High‐resolution quantification of building stock using multi‐source remote sensing imagery and deep learning

Bao, Yi; Huang, Zhou; Wang, Han; Yin, Ganmin; Zhou, Xiao; Gao, Yong

doi:10.1111/jiec.13356

Cited by 14 publications

(4 citation statements)

References 75 publications

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“…Arbabi et al combined the mobile-sensing approach with a convolutional neural network (CNN) based computer vision to estimate urban building stocks through the identification and semantic classification of stock objects, components, and materials from the generated 3D surface maps. Similarly, Bao et al . and Liu et al applied CNN to estimate high-resolution building stock in Beijing and major Japanese metropolitan areas using features extracted from optical remote sensing and nighttime light data.…”

Section: Where and How Data Science Has Helped The Circular Economy?mentioning

confidence: 99%

Data Science Applications in Circular Economy: Trends, Status, and Future

Zhao,

Yu,

Wang

et al. 2024

Environ. Sci. Technol.

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The circular economy (CE) aims to decouple the growth of the economy from the consumption of finite resources through strategies, such as eliminating waste, circulating materials in use, and regenerating natural systems. Due to the rapid development of data science (DS), promising progress has been made in the transition toward CE in the past decade. DS offers various methods to achieve accurate predictions, accelerate product sustainable design, prolong asset life, optimize the infrastructure needed to circulate materials, and provide evidence-based insights. Despite the exciting scientific advances in this field, there still lacks a comprehensive review on this topic to summarize past achievements, synthesize knowledge gained, and navigate future research directions. In this paper, we try to summarize how DS accelerated the transition to CE. We conducted a critical review of where and how DS has helped the CE transition with a focus on four areas including (1) characterizing socioeconomic metabolism, (2) reducing unnecessary waste generation by enhancing material efficiency and optimizing product design, (3) extending product lifetime through repair, and (4) facilitating waste reuse and recycling. We also introduced the limitations and challenges in the current applications and discussed opportunities to provide a clear roadmap for future research in this field.

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Section: Where and How Data Science Has Helped The Circular Economy?mentioning

confidence: 99%

Data Science Applications in Circular Economy: Trends, Status, and Future

Zhao,

Yu,

Wang

et al. 2024

Environ. Sci. Technol.

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“…Night-time lighting (NTL) data serve as a significant source for extracting building information [41]. Since 1992, global NTL products have been provided by the Defense Meteorological Satellite Program's Operational Line-scan System (DMSP-OLS) [42][43][44].…”

Section: Literature Reviewmentioning

confidence: 99%

High-Resolution Mapping of Urban Residential Building Stock Using Multisource Geographic Data

Shen,

Wang,

Yang

et al. 2024

Buildings

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The rapid pace of urbanization and the increasing concentration of populations in urban areas have generated a substantial demand for architectural structures, resulting in a significant increase in building stock and continuous material flows that interact with the environment. This study emphasizes the importance of high-spatial-resolution mapping of residential building stock for effective urban-construction resource management, planning, and waste management. Focusing on Xi’an as a case study, the research develops a comprehensive framework for mapping urban residential building stock by integrating diverse data dimensions, including temporal, spatial, network, and multi-attribute aspects. The findings indicate that between 1990 and 2020, approximately 4758 residential communities were established in central Xi’an. The analysis of seven key residential construction materials revealed that the building stock escalated from 1.53 million tons to 731.12 million tons, with a steady spatial expansion of material distribution. The study attributes this growth to factors such as population increase, economic advancement, and policy initiatives, which, in turn, have driven the demand for residential building materials and reinforced the interdependence between urban expansion and residential construction development. Remarkably, from 1990 to 2020, the population surged by 2.1-fold, the economy by 66-fold, and the stock of residential building materials by 477-fold, indicating that the growth rate of material stock consistently outpaced that of both population and economic growth. Over the past three decades, the rapid expansion of residential buildings has led to the encroachment of urban ecological spaces by concrete structures. The methodology proposed in this study for quantifying building material offers valuable insights for policymakers and urban and environmental planners to foster responsible resource consumption and supports component-level circularity in the built environment.

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“…On the other hand, remote sensing data have also provided many ideas and insights. Urban night light data, in the form of remote sensing imagery, are appropriate for studying multi-scale social and economic data gridding (Bao et al, 2023;Huang et al, 2020). The brightness of urban night lights represents the degree of local economy activities, whose positive relationships with GDP has been found with statistical significance at several spatial scales such as provinces and counties (Wang et al, 2021b).…”

Section: Evaluate Urban Vitality Through Big Datamentioning

confidence: 99%

A review of urban vitality research in the Chinese world

Jiang

Zhang

Liu

et al. 2023

Transactions in Urban Data, Science, and Technology

Self Cite

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This paper is a systematic review of the contemporary research progresses of urban vitality in China. Through examining the most related and advanced Chinese academic literatures, this paper introduces the core definitions and concepts and summarizes the methodology and findings of current Chinese urban vitality research. The data sources and quantitative research methods and paradigms such as vitality index frameworks are listed and discussed in detail regarding their strengths and limitations. From studies in multiple Chinese cities, we synthesize the effects and the influence mechanisms of multi-faceted factors on different types of urban vitality. Finally, we not only summarize and extrapolate the policy implications of these literatures for improving urban vitality but also propose recommendations for future research.

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High‐resolution quantification of building stock using multi‐source remote sensing imagery and deep learning

Cited by 14 publications

References 75 publications

Data Science Applications in Circular Economy: Trends, Status, and Future

Data Science Applications in Circular Economy: Trends, Status, and Future

High-Resolution Mapping of Urban Residential Building Stock Using Multisource Geographic Data

A review of urban vitality research in the Chinese world

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