Ullah, T.; Lautenbach, S.; Herfort, B.; Reinmuth, M.; Schorlemmer, D. Assessing Completeness of OpenStreetMap Building Footprints Using MapSwipe. ISPRS Int. J. Geo-Inf.2023, 12, 143.
Ullah, T.; Lautenbach, S.; Herfort, B.; Reinmuth, M.; Schorlemmer, D. Assessing Completeness of OpenStreetMap Building Footprints Using MapSwipe. ISPRS Int. J. Geo-Inf. 2023, 12, 143.
Ullah, T.; Lautenbach, S.; Herfort, B.; Reinmuth, M.; Schorlemmer, D. Assessing Completeness of OpenStreetMap Building Footprints Using MapSwipe. ISPRS Int. J. Geo-Inf.2023, 12, 143.
Ullah, T.; Lautenbach, S.; Herfort, B.; Reinmuth, M.; Schorlemmer, D. Assessing Completeness of OpenStreetMap Building Footprints Using MapSwipe. ISPRS Int. J. Geo-Inf. 2023, 12, 143.
Abstract
Natural hazards threaten millions of people all over the world. To address the risk, exposure and vulnerability models with high resolution data are essential. However, in many areas of the world, exposure models are rather coarse and aggregated over large areas. Although OpenStreetMap (OSM) offers great potential to assess risk at a detailed building-by-building level, completeness of OSM building footprints is still heterogeneous. We present an approach to close this gap by means of crowdsourcing 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 at four regions. Our results show that the crowdsourced approach yields a reasonable classification performance of the completeness of OSM building footprints. Nevertheless, this 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.
Keywords
OpenStreetMap; MapSwipe; data completeness; disaster management; exposure, Volunteered Geographic Information; data quality
Subject
Environmental and Earth Sciences, Remote Sensing
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
