OpenStreetMap Data Quality Assessment via Deep Learning and Remote Sensing Imagery

The number of applications associated with OpenStreetMap (OSM), one of the most famous crowd-sourced projects for volunteered geographic information (VGI), have increased because OSM data is both 'free' and 'up-to-date'. However, limited by the ability of the providers, the quality of the collected data remains a valid concern. This work focuses on how to assess the quality of OSM via deep learning and high-resolution remote imagery. First, considering that high-resolution remote sensing imagery is clear enough for recognizing buildings, we proposed using multi-task deep-convolutional networks to extract buildings in pixel level. The extracted buildings were converted into polygons with geographical coordinates, which were treated as reference data. Then, OSM footprint data were matched with the reference data. Quality was assessed in terms of both positional accuracy and data completeness. Finally, the building footprint data of OSM for the city of Las Vegas, Nevada, USA, were evaluated. The experiments show that the proposed method can assess OSM effectively and accurately. The results show that building extracted by the proposed method can be treated as a new data source for assessing OSM quality and can also be used for urban planning in regions where OSM lacks building data.