Multimodal Contrastive Learning of Urban Space Representations from POI Data

TL;DR

This paper introduces CaLLiPer, a multimodal contrastive learning model that creates detailed, semantically rich urban space representations from POI data, improving land use and socioeconomic predictions while being more efficient.

Contribution

The paper presents CaLLiPer, a novel contrastive learning approach that directly embeds urban spaces using multimodal POI data, bypassing complex data construction and negative sampling.

Findings

Achieves 5-15% improvement in land use classification accuracy.

Reduces training time compared to existing methods.

Effectively captures spatial and semantic variations in urban environments.

Abstract

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 urban spaces into vector representations that can capture the spatial and semantic distribution of urban environment. This model leverages a multimodal contrastive learning objective, aligning location embeddings with textual POI descriptions, thereby bypassing the need for complex training corpus construction and negative sampling. We validate CaLLiPer's effectiveness by applying it to learning urban space representations in London, UK, where it demonstrates 5-15% improvement in predictive performance for land use classification and socioeconomic mapping tasks compared to state-of-the-art methods. Visualisations of the learned representations further illustrate our model's advantages in capturing spatial variations in urban semantics with high accuracy and fine resolution. Additionally, CaLLiPer achieves reduced training time, showcasing its efficiency and scalability. This work provides a promising pathway for scalable, semantically rich urban space representation learning that can support the development of geospatial foundation models. The implementation code is available at https://github.com/xlwang233/CaLLiPer.