Abstract
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.
Highlights
Since the end of the 20th century, land use and land cover (LULC) maps have been extensively generated at different spatial and temporal scales
We introduced near boundary tag accuracy (NBTA) as a method to evaluate whether the quality of OSM data is suitable for it to be used as a sampling data source for LULC mapping
We found that the training samples’ accuracy was proportional to their proximity to the polygon’s boundary, but this proportionality was somewhat dependent on the area of the polygon
Summary
Since the end of the 20th century, land use and land cover (LULC) maps have been extensively generated at different spatial and temporal scales. Multi-temporal LULC maps can be used to monitor land changes over time, enabling the creation of indicators that can measure changes and support land management. One of the main challenges regarding LULC map production is the difficulty of distinguishing and accurately mapping land attributes. Crowdsourced content from online platforms, accessed, and exchanged by citizens, has emerged as a supplementary data source with significant implications for LULC database production [14]. This nontraditional data source is not necessarily a substitute for official data, but is considered to be complementary [15]
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