Anterior|Siguiente

Spatial calculation of urban built environment stock： Progress and prospects

  • role： First author第一作者
  • Affiliation：

    Institute of Remote Sensing and Geographical Information Systems, School of Earth and Space Sciences, Peking University, Beijing 100871, China

    Beijing Key Lab of Spatial Information Integration & Its Applications, Beijing 100871, China

  • Email：baoyi@pku.edu.cn
  • Introduction：鲍毅，研究方向为城市数据挖掘和地理信息科学。E-mail： baoyi@pku.edu.cn
BAO Yi,  
  • role： Corresponding author通信作者
  • Affiliation：

    Institute of Remote Sensing and Geographical Information Systems, School of Earth and Space Sciences, Peking University, Beijing 100871, China

    Beijing Key Lab of Spatial Information Integration & Its Applications, Beijing 100871, China

  • Email：huangzhou@pku.edu.cn
  • Introduction：黄舟，研究方向为地理大数据理论方法、时空大数据计算与挖掘。E-mail： huangzhou@pku.edu.cn
HUANG Zhou*,  
  • Affiliation：

    Institute of Remote Sensing and Geographical Information Systems, School of Earth and Space Sciences, Peking University, Beijing 100871, China

    Beijing Key Lab of Spatial Information Integration & Its Applications, Beijing 100871, China

GUO Qinghua,  
  • Affiliation：

    Institute of Remote Sensing and Geographical Information Systems, School of Earth and Space Sciences, Peking University, Beijing 100871, China

    Beijing Key Lab of Spatial Information Integration & Its Applications, Beijing 100871, China

LIU Yu

resumen

Urban built environment is the manufactured environment where human beings live. The stocks of the urban built environment refer to the quality of materials (e.g., concrete, steel, copper, etc.) that accumulated in buildings and infrastructure. Revealing the spatial distribution of urban built environment stocks arises as a new direction for digital city construction, which helps to understand the urban development patterns and urban resource and waste management. Developing an urban circular economy and realizing sustainable urban development is essential. Therefore, it is necessary to summarize and sort out the current spatial calculation method of built environment stocks.This study introduces a detailed theoretical basis and development status of three methods for spatial calculation of urban built environment stock: that are the top-down method, the bottom-up method, and the remote sensing calculation method. The advantages and limitations of these models are elaborated with application and data availability. The top-down approach has a complete set of theoretical foundations and algorithm models, which can perform large-scale material flow analysis well. Due to its inability to obtain a high spatial resolution, this method is not suitable for analyzing urban development within cities. Contrastingly, the bottom-up method permits fine-grained stock estimation by gathering cadastral-level physical measurements of buildings and infrastructure and associated material composition indicators. However, it is labour-intensive and the scope of the bottom-up method is often restricted to city-level or lower geographical regions. As for remote sensing calculation, previous studies established a linear regression relationship between the nighttime light radiation intensity and the built environment stocks in the study areas. However, the night light remote sensing data will degrade the reliability of quantitative analysis due to background noise and radiation saturation effect. Thus, stock data with the high spatial resolution are impossible to acquire. These three traditional methods are often difficult to strike a balance between large scale and high spatial resolution. However, in the era of big geographic data, more data sources have brought new research directions for stock calculation.Geo Big Data and Earth Observation data are essential in developing earth science, environmental science, remote sensing science, and geographic information science. Combining these wide-coverage, high-precision, and fast-update data and machine learning methods have been widely used in poverty surveys and energy consumption. This paper proposes a framework that combines big geographic data and machine learning for stock calculation based on the above background. We expect an end-to-end method to estimate grid stocks directly from publicly available information that minimizes manual involvement. However, the heterogeneity of geospatial and the black-box nature of deep learning may have an impact on the migration effects of the model. Despite its drawbacks, this migration model has the potential for large-scale, high-resolution stock calculation in future works.

palabra clave

urban built environment stock;urban mining;urban metabolism;sustainable development;machine learning

