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arXiv · 2503.16776

OpenCity3D: What do Vision-Language Models know about Urban Environments?

Valentin Bieri·Marco Zamboni·Nicolas S. Blumer·Qingxuan Chen·Francis Engelmann
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Abstract

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

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Valentin Bieri, Marco Zamboni, Nicolas S. Blumer, Qingxuan Chen, Francis Engelmann. 2025-03-21. OpenCity3D: What do Vision-Language Models know about Urban Environments?. https://arxiv.org/abs/2503.16776

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