Quality assessment of OpenStreetMap data using trajectory mining

Abstract

OpenStreetMap (OSM) data are widely used but their reliability is still variable. Many contributors to OSM have not been trained in geography or surveying and consequently their contributions, including geometry and attribute data inserts, deletions, and updates, can be inaccurate, incomplete, inconsistent, or vague. There are some mechanisms and applications dedicated to discovering bugs and errors in OSM data. Such systems can remove errors through user-checks and applying predefined rules but they need an extra control process to check the real-world validity of suspected errors and bugs. This paper focuses on finding bugs and errors based on patterns and rules extracted from the tracking data of users. The underlying idea is that certain characteristics of user trajectories are directly linked to the type of feature. Using such rules, some sets of potential bugs and errors can be identified and stored for further investigations.

Correcting rural building annotations in OpenStreetMap using convolutional neural networks

“…Several works proposed methods that can be useful to improve the quality of the OSM data, both for attribute classification and positional inaccuracies. Authors in [17] detect errors in OSM annotations of roads using patterns extracted from GPS tracking data. For instance, indoor corridors wrongly classified as tunnels can be detected using tracked trajectories of cars and pedestrians.…”

“…But if the quality of OSM data has been judged sufficient for urban areas [19], the same does not hold in rural areas, especially because of the lower update rate and the drop in the number of volunteers out of cities. By analyzing available OSM data in rural areas, we observed that the annotations performed by the volunteers suffer from three main issues, mostly due to infrequent imagery updates and incomplete/inaccurate volunteer annotations [17,20]:…”

Spatial information and the legibility of urban form: Big data in urban morphology

“…OSMnx allows users to download spatial data from OSM for any study site boundary in the world, automatically construct it into a model that conforms to urban design and transportation planning conventions, and then analyze and visualize it (Boeing, 2017). OSM is a valuable source of geospatial data as it has worldwide coverage, generally high quality, and an active collaborative user community (Barron et al, 2014;Basiri et al, 2016;Corcoran et al, 2013;Girres and Touya, 2010;Haklay, 2010;Jokar Arsanjani et al, 2015;Neis et al, 2011;Over et al, 2010;Zielstra et al, 2013).…”

Making tourist guidance systems more intelligent, adaptive and personalised using crowd sourced movement data

“…The level of robustness of output knowledge is strongly correlated with input sample size. In this regard, for successful pattern recognition and rule extraction there are some rulesof-thumb (Basiri et al 2016b). …”