OpenStreetMap (OSM) is a bottom up community-driven initiative to create a global map of the world. Yet the application of OSM to land use and land cover (LULC) mapping is still largely unexploited due to problems with inconsistencies in the data and harmonization of LULC nomenclatures with OSM. This chapter outlines an automated methodology for creating LULC maps using the nomenclature of two European LULC products: the Urban Atlas (UA) and CORINE Land Cover (CLC). The method is applied to two regions in London and Paris. The results show that LULC maps with a level of detail similar to UA can be obtained for the urban regions, but that OSM has limitations for conversion into the more detailed non-urban classes of the CLC nomenclature. Future work will concentrate on developing additional rules to improve the accuracy of the transformation and building an online system for processing the data.
TopIntroduction And Background
OpenStreetMap (OSM) is a well-known collaborative mapping project that involves volunteers from all over the world in the creation of a free, global geospatial database. With more than 2.8M registered contributors at the time of writing (July 2016) (http://wiki.openstreetmap.org/wiki/Stats), OSM is one of the most popular projects exemplifying the concept of Volunteered Geographic Information or VGI (See et al., 2016). The availability of the OSM database under a fully open license, which allows anyone to use the data freely and produce derived products, has attracted the interest of a multitude of end users such as industry, professionals, governments and humanitarian organizations (Haklay, Antoniou, Basiouka, Soden, & Mooney, 2014; Soden & Palen, 2014; Olteanu-Raimond et al., 2015; Mooney & Minghini, in press). The success of the project has also attracted the attention of the academic community (Jokar Arsanjani, Zipf, Mooney, & Helbich, 2015), and OSM is now considered to be a research topic on its own.
There are a number of factors that can account for the popularity and massive exploitation of OSM data. The large number of contributors over time has ensured that OSM data have reached a high degree of quality. Many studies exist that have compared OSM data with authoritative datasets and showed that they are of a comparable quality, at least in urban areas where more contributors are active (see e.g. Girres & Touya, 2010; Haklay, 2010; Ciepłuch, Jacob, Mooney, & Winstanley, 2010; Ludwig, Voss, & Krause-Traudes, 2011; Fan, Zipf, Fu, & Neis, 2014; Zheng & Zheng, 2014; Brovelli, Minghini, Molinari, & Zamboni, 2016). The OSM database is also extremely rich, as it includes a variety of thematic layers (with attribute information) that are not traditionally available in other official or authoritative datasets. Lastly, the OSM database is constantly updated and enriched by contributors, and each new version is immediately available for use. In contrast, there are a number of problems related to OSM including an inconsistent spatial coverage (see e.g. Haklay, 2010; Zielstra & Zipf, 2010; Hecht, Kunze, & Hahmann, 2013; Fram, Chistopoulou, & Ellul, 2015; Ribeiro & Fonte, 2015; Brovelli, Minghini, & Molinari, 2016) and positional and thematic inconsistencies, where the latter is due to the relative freedom provided to the contributors in defining object attributes (Ballatore & Mooney, 2015).