title: OpenCity3D: What do Vision-Language Models know about Urban
    Environments?
summary: 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
paper_authors._id: 67e3d39948da72bf6f3d27f7
paper_authors.name: Valentin Bieri
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-04-10T15:45:37.266Z
paper_authors._id: 67d42d13d70098db698e8453
paper_authors.avatarUrl: /avatars/0956711c9028edf871e41d10a3464bc9.svg
paper_authors.fullname: Valentin Bieri
paper_authors.type: user
paper_authors.user: bieriv
paper_authors._id: 67e3d39948da72bf6f3d27f8
paper_authors.name: Marco Zamboni
paper_authors._id: 67e3d39948da72bf6f3d27f9
paper_authors.name: Nicolas S. Blumer
paper_authors._id: 67e3d39948da72bf6f3d27fa
paper_authors.name: Qingxuan Chen
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-26T20:44:10.272Z
paper_authors._id: 64462645866784561a882802
paper_authors.avatarUrl: /avatars/4c08f6f60a82b7292c91fccb2c8bea37.svg
paper_authors.fullname: Qingxuan Chen
paper_authors.type: user
paper_authors.user: LUC1O
paper_authors._id: 67e3d39948da72bf6f3d27fb
paper_authors.name: Francis Engelmann
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-18T08:47:51.883Z
paper_authors._id: 671a7160e45d92e304e82f45
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/-trHIg97qX0dE9T4UYycc.png
paper_authors.fullname: Francis Engelmann
paper_authors.type: user
paper_authors.user: francisengelmann
paper_title: OpenCity3D: What do Vision-Language Models know about Urban
    Environments?
paper_summary: 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
paper_ai_summary: OpenCity3D leverages vision-language models for high-level urban analytics tasks using 3D reconstructions from aerial imagery, demonstrating zero-shot and few-shot capabilities.
paper_ai_keywords: vision-language models VLMs 3D scene understanding 3D reconstructions aerial imagery population density estimation building age classification property price prediction crime rate assessment noise pollution evaluation zero-shot capabilities few-shot capabilities language-driven urban analytics