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[HTML][HTML] Unsupervised machine learning in urban studies: A systematic review of applications
Unsupervised learning (UL) has a long and successful history in untangling the complexity
of cities. As the counterpart of supervised learning, it discovers patterns from intrinsic data …
of cities. As the counterpart of supervised learning, it discovers patterns from intrinsic data …
Location based services: ongoing evolution and research agenda
We are now living in a mobile information era, which is fundamentally changing science and
society. Location Based Services (LBS), which deliver information depending on the location …
society. Location Based Services (LBS), which deliver information depending on the location …
Revealing spatio-temporal evolution of urban visual environments with street view imagery
The visual landscape plays a pivotal role in urban planning and healthy cities. Recent
studies of visual evaluation focus on either objective or subjective approach, while …
studies of visual evaluation focus on either objective or subjective approach, while …
Knowledge and topology: A two layer spatially dependent graph neural networks to identify urban functions with time-series street view image
With the rise of GeoAI research, streetscape imagery has received extensive attention due to
its comprehensive coverage, abundant information, and accessibility. However, obtaining a …
its comprehensive coverage, abundant information, and accessibility. However, obtaining a …
Understanding house price appreciation using multi-source big geo-data and machine learning
Understanding house price appreciation benefits place-based decision makings and real
estate market analyses. Although large amounts of interests have been paid in the house …
estate market analyses. Although large amounts of interests have been paid in the house …
Urban function classification at road segment level using taxi trajectory data: A graph convolutional neural network approach
Extracting hidden information from human mobility patterns is one of the long-standing
challenges of urban studies. In addition, exploring the relationship between urban functional …
challenges of urban studies. In addition, exploring the relationship between urban functional …
Deep learning-based remote and social sensing data fusion for urban region function recognition
Urban region function recognition is key to rational urban planning and management. Due to
the complex socioeconomic nature of functional land use, recognizing urban region function …
the complex socioeconomic nature of functional land use, recognizing urban region function …
Learning urban region representations with POIs and hierarchical graph infomax
We present the hierarchical graph infomax (HGI) approach for learning urban region
representations (vector embeddings) with points-of-interest (POIs) in a fully unsupervised …
representations (vector embeddings) with points-of-interest (POIs) in a fully unsupervised …
Improved population map** for China using remotely sensed and points-of-interest data within a random forests model
Remote sensing image products (eg brightness of nighttime lights and land cover/land use
types) have been widely used to disaggregate census data to produce gridded population …
types) have been widely used to disaggregate census data to produce gridded population …
Portraying the spatial dynamics of urban vibrancy using multisource urban big data
Understanding urban vibrancy aids policy-making to foster urban space and therefore has
long been a goal of urban studies. Recently, the emerging urban big data and urban analytic …
long been a goal of urban studies. Recently, the emerging urban big data and urban analytic …