Using Open Source Geospatial Tools to Find Missing Building Footprints

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Researchers have developed a methodology that uses remotely sensed data measurements to predict completeness in the coverage of building footprint data in OpenStreetMap. The methodology, used to analyze three islands, Haiti, Dominica, and St. Lucia, was able to explain up to 94% of the variation in building footprint coverage. Being able to identify areas with poor GIS data coverage can be used to help prioritize mapping efforts, especially for use in disaster risk management.

OpenStreetMap is a crowdsourced project that uses volunteers to develop and edit geospatial datasets covering the world’s streets, buildings, parks, and other features. Coverage and accuracy of the collected datasets varies between and within countries. Using remotely sensed data from satellite sources, researchers developed a methodology to measure the completeness of OpenStreetMap data for building footprints by identifying “mapping gaps”, areas where footprints have not yet been mapped. To do this, predictors based on remotely sensed data were used to estimate the presence of OpenStreetMap building footprints. These predictors included nighttime lights, vegetation indices, roads, surface texture, and topography. Satellite data used included products from the Suomi National Polar-Orbiting Partnership (Suomi NPP) spacecraft, Sentinal-2, and Sentinel-1. Preprocessing, analysis, and data aggregation was done with Google Earth Engine. Statistical analysis was used to analyze the correlation between predictors and OSM building footprints and a regression with Random Forests evaluated the potential of the remotely sensed variables to predict the presence of building footprints in a given area.

Map A shows OpenStreetMap (OSM)  building footprints overplayed onto an aerial.  Map B shows predicted building footprint by area.  The darker purple areas predict a high frequency of building footprints.  The yellow overlay are the OSM building footprint layer. Source:  Goldblatt, Jones, & Mannix, 2020, CC BY 4.0
Map A shows OpenStreetMap (OSM) building footprints overplayed onto an aerial. Map B shows predicted building footprint by area. The darker purple areas predict a high frequency of building footprints. The yellow overlay are the OSM building footprint layer. Source: Goldblatt, Jones, & Mannix, 2020, CC BY 4.0 license

More detail about the study and the methodology used can be found in the published open access article:

Goldblatt, R., Jones, N., & Mannix, J. (2020). Assessing OpenStreetMap Completeness for Management of Natural Disaster by Means of Remote Sensing: A Case Study of Three Small Island States (Haiti, Dominica and St. Lucia). Remote Sensing12(1), 118. https://doi.org/10.3390/rs12010118

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