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

This is a Preprint and has not been peer reviewed. The published version of this Preprint is available: https://doi.org/10.1016/j.jag.2022.102791. This is version 2 of this Preprint.

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Authors

Eduardo Felix Justiniano, Edimilson Rodrigues Santos Junior, Breno Malheiros Melo, João Victor Nascimento Siqueira, Rúbia Gomes Morato, Marcel Fantin, Julio Cesar Pedrassoli, Marcos Roberto Martines, Fernando Shinji Kawakubo

Abstract

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 urban surfaces in Brazil (8.5 million square kilometers) and obtained a very high accuracy of 93.2%.

DOI

https://doi.org/10.31223/X53S6J

Subjects

Remote Sensing

Keywords

OpenStreetMap, volunteered geographic information, Google Earth Engine, Urban Environment, image classification

Dates

Published: 2022-01-28 17:52

Last Updated: 2022-02-04 00:24

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License

CC BY Attribution 4.0 International

Additional Metadata

Data Availability (Reason not available):
https://github.com/edujusti/IRS