data by combining heterogeneous data sets |
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| Authors: | Kon?tantín Rosina Filipe Batista e Silva Pilar Vizcaino Mario Marín Herrera Sérgio Freire Marcello Schiavina |
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| Institution: | 1. European Commission, Joint Research Centre (JRC), Ispra, Italykonstantin.rosina@ec.europa.eu https://orcid.org/0000-0002-4696-1320;3. European Commission, Joint Research Centre (JRC), Ispra, Italyhttps://orcid.org/0000-0002-8752-6464;4. European Commission, Joint Research Centre (JRC), Ispra, Italyhttps://orcid.org/0000-0002-7508-1568;5. European Commission, Joint Research Centre (JRC), Ispra, Italyhttps://orcid.org/0000-0003-4177-9471;6. European Commission, Joint Research Centre (JRC), Ispra, Italyhttps://orcid.org/0000-0003-2282-701X;7. European Commission, Joint Research Centre (JRC), Ispra, Italyhttps://orcid.org/0000-0003-3399-3400 |
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| Abstract: | ABSTRACTData on land use and land cover (LULC) are a vital input for policy-relevant research, such as modelling of the human population, socioeconomic activities, transportation, environment, and their interactions. In Europe, CORINE Land Cover has been the only data set covering the entire continent consistently, but with rather limited spatial detail. Other data sets have provided much better detail, but either have covered only a fraction of Europe (e.g. Urban Atlas) or have been thematically restricted (e.g. Copernicus High Resolution Layers). In this study, we processed and combined diverse LULC data to create a harmonised, ready-to-use map covering 41 countries. By doing so, we increased the spatial detail (from 25 to one hectare) and the thematic detail (by seven additional LULC classes) compared to the CORINE Land Cover. Importantly, we decomposed the class ‘Industrial and commercial units’ into ‘Production facilities’, ‘Commercial/service facilities’ and ‘Public facilities’ using machine learning to exploit a large database of points of interest. The overall accuracy of this thematic breakdown was 74%, despite the confusion between the production and commercial land uses, often attributable to noisy training data or mixed land uses. Lessons learnt from this exercise are discussed, and further research direction is proposed. |
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| Keywords: | Data fusion land use land cover machine learning points of interest |
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https://orcid.org/0000-0002-4696-1320;3. European Commission, Joint Research Centre (JRC), Ispra, Italy