Summary of Buildingview: Constructing Urban Building Exteriors Databases with Street View Imagery and Multimodal Large Language Mode, by Zongrong Li and Yunlei Su and Hongrong Wang and Wufan Zhao
BuildingView: Constructing Urban Building Exteriors Databases with Street View Imagery and Multimodal Large Language Mode
by Zongrong Li, Yunlei Su, Hongrong Wang, Wufan Zhao
First submitted to arxiv on: 29 Sep 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computer Vision and Pattern Recognition (cs.CV); Computers and Society (cs.CY)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes BuildingView, a novel approach integrating Google Street View and OpenStreetMap data to create accurate and detailed urban building exterior databases. By leveraging multimodal Large Language Models (LLMs) for annotation, the authors aim to identify critical indicators for energy efficiency, environmental sustainability, and human-centric design. The methodology involves a systematic literature review, building and Street View sampling, and annotation using the ChatGPT-4O API. The resulting database is validated with data from New York City, Amsterdam, and Singapore, providing a comprehensive tool for urban studies supporting informed decision-making in urban planning, architectural design, and environmental policy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research helps cities make better decisions about building design and sustainability. It combines pictures of buildings taken by Google Street View with information from OpenStreetMap to create a detailed database of building exteriors. The goal is to identify what makes some buildings more energy-efficient or environmentally friendly, and how they can be designed to be more people-friendly. The researchers used special computer models to help them categorize the data, which was then tested in several cities around the world. The result is a valuable tool that can be used by city planners, architects, and policymakers to make more informed decisions. |
