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 …

Abstract Generative Adversarial Networks (GANs) are a type of deep neural network that
have achieved many state-of-the-art results for generative tasks. GANs can be useful in the …

Geo-tagged images are publicly available in large quantities, whereas labels such as object
classes are rather scarce and expensive to collect. Meanwhile, contrastive learning has …

Street view imagery (SVI) is increasingly in competition with traditional remote sensing
sources and assuming its domination in myriads of studies, mainly thanks to the …

A healthy acoustic environment is an essential component of sustainable cities. Various
noise monitoring and simulation techniques have been developed to measure and evaluate …

With the rise of GeoAI research, streetscape imagery has received extensive attention due to
its comprehensive coverage, abundant information, and accessibility. However, obtaining a …

Graph neural networks (GNNs) have been extensively used in a wide variety of domains in
recent years. Owing to their power in analyzing graph-structured data, they have become …

The field of Artificial Intelligence (AI) can be roughly divided into two branches: Symbolic
Artificial Intelligence and Connectionist Artificial Intelligence (or so-called Subsymbolic AI) …

Conventional sidewalk studies focused on quantitative analysis of sidewalk walkability at a
large scale which cannot capture the dynamic interactions between the environment and …

Traditional approaches for visual perception and evaluation of river landscapes adopt on-
site surveys or assessments through photographs. The former is expensive, hindering large …