Seeing the City or Recognizing the Place? What Street-View Imagery Adds Beyond Existing Urban Data in VLM Urban Sensing
The study compared predictions from Vision-Language Models (VLMs) using street-view imagery against existing urban data across seven attributes like road damage and building type. Findings show that image value depends heavily on the attribute; images are best for visible changes, while existing records excel at stable attributes. The research suggests collecting imagery should be guided by where visual information is sparse compared to current maps.