Data 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.

The MOLAND (MOnitoring LANd use/cover Dynamics) and the Urban Atlas (UA) are two well-known, detailed data sets of land use/cover information focused on European cities. The MOLAND data set contains a unique time series of land use/cover changes for more than thirty urban areas covering a wide temporal window (1950 to late 1990s). The UA is a more recent project that mapped land use/cover for more than 300 cities for the year 2006. In this paper we discuss the integration of both data sets in order to produce a single geo-database covering an extended time series spanning from 1950 to 2006. The different cartographic specifications of the two input data sets, particularly in terms of spatial and thematic resolution, impeded a straightforward integration. A methodology was therefore set up to harmonize the two data sets and merge them into a consistent and comparable geo-database that can be easily queried and used for both visual and analytical purposes. The usefulness of the newly integrated geo-database was demonstrated by some exploratory analyses of the urban dynamics that occurred during the time span of the combined geo-database. We further 305 306 R. R. Barranco et al. discuss the role of time series of land use/cover data and draw recommendations and directions for future work and research.

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