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Title: Generating Up-to-Date and Detailed Land Use and Land Cover Maps Using OpenStreetMap and GlobeLand30
Authors: Fonte, Cidália 
Minghini, Marco 
Patriarca, Joaquim 
Antoniou, Vyron 
See, Linda 
Skopeliti, Andriani 
Keywords: land use/land cover mapping; OpenStreetMap; Volunteered geographic information; Urban Atlas; GlobeLand30
Issue Date: Apr-2017
Publisher: ISPRS Int. J. Geo-Inf
Project: EU COST Action TD1202 ‘Mapping and the Citizen Sensor’ ( 
EU COST Action IC1203 ‘European Network Exploring Research into Geospatial Information Crowdsourcing: software and methodologies for harnessing geographic information from the crowd (ENERGIC)’ ( 
FCT project grant UID/MULTI/00308/2013 do INESC Coimbra 
EU-funded ERC CrowdLand project (No. 617754) 
Horizon2020 LandSense project (No. 689812) 
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.
DOI: 10.3390/ijgi6040125
Rights: openAccess
Appears in Collections:I&D INESCC - Artigos em Revistas Internacionais

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