The Value of OpenStreetMap Historical Contributions as a Source of Sampling Data for Multi-Temporal Land Use/Cover Maps

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

Long-Term Satellite Image Time-Series for Land Use/Land Cover Change Detection Using Refined Open Source Data in a Rural Region

“…However, we decided to a priori reduce the polygon areas of the COS maps, using a QGIS Python plugin called "Buffer by Percentage". We followed this approach, because we knew training samples had low accuracies when their proximity to the polygon's boundaries increased [50]. As we were using the shape and area of each COS map polygon to produce the training samples, we sought to ensure a relationship of magnitude between the MMUs of both data sources (the COS map and the Landsat imagery) [51].…”

“…However, generating training samples from a LULC map (such as the COS) produced by a governmental institution that we considered a reliable source of information still had associated problems. For example, following a previous work [50] we decided to reduce the COS map polygon areas before the generation of random points since the authors of the cited work concluded that the boundaries of the polygons of such sources can have low accuracy. In addition, bias in the representation of the samples can exist if there is no prior knowledge regarding the major LULC types present in the study area.…”

Introductory Chapter: Geographic Information Systems and Science

“…Additionally, Crampton et al [61] evaluated the possible influences of big data on critical geography using exploratory methods to overcome some of the limitations related to the usage of VGI, and Viana et al [62] accessed the value of OpenStreetMap (OSM) data for land use land cover (LULC) cartography. One may state that neogeography is bringing cartographic and GIS expertise to the common citizens [19].…”

An approach for establishing correspondence between OpenStreetMap and reference datasets for land use and land cover mapping

“…In similar work, Fonte and Martinho () assessed the applicability of OSM datasets as references for validating LULC maps created through classifying RS data, while Johnson and Lizuka () integrated OSM data and Landsat satellite data for mapping LULC patterns within the Laguna de Bay area (Philippines); the highest overall accuracy achieved was 84.0%. Schultz, Voss, Auer, Carter, and Zipf () also combined OSM and RS data for LULC mapping, using the former to extract training samples, while Viana, Encalada, and Rocha () concluded that OSM historical data can be used as a source of sampling data for multi‐temporal LULC maps. Estima and Painho () investigated the suitability of an OSM POI dataset for producing a LULC map, while Cheng et al () used both road and POI data in OSM to identify urban land, concluding that the overall accuracy of urban land extracted from OSM data was significantly higher than that from nighttime light data.…”