The value of OpenStreetMap Historical Contributions as a Source of Sampling Data for Multi‑temporal Land Use/Cover Maps

• Published in: ISPRS international journal of geo-information 2019-03, Vol.8 (3), p.116
• Main Authors: Viana, Cláudia M., Encalada, Luis, Rocha, Jorge
• Format: Article
• Language: English
• Subjects: Accuracy; Boundaries; Completeness; Data; Datasets; Digital mapping; Evaluation; Geography; Land cover; Land use; land use land cover; Mapping; OpenStreetMap (OSM); Rural areas; Sampling; sampling data; Training; Urban areas; Volunteered Geographic Information (VGI)
• ISSN: 2220-9964; 2220-9964
• DOI: 10.3390/ijgi8030116

OpenStreetMap (OSM) is a free, open-access Volunteered geographic information (VGI) platform that has been widely used over the last decade as a source for Land Use Land Cover (LULC) mapping and visualization. However, it is known that the spatial coverage and accuracy of OSM data are not evenly distributed across all regions, with urban areas being likelier to have promising contributions (in both quantity and quality) than rural areas. The present study used OSM data history to generate LULC datasets with one-year timeframes as a way to support regional and rural multi-temporal LULC mapping. We evaluated the degree to which the different OSM datasets agreed with two existing reference datasets (CORINE Land Cover and the official Portuguese Land Cover Map). We also evaluated whether our OSM dataset was of sufficiently high quality (in terms of both completeness accuracy and thematic accuracy) to be used as a sampling data source for multi-temporal LULC maps. In addition, we used the near boundary tag accuracy criterion to assesses the fitness of the OSM data for producing training samples, with promising results. For each annual dataset, the completeness ratio of the coverage area for the selected study area was low. Nevertheless, we found high thematic accuracy values (ranged from 77.3% to 91.9%). Additionally, the training samples thematic accuracy improved as they moved away from the features’ boundaries. Features with larger areas (> 10 ha), e.g., Agriculture and Forest, had a steadily positive correlation between training samples accuracy and distance to feature boundaries