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
This research investigated the impact of social-networking service posts on the formation of image structure of cities,focusing on the spatial distribution of images and their content similarity.It aimed to delineate ...This research investigated the impact of social-networking service posts on the formation of image structure of cities,focusing on the spatial distribution of images and their content similarity.It aimed to delineate the image structure of cities created by numerous users,moving beyond traditional qualitative methods towards a more quantitative and objective approach with big data.Taking central Tokyo as an example,this study extracted geotagged image data of 33 major railway station areas from Flickr’s API(Application Programming Interface).Four coverage types of viewpoint distribution,namely planar,intersecting linear,linear,and nodal,were identified,reflecting the unique urban structures respectively.Further investigation of the image contents,primarily consisting of“urban landscape”and“landscape/street trees,”showed that such contents significantly influenced the formationof the image structure of cities.The study concluded that asthe number of photo posts increased and the representativeviewpoints concentrated,the digital information received by usersbecame more homogeneous,leading to strongly stereotypedimages of urban landscapes.These findings highlight the role ofsocial networking services in shaping perceptions of the urbanenvironment and provide insights into the image structure of citiesas formed by digital information.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.
文摘This research investigated the impact of social-networking service posts on the formation of image structure of cities,focusing on the spatial distribution of images and their content similarity.It aimed to delineate the image structure of cities created by numerous users,moving beyond traditional qualitative methods towards a more quantitative and objective approach with big data.Taking central Tokyo as an example,this study extracted geotagged image data of 33 major railway station areas from Flickr’s API(Application Programming Interface).Four coverage types of viewpoint distribution,namely planar,intersecting linear,linear,and nodal,were identified,reflecting the unique urban structures respectively.Further investigation of the image contents,primarily consisting of“urban landscape”and“landscape/street trees,”showed that such contents significantly influenced the formationof the image structure of cities.The study concluded that asthe number of photo posts increased and the representativeviewpoints concentrated,the digital information received by usersbecame more homogeneous,leading to strongly stereotypedimages of urban landscapes.These findings highlight the role ofsocial networking services in shaping perceptions of the urbanenvironment and provide insights into the image structure of citiesas formed by digital information.
