UrbanVLP: A Multi-Granularity Vision-Language Pre-Trained Foundation Model for Urban Indicator Prediction

arXiv:2403.16831v1 Announce Type: new
Abstract: Urban indicator prediction aims to infer socio-economic metrics in diverse urban landscapes using data-driven methods. However, prevalent pre-trained models, particularly those reliant on satellite imagery, face dual challenges. Firstly, concentrating solely on macro-level patterns from satellite data may introduce bias, lacking nuanced details at micro levels, such as architectural details at a place. Secondly, the lack of interpretability in pre-trained models limits their utility in providing transparent evidence for urban planning. In response to these …

abstract arxiv bias challenges cs.ai cs.cv data data-driven diverse economic face foundation foundation model however language macro metrics patterns prediction pre-trained models satellite type urban vision