CREATING A LAND USE/LAND COVER DICTIONARY BASED ON MULTIPLE PAIRS OF OSM AND REFERENCE DATASETS

Abstract

Abstract. OpenStreetMap (OSM) can supply useful information to improve land use/land cover (LULC) mapping. A dictionary is needed to convert each OSM tag into a LULC class. However, such a dictionary was mostly created subjectively or with only one pair of OSM and reference datasets. As a result, the existing dictionaries may not be applicable to other study areas. This study designed four measures: sample count, average area percentage, sample ratio and average maximum percentage; and used multiple pair of OSM and reference datasets to create a dictionary. 50 pan-European metropolitans were involved for testing and 1409 different OSM tags were found. We further found that: (1) Only a small proportion of OSM tags play a decisive role for LULC mapping. (2) An OSM tag may correspond to multiple different LULC classes, but the issue that which and how different LULC classes correspond to each OSM tag can be determined. Moreover, not only the proposed dictionary is useful for various applications, e.g., producing LULC maps and assessing the quality of an OSM dataset, but also the approach can be applicable to different study areas and/or LULC datasets.