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
this https URL

城市计算是一门关键学科，通过整合来自不同领域和模式（如时空、视觉和文本模式）的多源（如地理、交通、社交媒体和环境数据）跨领域数据融合，可为可持续发展提供支持。最近，我们观察到在智能城市中利用各种深度学习方法促进跨领域数据融合的趋势逐渐增长。为此，我们提出了第一份系统综述，对专为城市计算定制的基于深度学习的数据融合方法的最新进展进行了系统回顾。与以往综述相比，我们更关注深度学习方法与城市计算应用之间的协同作用。此外，我们还展望了大型语言模型（LLMs）与城市计算之间的相互影响，并提出了可能会改变该领域的未来研究方向。我们坚信，我们的综述所勾勒出的分类系统、进展情况和前景将会显著丰富研究社区。