6 papers
HypLTSF: A Hyperbolic Geometric View of Multi-Scale Hierarchies for Long-Term Time Series Forecasting
Namwoo Kim, Hyungryul Baik, Yoonjin Yoon
Multi-scale modeling has become an effective approach for long-term time series forecasting, capturing temporal patterns that range from fine-grained local dynamics to coarse globa…
Synergistic Fusion of Topological Structure and Temporal Semantics of Mobility for Urban Region Embedding
Namwoo Kim, Jeeyun Chang, Kanghoon Lee +1
Urban region embeddings have shown promising results in diverse urban sensing tasks such as crime, income, and service-call prediction. Recent methods improve representation qualit…
How Well Do Vision--Language Models Understand Cities? A Comparative Study on Spatial Reasoning from Street-View Images
Juneyoung Ro, Namwoo Kim, Yoonjin Yoon
Effectively understanding urban scenes requires fine-grained spatial reasoning about objects, layouts, and depth cues. However, how well current vision-language models (VLMs), pret…
USE-LFA: A Data-Driven Framework for UAM Site Evaluation using Latent Factor Analysis
Sungmin Sohn, Namwoo Kim, Mark Hansen +1
Urban air mobility (UAM) introduces new challenges for infrastructure planning, requiring data driven approaches for sustainable site selection. This study proposes USE-LFA (Urban…
TopoCL: Topological Contrastive Learning for Time Series
Namwoo Kim, Hyungryul Baik, Yoonjin Yoon
Universal time series representation learning is challenging but valuable in real-world applications such as classification, anomaly detection, and forecasting. Recently, contrasti…
MobiCLR: Mobility Time Series Contrastive Learning for Urban Region Representations
Namwoo Kim, Takahiro Yabe, Chanyoung Park +1
Recently, learning effective representations of urban regions has gained significant attention as a key approach to understanding urban dynamics and advancing smarter cities. Exist…