BuildingView: Constructing Urban Building Exteriors Databases with Street View Imagery and Multimodal Large Language Mode

• URL: http://arxiv.org/abs/2409.19527v1
• Date: Sun, 29 Sep 2024 03:00:16 GMT
• Title: BuildingView: Constructing Urban Building Exteriors Databases with Street View Imagery and Multimodal Large Language Mode
• Authors: Zongrong Li, Yunlei Su, Chenyuan Zhu, Wufan Zhao,
• Abstract summary: Building Exteriors are increasingly important in urban analytics, driven by advancements in Street View Imagery and its integration with urban research. We propose BuildingView, a novel approach that integrates high-resolution visual data from Google Street View with spatial information from OpenStreetMap via the Overpass API. This research improves the accuracy of urban building exterior data, identifies key sustainability and design indicators, and develops a framework for their extraction and categorization.
• Score: 1.0937094979510213
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Urban Building Exteriors are increasingly important in urban analytics, driven by advancements in Street View Imagery and its integration with urban research. Multimodal Large Language Models (LLMs) offer powerful tools for urban annotation, enabling deeper insights into urban environments. However, challenges remain in creating accurate and detailed urban building exterior databases, identifying critical indicators for energy efficiency, environmental sustainability, and human-centric design, and systematically organizing these indicators. To address these challenges, we propose BuildingView, a novel approach that integrates high-resolution visual data from Google Street View with spatial information from OpenStreetMap via the Overpass API. This research improves the accuracy of urban building exterior data, identifies key sustainability and design indicators, and develops a framework for their extraction and categorization. Our methodology includes a systematic literature review, building and Street View sampling, and annotation using the ChatGPT-4O API. The resulting database, validated with data from New York City, Amsterdam, and Singapore, provides a comprehensive tool for urban studies, supporting informed decision-making in urban planning, architectural design, and environmental policy. The code for BuildingView is available at https://github.com/Jasper0122/BuildingView.

Related papers

• StreetviewLLM: Extracting Geographic Information Using a Chain-of-Thought Multimodal Large Language Model [12.789465279993864]
  Geospatial predictions are crucial for diverse fields such as disaster management, urban planning, and public health. We propose StreetViewLLM, a novel framework that integrates a large language model with the chain-of-thought reasoning and multimodal data sources. The model has been applied to seven global cities, including Hong Kong, Tokyo, Singapore, Los Angeles, New York, London, and Paris.
  arXiv  Detail & Related papers  (2024-11-19T05:15:19Z)
• Identifying every building's function in large-scale urban areas with multi-modality remote-sensing data [5.18540804614798]
  This study proposes a semi-supervised framework to identify every building's function in large-scale urban areas. optical images, building height, and nighttime-light data are collected to describe the morphological attributes of buildings. Results are evaluated by 20,000 validation points and statistical survey reports from the government.
  arXiv  Detail & Related papers  (2024-05-08T15:32:20Z)
• The State of the Art in Visual Analytics for 3D Urban Data [5.056350278679641]
  Urbanization has amplified the importance of three-dimensional structures in urban environments. With the growing availability of 3D urban data, numerous studies have focused on developing visual analysis techniques tailored to the unique characteristics of urban environments. incorporating the third dimension into visual analytics introduces additional challenges in designing effective visual tools to tackle urban data's diverse complexities.
  arXiv  Detail & Related papers  (2024-04-24T16:50:42Z)
• MatrixCity: A Large-scale City Dataset for City-scale Neural Rendering and Beyond [69.37319723095746]
  We build a large-scale, comprehensive, and high-quality synthetic dataset for city-scale neural rendering researches. We develop a pipeline to easily collect aerial and street city views, accompanied by ground-truth camera poses and a range of additional data modalities. The resulting pilot dataset, MatrixCity, contains 67k aerial images and 452k street images from two city maps of total size $28km2$.
  arXiv  Detail & Related papers  (2023-09-28T16:06:02Z)
• Unified Data Management and Comprehensive Performance Evaluation for Urban Spatial-Temporal Prediction [Experiment, Analysis & Benchmark] [78.05103666987655]
  This work addresses challenges in accessing and utilizing diverse urban spatial-temporal datasets. We introduceatomic files, a unified storage format designed for urban spatial-temporal big data, and validate its effectiveness on 40 diverse datasets. We conduct extensive experiments using diverse models and datasets, establishing a performance leaderboard and identifying promising research directions.
  arXiv  Detail & Related papers  (2023-08-24T16:20:00Z)
• The Urban Toolkit: A Grammar-based Framework for Urban Visual Analytics [5.674216760436341]
  The complex nature of urban issues and the overwhelming amount of available data have posed significant challenges in translating these efforts into actionable insights. When analyzing a feature of interest, an urban expert must transform, integrate, and visualize different thematic (e.g., sunlight access, demographic) and physical (e.g., buildings, street networks) data layers. This makes the entire visual data exploration and system implementation difficult for programmers and also sets a high entry barrier for urban experts outside of computer science.
  arXiv  Detail & Related papers  (2023-08-15T13:43:04Z)
• UrbanBIS: a Large-scale Benchmark for Fine-grained Urban Building Instance Segmentation [50.52615875873055]
  UrbanBIS comprises six real urban scenes, with 2.5 billion points, covering a vast area of 10.78 square kilometers. UrbanBIS provides semantic-level annotations on a rich set of urban objects, including buildings, vehicles, vegetation, roads, and bridges. UrbanBIS is the first 3D dataset that introduces fine-grained building sub-categories.
  arXiv  Detail & Related papers  (2023-05-04T08:01:38Z)
• SensatUrban: Learning Semantics from Urban-Scale Photogrammetric Point Clouds [52.624157840253204]
  We introduce SensatUrban, an urban-scale UAV photogrammetry point cloud dataset consisting of nearly three billion points collected from three UK cities, covering 7.6 km2. Each point in the dataset has been labelled with fine-grained semantic annotations, resulting in a dataset that is three times the size of the previous existing largest photogrammetric point cloud dataset.
  arXiv  Detail & Related papers  (2022-01-12T14:48:11Z)
• GANs for Urban Design [0.0]
  The topic investigated in this paper is the application of Generative Adversarial Networks to the design of an urban block. The research presents a flexible model able to adapt to the morphological characteristics of a city.
  arXiv  Detail & Related papers  (2021-05-04T19:50:24Z)
• Methodological Foundation of a Numerical Taxonomy of Urban Form [62.997667081978825]
  We present a method for numerical taxonomy of urban form derived from biological systematics. We derive homogeneous urban tissue types and, by determining overall morphological similarity between them, generate a hierarchical classification of urban form. After framing and presenting the method, we test it on two cities - Prague and Amsterdam.
  arXiv  Detail & Related papers  (2021-04-30T12:47:52Z)
• Towards Semantic Segmentation of Urban-Scale 3D Point Clouds: A Dataset, Benchmarks and Challenges [52.624157840253204]
  We present an urban-scale photogrammetric point cloud dataset with nearly three billion richly annotated points. Our dataset consists of large areas from three UK cities, covering about 7.6 km2 of the city landscape. We evaluate the performance of state-of-the-art algorithms on our dataset and provide a comprehensive analysis of the results.
  arXiv  Detail & Related papers  (2020-09-07T14:47:07Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.