Proposal for an index of roads and structures for the mapping of non-vegetated urban surfaces using OSM and Sentinel-2 data

•A new easily reproducible methodology for urban mapping is presented.•The methodology allows combined processing of OpenStreeMap and remote sensing data.•A metric named index of roads and structures (IRS) is proposed to map urban areas.•Earth Engine is used as a platform to process the massive dataset.•The IRS is used with NDVI and MNDWI to map urban surface with high accuracy. The use of volunteered geographic information (VGI), such as OpenStreetMap (OSM), to assist in mapping land use and coverage together with remote sensing images is relatively recent. Most studies have used OSM to assist in sample collection for image classification or aggregated vectors of buildings, transport, and land-use and coverage as ancillary data to support mapping refinements. This study proposes a metric called the “index of roads and structures” (IRS) created on the basis of OSM data with the intention of assisting in the mapping of non-vegetated and non-aquatic urban surfaces. IRS thresholds were defined and supplemented with information derived from the Normalized Difference Vegetation Index (NDVI) and the Modified Normalized Difference Water Index (MNDWI) as a way of extending the restriction between urban and non-urban classes and thus achieving better mapping accuracy. To implement this study, multispectral Sentinel-2 images resampled to 10 m on the ground were processed in the Google Earth Engine (GEE). The IRS is a raster file, in which each pixel is associated with the possibility of being inserted in an urban context; thus, the importance of this index as an aid in mapping urban areas is clear. We have demonstrated the possibility of using the IRS to map non-vegetated urban surfaces in Brazil (8.5 million square kilometers) and obtained a very high accuracy of 91.2%.