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GIScience News Blog » Blog Archive » OSMlanduse European Union validation effort EuroSDR conference 11/24/2020

During the EuroSDR workshop we will present our OSMlanduse product (earlier post) to the land use (LU) and land cover community (LC) and highlight class accuracies and a benchmark comparison towards existing national authoritative products. Accuracy estimated to be presented are based on more than 7k reference points collected in the past month through a permanently open validation campaign. The campaign was featured on Octobers the EuroRegions week and GeoNet MRN meetup.

The mapathon comes in four themes: nature, urban, agriculture or expert. While the expert campaign may be addressed exclusively by application professionals the themes nature, urban, agriculture can be done by anyone that is enthusiastic about geography. Contribute here and choose your flavor.

If you have not registered for EuroSDR you can directly join the validation event held on 24.11.2020, 14:00 – 15:30 by contacting Michael Schultz to receive login credentials. Given that the validation effort is open permanently visit it directly here.

Related Work:

  • Li, H.; Ghamisi, P.; Rasti, B.; Wu, Z.; Shapiro, A.; Schultz, M.; Zipf, A. A Multi-Sensor Fusion Framework Based on Coupled Residual Convolutional Neural Networks. Remote Sensing. 2020, 12, 2067. DOI: https://doi.org/10.3390/rs12122067
  • 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.
  • Raimond, A.-M., See L., Schultz, M., Foody, G., Jolivet, L., Le Bris, A., Meneroux, Y., Liu, L., Poupee, M., Gombert, M. (2020): Use of Automated Change Detection and VGI for Identifying and Validating Urban Land Use Change. Remote Sensing
  • Schultz, M. (2018): Definition of citizen-observed and authoritative data collection requirements for LandSense demonstration cases. H2020 LandSense. https://doi.org/10.5281/zenodo.3670341
  • Wu, Zhaoyan, Li, Hao, & Zipf, Alexander. (2020). From Historical OpenStreetMap data to customized training samples for geospatial machine learning. In proceedings of the Academic Track at the State of the Map 2020 Online Conference, July 4-5 2020. DOI: http://doi.org/10.5281/zenodo.3923040
  • Yan, Y., Schultz, M., Zipf, A. (2019): An exploratory analysis of usability of Flickr tags for land use/land cover attribution, Geo-spatial Information Science (GSIS), Taylor & Francis. https://doi.org/10.1080/10095020.2018.1560044
  • 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., (eds) OpenStreetMap in GIScience: experiences, research, applications. ISBN:978-3-319-14279-1, pp. 37-51, Springer Press.
  • 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 (IJGIS). Taylor & Francis. DOI: 10.1080/13658816.2013.800871.
  • 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
  • Li, H., Herfort, B., Huang, W., Zia, M. and Zipf, A. (2020):
    Exploration of OpenStreetMap Missing Built-up Areas using Twitter Hierarchical Clustering and Deep Learning in Mozambique. ISPRS Journal of Photogrammetry and Remote Sensing. https://doi.org/10.1016/j.isprsjprs.2020.05.007

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