Quality of crowdsourced geospatial building information: A global assessment of OpenStreetMap attributes

Biljecki, Filip; Chow, Yoong Shin; Lee, Kay

doi:10.1016/j.buildenv.2023.110295

Cited by 48 publications

(15 citation statements)

References 148 publications

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“…We extract street networks, points of interest, and building footprint data from OpenStreetMap (OSM). OSM is an open collaborative mapping platform that hosts the most comprehensive global crowdsourced collection of geospatial data 58 . The data from OpenStreetMap (OSM) is released under the Open Data Commons Open Database License (ODbL).…”

Section: Methodsmentioning

confidence: 99%

A Global Feature-Rich Network Dataset of Cities and Dashboard for Comprehensive Urban Analyses

Yap,

Biljecki

2023

Sci Data

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Urban network analytics has become an essential tool for understanding and modeling the intricate complexity of cities. We introduce the Urbanity data repository to nurture this growing research field, offering a comprehensive, open spatial network resource spanning 50 major cities in 29 countries worldwide. Our workflow enhances OpenStreetMap networks with 40 + high-resolution indicators from open global sources such as street view imagery, building morphology, urban population, and points of interest, catering to a diverse range of applications across multiple fields. We extract streetscape semantic features from more than four million street view images using computer vision. The dataset’s strength lies in its thorough processing and validation at every stage, ensuring data quality and consistency through automated and manual checks. Accompanying the dataset is an interactive, web-based dashboard we developed which facilitates data access to even non-technical stakeholders. Urbanity aids various GeoAI and city comparative analyses, underscoring the growing importance of urban network analytics research.

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Section: Methodsmentioning

confidence: 99%

A Global Feature-Rich Network Dataset of Cities and Dashboard for Comprehensive Urban Analyses

Yap,

Biljecki

2023

Sci Data

Self Cite

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“…In general, creating large-scale datasets is expensive, difficult, and likely to give rise to errors [28]. There have been significant advances in the method of continuously collecting building data in terms of both quantity and contents [29], spurred by the diversity of stakeholders and their types. Today, the building data collected by volunteers as well as the government, companies, and academia are utilized in research [30][31][32][33][34][35].…”

Section: Crowdsourced Data Collectionmentioning

confidence: 99%

Workflow for Window Composition Detection to Aid Energy-Efficient Renovation in Low-Income Housing in Korea

Lee

2024

Buildings

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Enhancing the efficiency of windows is important for improving the energy efficiency of buildings. The Korean government has performed numerous building renovation projects to reduce greenhouse gas emissions and mitigate energy poverty. To reduce the costs and manpower requirements of conventional field surveys, this study presents a deep-learning model to examine the insulation performance of windows using photographs taken in low-income housing. A smartphone application using crowdsourcing was developed for data collection. The insulation performance of windows was determined based on U-value, derived considering the frame-material type, number of panes, and area of windows. An image-labeling tool was designed to identify and annotate window components within photographs. Furthermore, software utilizing open-source computer vision was developed to estimate the window area. After training on a dataset with ResNet and EfficientNet, an accuracy of approximately 80% was achieved. Thus, this study introduces a novel workflow to evaluate the insulation performance of windows, which can support the energy-efficient renovation of low-income housing.

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“…The implementation of version control mechanisms is crucial in this context, enabling the tracking of changes over time and ensuring that stakeholders have access to the correct version of the model. The management of data within the BIM environment also extends to the integration of MEP-specific information, such as performance specifications, installation details, and maintenance requirements [3]. This detailed information enriches the BIM model, transforming it into a comprehensive resource for all phases of the project, from design through to facility management.…”

Section: Data Managementmentioning

confidence: 99%

Optimizing construction project outcomes through BIM and MEP system integration

Chen,

Zhu,

Shang

2024

ACE

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The integration of Building Information Modeling (BIM) with Mechanical, Electrical, and Plumbing (MEP) systems represents a transformative approach to improving efficiency, coordination, and cost-effectiveness in construction projects. This paper explores a comprehensive methodology for BIM and MEP integration, encompassing framework development, data management, and collaboration mechanisms. It highlights the significant benefits such as enhanced project coordination, improved efficiency, and cost reduction while addressing key challenges including technical barriers, cultural and organizational changes, and data security concerns. Solutions such as the development of interoperability standards, middleware technologies, robust data management protocols, and collaborative platforms are discussed. The study underscores the necessity of a multifaceted strategy involving training, change management, and leadership commitment to drive successful implementation. Through detailed analysis and discussion, this paper demonstrates how BIM and MEP integration can lead to more streamlined, cost-effective, and high-quality construction projects.

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Quality of crowdsourced geospatial building information: A global assessment of OpenStreetMap attributes

Cited by 48 publications

References 148 publications

A Global Feature-Rich Network Dataset of Cities and Dashboard for Comprehensive Urban Analyses

A Global Feature-Rich Network Dataset of Cities and Dashboard for Comprehensive Urban Analyses

Workflow for Window Composition Detection to Aid Energy-Efficient Renovation in Low-Income Housing in Korea

Optimizing construction project outcomes through BIM and MEP system integration

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