A new study has been published in the international open access journal Geo-spatial Information Science (GSIS, Taylor & Francis), that explores the land use/land cover (LULC) separability by the machine-generated and user-generated Flickr photo tags (i.e. the auto-tags and the user-tags, respectively), based on an authoritative LULC dataset for San Diego County in the United States. The analysis was conducted both quantitatively and semantically. This provided some useful information for the LULC separation.
Ten types of LULCs were derived from the authoritative dataset. It was observed that certain types of the reclassified LULCs had abundant tags (e.g. the parks) or a high tag density (e.g. the commercial lands), compared with the less populated ones (e.g. the agricultural lands). Certain highly weighted terms of the tags derived based on a term frequency–inverse document frequency weighting scheme were helpful for identifying specific types of the LULCs, especially for the commercial recreation lands (e.g. the zoos). However, given the 10 sets of tags retrieved from the corresponding 10 types of LULCs, one set of tags (all the tags located at one specific type of the LULCs) could not fully delineate the corresponding LULC due to semantic overlaps, according to a latent semantic analysis.
It was further observed that the pairwise semantic similarities of the 10 sets of weighted user-tag terms were generally lower than those of the 10 sets of weighted auto-tag terms. Some further and future work is discussed in the full article.
(2019): An exploratory analysis of usability of Flickr tags for land use/land cover attribution, Geo-spatial Information Science, DOI: 10.1080/10095020.2018.1560044
Some 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 see also: http://osmlanduse.org
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.
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.
Jacobs C. and A. Zipf (2017): Completeness of Citizen Science Biodiversity Data from a Volunteered Geographic Information Perspective. Geo-Spatial information Science, 20(1): 3-13. Taylor & Francis. DOI: 10.1080/10095020.2017.1288424.
Yan, Y., M. Eckle, C.-L. Kuo, B. Herfort, H. Fan and A. Zipf (2017): Monitoring and Assessing Post-Disaster Tourism Recovery Using Geotagged Social Media Data. International Journal of Geo-Information, ISPRS IJGI. 6(5), 144; doi:10.3390/ijgi6050144.
Kuo, C.-L., T.C. Chan, I.-C. Fan, A. Zipf (2018): Efficient Method for POI/ROI Discovery Using Flickr Geotagged Photos. ISPRS Int. J. Geo-Inf. 2018, 7(3), 121; doi:10.3390/ijgi7030121.
Some further work related to VGI and Social Media Analytics and also LULC can be found on in our VGI related special issues of the journal GSIS, i.e.: Crowdsourcing for Urban Geoinformatics (2018) and Volunteered Geographic Information Analytics (2017).