This study explored 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 ...This study explored 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.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.展开更多
基金This work is supported by the European Union LandSense project with the project title“A Citizen Observatory and Innovation Marketplace for Land Use and Land Cover Monitoring”,instrument Horizon 2020 and call identifier SC5-17-2015,demonstrating the concept of citizen observatories as an innovation action.
文摘This study explored 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.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.
