Multimodal Contrastive Learning of Urban Space Representations from POI
Data
Multimodal Contrastive Learning of Urban Space Representations from POI
Data
Existing methods for learning urban space representations from Point-of-Interest (POI) data face several limitations, including issues with geographical delineation, inadequate spatial information modelling, underutilisation of POI semantic attributes, and computational inefficiencies. To address these issues, we propose CaLLiPer (Contrastive Language-Location Pre-training), a novel representation learning model that directly embeds continuous …