Assessing Completeness of OpenStreetMap Building Footprints Using MapSwipe
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) of...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-03-01
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| Series: | ISPRS International Journal of Geo-Information |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2220-9964/12/4/143 |
| _version_ | 1797605205115666432 |
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| author | Tahira Ullah Sven Lautenbach Benjamin Herfort Marcel Reinmuth Danijel Schorlemmer |
| author_facet | Tahira Ullah Sven Lautenbach Benjamin Herfort Marcel Reinmuth Danijel Schorlemmer |
| author_sort | Tahira Ullah |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-11T04:57:48Z |
| format | Article |
| id | doaj.art-1ea006c0a4db4eae9f8f0f43097b3e62 |
| institution | Directory Open Access Journal |
| issn | 2220-9964 |
| language | English |
| last_indexed | 2024-03-11T04:57:48Z |
| publishDate | 2023-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | ISPRS International Journal of Geo-Information |
| spelling | doaj.art-1ea006c0a4db4eae9f8f0f43097b3e622023-11-17T19:31:10ZengMDPI AGISPRS International Journal of Geo-Information2220-99642023-03-0112414310.3390/ijgi12040143Assessing Completeness of OpenStreetMap Building Footprints Using MapSwipeTahira Ullah0Sven Lautenbach1Benjamin Herfort2Marcel Reinmuth3Danijel Schorlemmer4GIScience Research Group, Heidelberg University, Im Neuenheimer Feld 368, 69126 Heidelberg, GermanyGIScience Research Group, Heidelberg University, Im Neuenheimer Feld 368, 69126 Heidelberg, GermanyGIScience Research Group, Heidelberg University, Im Neuenheimer Feld 368, 69126 Heidelberg, GermanyHeidelberg Institute for Geoinformation Technology gGmbH, Schloss-Wolfsbrunnenweg 33, 69118 Heidelberg, GermanyGFZ German Research Center for Geosciences, Telegrafenberg, 14473 Potsdam, GermanyNatural 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.https://www.mdpi.com/2220-9964/12/4/143OpenStreetMapMapSwipedata completenessdisaster managementexposurevolunteered geographic information |
| spellingShingle | Tahira Ullah Sven Lautenbach Benjamin Herfort Marcel Reinmuth Danijel Schorlemmer Assessing Completeness of OpenStreetMap Building Footprints Using MapSwipe ISPRS International Journal of Geo-Information OpenStreetMap MapSwipe data completeness disaster management exposure volunteered geographic information |
| title | Assessing Completeness of OpenStreetMap Building Footprints Using MapSwipe |
| title_full | Assessing Completeness of OpenStreetMap Building Footprints Using MapSwipe |
| title_fullStr | Assessing Completeness of OpenStreetMap Building Footprints Using MapSwipe |
| title_full_unstemmed | Assessing Completeness of OpenStreetMap Building Footprints Using MapSwipe |
| title_short | Assessing Completeness of OpenStreetMap Building Footprints Using MapSwipe |
| title_sort | assessing completeness of openstreetmap building footprints using mapswipe |
| topic | OpenStreetMap MapSwipe data completeness disaster management exposure volunteered geographic information |
| url | https://www.mdpi.com/2220-9964/12/4/143 |
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