Deep Learning for Cross-Domain Data Fusion in Urban Computing: Taxonomy,
  Advances, and Outlook
Xingchen Zou Yibo Yan Xixuan Hao Yuehong Hu Haomin Wen Erdong Liu Junbo Zhang Yong Li Tianrui Li Yu Zheng Yuxuan Liang
As cities continue to burgeon, Urban Computing emerges as a pivotal
discipline for sustainable development by harnessing the power of cross-domain
data fusion from diverse sources (e.g., geographical, traffic, social media,
and environmental data) and modalities (e.g., spatio-temporal, visual, and
textual modalities). Recently, we are witnessing a rising trend that utilizes
various deep-learning methods to facilitate cross-domain data fusion in smart
cities. To this end, we propose the first survey that systematically reviews
the latest advancements in deep learning-based data fusion methods tailored for
urban computing. Specifically, we first delve into data perspective to
comprehend the role of each modality and data source. Secondly, we classify the
methodology into four primary categories: feature-based, alignment-based,
contrast-based, and generation-based fusion methods. Thirdly, we further
categorize multi-modal urban applications into seven types: urban planning,
transportation, economy, public safety, society, environment, and energy.
Compared with previous surveys, we focus more on the synergy of deep learning
methods with urban computing applications. Furthermore, we shed light on the
interplay between Large Language Models (LLMs) and urban computing, postulating
future research directions that could revolutionize the field. We firmly
believe that the taxonomy, progress, and prospects delineated in our survey
stand poised to significantly enrich the research community. The summary of the
comprehensive and up-to-date paper list can be found at
https://github.com/yoshall/Awesome-Multimodal-Urban-Computing.