Crowdsourcing Landforms for Open GIS Enrichment

Abstract

Open Source Geographical Information Systems, such as OpenStreetMap (OSM), offer a valuable alternative to proprietary solutions for the development of voluntary environment monitoring systems. However, the quantity and quality of information stored in such systems must be carefully evaluated and the contributions of volunteers must be boosted by means of effective engagement methods. This paper reports the results of the assessment of the quality and quantity of OpenStreetMap mountain information: different types of information and world regions have different gaps and improvement requirements. To address this issue, we propose a hybrid approach, in which an open Digital Elevation Model data set is processed with a heuristic algorithm to find candidate mountain information and uncertainty in the automatically extracted candidates is reduced by means of voluntary expert crowdsourcing. The improvement of landform information (not only about mountains, but also about orography and hydrography in general) can support the development of environment monitoring applications.