OpenCity3D: What do Vision-Language Models know about Urban
  Environments?
Authors: name: Valentin Bieri, name: Marco Zamboni, name: Nicolas S. Blumer, name: Qingxuan Chen, name: Francis Engelmann

Vision-language models (VLMs) show great promise for 3D scene understanding
but are mainly applied to indoor spaces or autonomous driving, focusing on
low-level tasks like segmentation. This work expands their use to urban-scale
environments by leveraging 3D reconstructions from multi-view aerial imagery.
We propose OpenCity3D, an approach that addresses high-level tasks, such as
population density estimation, building age classification, property price
prediction, crime rate assessment, and noise pollution evaluation. Our findings
highlight OpenCity3D's impressive zero-shot and few-shot capabilities,
showcasing adaptability to new contexts. This research establishes a new
paradigm for language-driven urban analytics, enabling applications in
planning, policy, and environmental monitoring. See our project page:
opencity3d.github.io