eprintid: 14543 rev_number: 11 eprint_status: archive userid: 353 dir: disk0/00/01/45/43 datestamp: 2017-04-24 06:59:18 lastmod: 2021-08-27 17:28:51 status_changed: 2017-04-24 06:59:18 type: article metadata_visibility: show creators_name: Fonte, C.C. creators_name: Minghini, M. creators_name: Patriarca, J. creators_name: Antoniou, V. creators_name: See, L. creators_name: Skopeliti, A. creators_id: 8571 creators_orcid: 0000-0002-2665-7065 title: Generating Up-to-Date and Detailed Land Use and Land Cover Maps Using OpenStreetMap and GlobeLand30 ispublished: pub divisions: prog_esm keywords: land use/land cover mapping; OpenStreetMap; volunteered geographic information; Urban Atlas; GlobeLand30 abstract: With the opening up of the Landsat archive, global high resolution land cover maps have begun to appear. However, they often have only a small number of high level land cover classes and they are static products, corresponding to a particular period of time, e.g., the GlobeLand30 (GL30) map for 2010. The OpenStreetMap (OSM), in contrast, consists of a very detailed, dynamically updated, spatial database of mapped features from around the world, but it suffers from incomplete coverage, and layers of overlapping features that are tagged in a variety of ways. However, it clearly has potential for land use and land cover (LULC) mapping. Thus the aim of this paper is to demonstrate how the OSM can be converted into a LULC map and how this OSM-derived LULC map can then be used to first update the GL30 with more recent information and secondly, enhance the information content of the classes. The technique is demonstrated on two study areas where there is availability of OSM data but in locations where authoritative data are lacking, i.e., Kathmandu, Nepal and Dar es Salaam, Tanzania. The GL30 and its updated and enhanced versions are independently validated using a stratified random sample so that the three maps can be compared. The results show that the updated version of GL30 improves in terms of overall accuracy since certain classes were not captured well in the original GL30 (e.g., water in Kathmandu and water/wetlands in Dar es Salaam). In contrast, the enhanced GL30, which contains more detailed urban classes, results in a drop in the overall accuracy, possibly due to the increased number of classes, but the advantages include the appearance of more detailed features, such as the road network, that becomes clearly visible. date: 2017-04-22 date_type: published publisher: MDPI Publishing ; International Society for Photogrammetry and Remote Sensing (ISPRS) id_number: 10.3390/ijgi6040125 creators_browse_id: 276 full_text_status: public publication: ISPRS International Journal of Geo-Information volume: 6 number: 4 refereed: TRUE issn: 2220-9964 projects: Harnessing the power of crowdsourcing to improve land cover and land-use information (CROWDLAND, FP7 617754) projects: A Citizen Observatory and Innovation Marketplace for Land Use and Land Cover Monitoring (LANDSENSE, H2020 689812) coversheets_dirty: FALSE fp7_project: yes fp7_project_id: info:eu-repo/grantAgreement/EC/FP7/617754/EU//CROWDLAND; info:eu-repo/grantAgreement/EC/H2020/689812/EU//LANDSENSE fp7_type: info:eu-repo/semantics/article access_rights: info:eu-repo/semantics/openAccess citation: Fonte, C.C., Minghini, M., Patriarca, J., Antoniou, V., See, L. ORCID: https://orcid.org/0000-0002-2665-7065 , & Skopeliti, A. (2017). Generating Up-to-Date and Detailed Land Use and Land Cover Maps Using OpenStreetMap and GlobeLand30. ISPRS International Journal of Geo-Information 6 (4) 10.3390/ijgi6040125 . document_url: https://pure.iiasa.ac.at/id/eprint/14543/1/ijgi-06-00125.pdf