18 citations · 21 across the 8 of their papers we have counts for
9 papers
Modeling Subjective Urban Perception with Human Gaze
Lin Che, Xi Wang, Marc Pollefeys +3
Urban perception describes how people subjectively evaluate urban environments, shaping how cities are experienced and understood. Existing computational approaches primarily model…
HolmeSketcher: Generative 3D Sketch Mapping for Spatial Reconstruction in Crime Scene Investigation
Tianyi Xiao, Yizi Chen, Sidi Wu +3
Sketch mapping is widely used in crime scene investigation (CSI) to document, interpret, and communicate spatial information. However, it is typically performed on 2D media, which…
Bidirectional yet asymmetric causality between urban systems and traffic dynamics in 30 cities worldwide
Yatao Zhang, Ye Hong, Song Gao +1
Understanding how urban systems and traffic dynamics co-evolve is crucial for advancing sustainable and resilient cities. However, their bidirectional causal relationships remain u…
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…
Deep Generative Model for Human Mobility Behavior
Ye Hong, Yatao Zhang, Konrad Schindler +1
Understanding and modeling human mobility is central to challenges in transport planning, sustainable urban design, and public health. Despite decades of effort, simulating individ…
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…
Bike network planning in limited urban space
Nina Wiedemann, Christian Nöbel, Lukas Ballo +2
The lack of cycling infrastructure in urban environments hinders the adoption of cycling as a viable mode for commuting, despite the evident benefits of (e-)bikes as sustainable, e…
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…