Horizon 2020 LandSense project was concluded successful. Please find a selection of the produced publications and deliverables here. The project has enabled our group to pursue quality aspects of voluntarily collected geo information data and to ramp up efforts related to OSMlanduse. Together with the University of Nottingham (Giles Foody) and Institut national de l’information géographique et forestière France (Ana-Maria Olteanu-Raimond) Heidelberg group was leading the quality assurance and quality control of the largest Horizon 2020 Citicen Science Project, providing accuracy, performance and benchmark services for six citizen science pilot projects.
One of the key aspects for the quality estimation was the collection of reference data for OSMlanduse where you can find the latest edition of our mapathon campaigns here. For OSMlanduse and its integration with our mapathon data as well as additional auxiliary remote sensing data we’re using our own OSM big data processing platform an in-house labor of love, handcrafted in Java and Python, using only hand picked, organic, free license libraries. We are currently concluding results and making produced models and data validation data collected publicly available.
We are thankful for the excellent collaboration and among our partners and are thankful for the extended collaboration within follow up projects.
Long, G., Schultz, M., Olteanu-Raimond, A.-M. (2020): Good practice guidelines, protocols and benchmarking standards for quality assurance. H2020 LandSense. https://zenodo.org/record/4133626
Capellan, S., Stickler, T., Birli, B., Olteanu-Raimond, A.-M., Schultz, M., Mrkajic, V., Moorthy, I. (2020): Engagement action plans and campaign strategies for LandSense demonstration cases I. H2020 LandSense. https://zenodo.org/record/3670135
Moorthy, I., See, L., Banko, G., Capellan, S., Mrkajic, V., Olteanu-Raimond, A.-M., Schrammeijer, B., Schultz, M., Batič, M., Fritz, S. (2020): LandSense: Coupling citizen science and earth observation data to promote environmental monitoring. H2020 LandSense. https://zenodo.org/record/4146846
Stickler, T., Raimond, A.-M., Schrammeijer, B., Schultz, M. (2020): Cost Reduction and data conflation in monitoring land change. H2020 LandSense. https://zenodo.org/record/3670904
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
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., 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.
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