18 citations · 21 across the 8 of their papers we have counts for
4 papers · 1 filter
UrbanFusion: Stochastic Multimodal Fusion for Contrastive Learning of Robust Spatial Representations
Dominik J. Mühlematter, Lin Che, Ye Hong +2
Forecasting urban phenomena such as housing prices and public health indicators requires the effective integration of various geospatial data. Current methods primarily utilize tas…
GeOT: A spatially explicit framework for evaluating spatio-temporal predictions
Nina Wiedemann, Théo Uscidda, Martin Raubal
When predicting observations across space and time, the spatial layout of errors impacts a model's real-world utility. For instance, in bike sharing demand prediction, error patter…
Context-aware knowledge graph framework for traffic speed forecasting using graph neural network
Yatao Zhang, Yi Wang, Song Gao +1
Human mobility is intricately influenced by urban contexts spatially and temporally, constituting essential domain knowledge in understanding traffic systems. While existing traffi…
Counterfactual Explanations for Deep Learning-Based Traffic Forecasting
Rushan Wang, Yanan Xin, Yatao Zhang +2
Deep learning models are widely used in traffic forecasting and have achieved state-of-the-art prediction accuracy. However, the black-box nature of those models makes the results…