A new abstract has been accepted at EGU about Citizen Science for Big Earth Observation Data Analytics in Land Use and Land Cover Change Monitoring: From Scope to Future Directions
in the Session Citizen Science in the Era of Big Data at European Geosciences Union (EGU) General Assembly 2018 Vienna | Austria | 8–13 April 2018
Land use and cover changes (LUCC) monitoring is a basic need for understanding sociopolitical, ethical and economic aspects of local to global size. Several techniques, auxiliary data sets
and remote sensing tools provide mechanisms to track LUC over large areas. This combination is a time-consuming task considering the growing numbers of satellite imagery. Yet, scientists still lack of ways to organize thousands of downloaded files and analyze the high variability of their spectral and spatial attributes.
Studies moved toward a new architecture of analysis called big Earth Observation (EO) data analytics. This allows to develop and adapt methods with minimal reworking for generating and sharing LUCC results in a collaborative and replicable manner. Scientists seek an automated and generalized method to mitigate the burden on generating LUCC classification maps. Yet it is hard to achieve accurate and efficient results without extensive human supervision. Human visual image interpretation and collection of in situ data are still the simplest and qualitative methods. A manual approach allows non-specialists to collaborate with LUCC information through field-based and online contributions.
The challenge here is how to promote a more active scientific citizenship approach with big EO data analytics for LUCC monitoring. With that in mind, we aim to discuss the scope and future directions on how to collect, organize, incorporate and improve society’s judgements (lots of citizens) into generated automated LUCC maps. See you at EGU 2018!
Related work:
http://www.geog.uni-heidelberg.de/gis/land_sense.html
OSMlanduse.org Our WebService providing LULC information derived from OpenStreetMap (and Remote Sensing).
OSMatrix Our WebService providing quality related metadata maps for OpenStreetMap
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.
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.
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
Törnros, T., Dorn, H., Hahmann, S., and Zipf, A. (2015): Uncertainties of completeness measures in OpenStreetMap – A Case Study for buildings in a medium-sized German city, ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., II-3/W5, 353-357, doi:10.5194/isprsannals-II-3-W5-353-2015.