Extracting and understanding urban areas of interest using geotagged photos
By Yingjie Hu, Song Gao, Krzysztof Janowicz, Bailang Yu, Wenwen Li, Sathya Prasad. Computers, Environment and Urban Systems, 2015
327 citations recorded by the source corpus.
Subfields: Geography, Planning and Development, Transportation, Signal Processing
View Paper: https://www.semanticscholar.org/paper/b8d4c171ef2d575f12ee49dade943ef5e83ef0bb
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Citations in the corpus
- Exploring spatio-temporal changes of city inbound tourism flow: The case of Shanghai, China (2020, methodology): “For example, Hu et al. (2015) use Flickr data to extract AOI from different cities using DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm, Kisilevich, Mansmann, and Keim (2010) use Flickr data to solve the nonidentity of numbers between photos and visitors using…”
- A human-machine adversarial scoring framework for urban perception assessment using street-view images (2019, background): “2017) and thus could e ff ectively re fl ect visual scenery that is seen in daily life (Hu et al .”
- Analyzing the correlation between visual space and residents' psychology in Wuhan, China using street-view images and deep-learning technique (2021, unknown): “At the method level, because the street view can truly reflect the city scene, it is often used to measure the visual physical characteristics of the city [28,29].”
- Extracting urban functional regions from points of interest and human activities on location‐based social networks (2017, background): “…data (e.g., trajectories from mobile phones), POI data, and social media feeds to study the spatial and social structure of urban environments (Hu et al., 2015; Jiang, Alves, Rodrigues, Ferreira, & Pereira, 2015; Liu et al., 2015; McKenzie et al., 2015; Pei et al., 2014; Steiger, Westerholt,…”
- Social Media data: Challenges, opportunities and limitations in urban studies (2019, background): “Firstly, LSBN data are generated by millions of people from different countries throughout the world (Hu et al., 2015) and, as the number of social network users grows, so does the amount, quality and usability of data.”
- Passive crowdsourcing of social media in environmental research: A systematic map (2019, background): “, 2017) and areas of interest for more sustainable urban development (Garcia-Palomares et al., 2015; Hu et al., 2015), including patterns of use of urban green spaces (Roberts, 2017).”