Recently, Michael Schulz from GIScience Heidelberg gave a talk at the colloquium of Prof. Carl Baierkuhnlein at the University of Bayreuth. During his talk he presented the results on OpenStreetMap usage targeting humanitarian applications, healthy and green routing based on openrouteservice and ecological analysis or the global climate protection map. OSM is harnessed for an abundance of applications and the OSM land use prototype (see osmlanduse.org) was presented in detail. There, human knowledge was injected into a robust machine learning method highlighting the potential of closer machine human data interaction. Work in progress, stay tuned for further updates!
Related earlier work:
Schultz, M., Voss, J., Auer, M., Carter, S., and Zipf, A. (2017): Open land cover from OpenStreetMap and remote sensing. International Journal of Applied Earth Observation and Geoinformation, 63, pp. 206-213. DOI: 10.1016/j.jag.2017.07.014
Novack, T.; Wang, Z.; Zipf, A. (2018): A System for Generating Customized Pleasant Pedestrian Routes Based on OpenStreetMap Data. Sensors 2018, 18, 3794.
Chen, J., Zipf, A. (2017): Deep Learning with Satellite Images and Volunteered Geographic Information. In: Karimi, H. A. and Karimi, B. (eds.): Geospatial Data Science: Techniques and Applications. Taylor & Francis.
Jokar Arsanjani, J., Mooney, P., Zipf, A., Schauss, A., (2015): Quality assessment of the contributed land use information from OpenStreetMap versus authoritative datasets. In: Jokar Arsanjani, J., Zipf, A., Mooney, P., Helbich, M., OpenStreetMap in GIScience: experiences, research, applications. ISBN:978-3-319-14279-1, PP. 37-58, Springer Press.
Dorn, H., Törnros, T. & Zipf, A. (2015): Quality Evaluation of VGI using Authoritative Data – A Comparison with Land Use Data in Southern Germany. ISPRS International Journal of Geo-Information. Vol 4(3), pp. 1657-1671, doi: 10.3390/ijgi4031657
Jokar Arsanjani, J., Helbich, M., Bakillah, M., Hagenauer, J., & Zipf, A. (2013). Toward mapping land-use patterns from volunteered geographic information. International Journal of Geographical Information Science, 2264-2278. DOI:10.1080/13658816.2013.800871.
Scholz, S., Knight, P., Eckle, M., Marx, S., Zipf, A. (2018): Volunteered Geographic Information for Disaster Risk Reduction: The Missing Maps Approach and Its Potential within the Red Cross and Red Crescent Movement. Remote Sens., 10(8), 1239, doi: 10.3390/rs10081239.
Novack, T., J. Voss, M. Schultz, A. Zipf (2018): Associating OpenStreetMap tags to CORINE land-cover classes using text and semantic similarity measures. VGI-ALIVE Workshop at AGILE 2018. Lund, Sweden.
Chen, J., Y. Zhou, A. Zipf and H. Fan (2018): Deep Learning from Multiple Crowds: A Case Study of Humanitarian Mapping. IEEE Transactions on Geoscience and Remote Sensing (TGRS). 1-10. https://doi.org/10.1109/TGRS.2018.2868748
