Abstract
ABSTRACTThis 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.
Highlights
Human species reshapes 50% of earth’s surface and continues to accelerate land transformations (Hooke, Martín-Duque, and Pedraza 2012; Waters et al 2016)
This study explored the land use/land cover (LULC) separability by the machine-generated and user-generated Flickr photo tags, based on an authoritative LULC dataset for San Diego County in the United States
Regarding the number of tags per square kilometer for both the auto-tags and the user-tags, the largest tag quantity appears on the commercial lands (C), followed by the commercial recreation lands (CR), and the agricultural lands (A) are associated with a very small tag density (Figure 3(b))
Summary
Human species reshapes 50% of earth’s surface and continues to accelerate land transformations (Hooke, Martín-Duque, and Pedraza 2012; Waters et al 2016). Incorrect thematic information (tagging), overlapping features, and spatial and temporal inhomogeneities are significant shortcomings affecting OSM credibility (Bégin, Devillers, and Roche 2013; Fonte et al 2016; Jokar Arsanjani et al 2015; Jokar Arsanjani and Vaz 2015) Data completeness is another challenge, which was found to depend on contributor activities (Foody et al 2013; Neis and Zipf 2012). Multiple tags provided by different users were found to compromise legend harmonization which was necessary for comparison with other LULC products (Dorn, Törnros, and Zipf 2015; Estima and Painho 2013a; Jokar Arsanjani and Vaz 2015)
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