date: 2023-03-28T08:36:37Z
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pdf:docinfo:title: Assessing Completeness of OpenStreetMap Building Footprints Using MapSwipe
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Keywords: OpenStreetMap; MapSwipe; data completeness; disaster management; exposure; volunteered geographic information; data quality
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subject: Natural hazards threaten millions of people all over the world. To address this risk, exposure and vulnerability models with high resolution data are essential. However, in many areas of the world, exposure models are rather coarse and are aggregated over large areas. Although OpenStreetMap (OSM) offers great potential to assess risk at a detailed building-by-building level, the completeness of OSM building footprints is still heterogeneous. We present an approach to close this gap by means of crowd-sourcing based on the mobile app MapSwipe, where volunteers swipe through satellite images of a region collecting user feedback on classification tasks. For our application, MapSwipe was extended by a completeness feature that allows to classify a tile as ?no building?, ?complete? or ?incomplete?. To assess the quality of the produced data, the completeness feature was applied to four regions. The MapSwipe-based assessment was compared with an intrinsic approach to quantify completeness and with the prediction of an existing model. Our results show that the crowd-sourced approach yields a reasonable classification performance of the completeness of OSM building footprints. Results showed that the MapSwipe-based assessment produced consistent estimates for the case study regions while the other two approaches showed a higher variability. Our study also revealed that volunteers tend to classify nearly completely mapped tiles as ?complete?, especially in areas with a high OSM building density. Another factor that influenced the classification performance was the level of alignment of the OSM layer with the satellite imagery. 
dc:creator: Tahira Ullah, Sven Lautenbach, Benjamin Herfort, Marcel Reinmuth and Danijel Schorlemmer
dcterms:created: 2023-03-28T08:23:52Z
Last-Modified: 2023-03-28T08:36:37Z
dcterms:modified: 2023-03-28T08:36:37Z
dc:format: application/pdf; version=1.7
title: Assessing Completeness of OpenStreetMap Building Footprints Using MapSwipe
Last-Save-Date: 2023-03-28T08:36:37Z
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pdf:docinfo:keywords: OpenStreetMap; MapSwipe; data completeness; disaster management; exposure; volunteered geographic information; data quality
pdf:docinfo:modified: 2023-03-28T08:36:37Z
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dc:title: Assessing Completeness of OpenStreetMap Building Footprints Using MapSwipe
modified: 2023-03-28T08:36:37Z
cp:subject: Natural hazards threaten millions of people all over the world. To address this risk, exposure and vulnerability models with high resolution data are essential. However, in many areas of the world, exposure models are rather coarse and are aggregated over large areas. Although OpenStreetMap (OSM) offers great potential to assess risk at a detailed building-by-building level, the completeness of OSM building footprints is still heterogeneous. We present an approach to close this gap by means of crowd-sourcing based on the mobile app MapSwipe, where volunteers swipe through satellite images of a region collecting user feedback on classification tasks. For our application, MapSwipe was extended by a completeness feature that allows to classify a tile as ?no building?, ?complete? or ?incomplete?. To assess the quality of the produced data, the completeness feature was applied to four regions. The MapSwipe-based assessment was compared with an intrinsic approach to quantify completeness and with the prediction of an existing model. Our results show that the crowd-sourced approach yields a reasonable classification performance of the completeness of OSM building footprints. Results showed that the MapSwipe-based assessment produced consistent estimates for the case study regions while the other two approaches showed a higher variability. Our study also revealed that volunteers tend to classify nearly completely mapped tiles as ?complete?, especially in areas with a high OSM building density. Another factor that influenced the classification performance was the level of alignment of the OSM layer with the satellite imagery. 
pdf:docinfo:subject: Natural hazards threaten millions of people all over the world. To address this risk, exposure and vulnerability models with high resolution data are essential. However, in many areas of the world, exposure models are rather coarse and are aggregated over large areas. Although OpenStreetMap (OSM) offers great potential to assess risk at a detailed building-by-building level, the completeness of OSM building footprints is still heterogeneous. We present an approach to close this gap by means of crowd-sourcing based on the mobile app MapSwipe, where volunteers swipe through satellite images of a region collecting user feedback on classification tasks. For our application, MapSwipe was extended by a completeness feature that allows to classify a tile as ?no building?, ?complete? or ?incomplete?. To assess the quality of the produced data, the completeness feature was applied to four regions. The MapSwipe-based assessment was compared with an intrinsic approach to quantify completeness and with the prediction of an existing model. Our results show that the crowd-sourced approach yields a reasonable classification performance of the completeness of OSM building footprints. Results showed that the MapSwipe-based assessment produced consistent estimates for the case study regions while the other two approaches showed a higher variability. Our study also revealed that volunteers tend to classify nearly completely mapped tiles as ?complete?, especially in areas with a high OSM building density. Another factor that influenced the classification performance was the level of alignment of the OSM layer with the satellite imagery. 
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pdf:docinfo:creator: Tahira Ullah, Sven Lautenbach, Benjamin Herfort, Marcel Reinmuth and Danijel Schorlemmer
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creator: Tahira Ullah, Sven Lautenbach, Benjamin Herfort, Marcel Reinmuth and Danijel Schorlemmer
meta:author: Tahira Ullah, Sven Lautenbach, Benjamin Herfort, Marcel Reinmuth and Danijel Schorlemmer
dc:subject: OpenStreetMap; MapSwipe; data completeness; disaster management; exposure; volunteered geographic information; data quality
meta:creation-date: 2023-03-28T08:23:52Z
created: Tue Mar 28 10:23:52 CEST 2023
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meta:keyword: OpenStreetMap; MapSwipe; data completeness; disaster management; exposure; volunteered geographic information; data quality
Author: Tahira Ullah, Sven Lautenbach, Benjamin Herfort, Marcel Reinmuth and Danijel Schorlemmer
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