5 papers
CITYREP: A Unified Benchmark for Urban Representations Across Cities, Tasks, and Modalities
Junyuan Liu, Xinglei Wang, Zichao Zeng +5
Urban representation learning encodes complex urban environments into general-purpose embeddings for diverse downstream tasks and emerging urban foundation models. However, current…
Beyond AlphaEarth: Toward Human-Centered Geospatial Foundation Models via POI-Guided Contrastive Learning
Junyuan Liu, Quan Qin, Guangsheng Dong +4
Recent geospatial foundation models (GFMs) produce spatially extensive representations of the Earth's surface that capture rich physical and environmental patterns. Among them, the…
Into the Unknown: Applying Inductive Spatial-Semantic Location Embeddings for Predicting Individuals' Mobility Beyond Visited Places
Xinglei Wang, Tao Cheng, Stephen Law +6
Predicting individuals' next locations is a core task in human mobility modelling, with wide-ranging implications for urban planning, transportation, public policy and personalised…
Enriching Location Representation with Detailed Semantic Information
Junyuan Liu, Xinglei Wang, Tao Cheng
Spatial representations that capture both structural and semantic characteristics of urban environments are essential for urban modeling. Traditional spatial embeddings often prior…
Multimodal Contrastive Learning of Urban Space Representations from POI Data
Xinglei Wang, Tao Cheng, Stephen Law +3
Existing methods for learning urban space representations from Point-of-Interest (POI) data face several limitations, including issues with geographical delineation, inadequate spa…