References

  1. 1.
    Ajayebi A, Hopkinson P, Zhou K, Lam D, Chen H M and Wang Y. 2020. Spatiotemporal model to quantify stocks of building structural products for a prospective circular economy. Resources, Conservation and Recycling, 162: 105026
  2. 2.
    Aksözen M, Hassler U and Kohler N. 2017. Reconstitution of the dynamics of an urban building stock. Building Research and Information, 45(3): 239-258
  3. 3.
    Bergsdal H, Brattebø H, Bohne R A and Müller D B. 2007. Dynamic material flow analysis for Norway’s dwelling stock. Building Research and Information, 35(5): 557-570
  4. 4.
    Brattebø H, Bergsdal H, Sandberg N H, Hammervold J and Müller D B. 2009. Exploring built environment stock metabolism and sustainability by systems analysis approaches. Building Research & Information, 37(5-6): 569-582.
  5. 5.
    Brenner N, Marcuse P and Mayer M. 2012. Cities for People, Not for Profit: Critical Urban Theory and the Right to the City. New York: Routledge: 476-478
  6. 6.
    Cao W P, Dong L, Wu L and Liu Y. 2020. Quantifying urban areas with multi-source data based on percolation theory. Remote Sensing of Environment, 241: 111730-111741.
  7. 7.
    Chen W Q and Shi L. 2012. Analysis of aluminum stocks and flows in Mainland China from 1950 to 2009: exploring the dynamics driving the rapid increase in China’s aluminum production. Resources, Conservation and Recycling, 65: 18-28
  8. 8.
    Chen Y B, Zheng Z H, Wu Z F and Qian Q L. 2019. Review and prospect of application of nighttime light remote sensing data. Progress in Geograph, 38(2): 205-223
  9. 9.
    Cheng K L, Hsu S C, Li W M and Ma H W. 2018. Quantifying potential anthropogenic resources of buildings through hot spot analysis. Resources, Conservation and Recycling, 133: 10-20
  10. 10.
    Daigo I, Igarashi Y, Matsuno Y and Adachi Y. 2007. Accounting for steel stock in Japan ISIJ International 47(7):1065-1069.
  11. 11.
    Doll C N H, Muller J P and Elvidge C D. 2000. Night-time imagery as a tool for global mapping of socioeconomic parameters and greenhouse gas emissions. AMBIO, 29(3): 157-162
  12. 12.
    Elhacham E, Ben-Uri L, Grozovski J, Bar-On Y M and Milo R. 2020. Global human-made mass exceeds all living biomass. Nature, 588(7838): 442-444
  13. 13.
    Ergun D and Gorgolewski M. 2015. Inventorying Toronto’s single detached housing stocks to examine the availability of clay brick for urban mining. Waste Management, 45: 180-185
  14. 14.
    Esch T, Brzoska E, Dech S, Leutner B, Palacios-Lopez D, Metz-Marconcini A, Marconcini M, Roth A and Zeidler J. 2022. World settlement footprint 3D—a first three-dimensional survey of the global building stock. Remote Sensing of Environment, 270: 112877-112892
  15. 15.
    Ferreira B, Iten M and Silva R G. 2020. Monitoring sustainable development by means of earth observation data and machine learning: a review. Environmental Sciences Europe, 32(1): 120
  16. 16.
    Fishman T, Schandl H and Tanikawa H. 2015. The socio-economic drivers of material stock accumulation in Japan’s prefectures. Ecological Economics, 113: 76-84
  17. 17.
    Fishman T, Schandl H and Tanikawa H. 2016. Stochastic analysis and forecasts of the patterns of speed, acceleration, and levels of material stock accumulation in society. Environmental Science & Technology, 50(7), 3729-3737 [DOI:
  18. 18.
    Frantz D, Schug F, Okujeni A, Navacchi C, Wagner W, van der Linden S and Hostert P. 2021. National-scale mapping of building height using Sentinel-1 and Sentinel-2 time series. Remote Sensing of Environment, 252: 112128-112145
  19. 19.
    Gallardo C, Sandberg N H and Brattebø H. 2014. Dynamic-MFA examination of Chilean housing stock: long-term changes and earthquake damage. Building Research and Information, 42(3): 343-358
  20. 20.
    Gavankar N L and Ghosh S K. 2018. Automatic building footprint extraction from high-resolution satellite image using mathematical morphology. European Journal of Remote Sensing, 51(1): 182-193
  21. 21.
    Gondan M. 2009. Testing the race model inequality in redundant stimuli with variable onset asynchrony. Journal of Experimental Psychology: Human Perception and Performance, 35(2): 575-579
  22. 22.
    Gontia P, Nägeli C, Rosado L, Kalmykova Y and Österbring M. 2018. Material-intensity database of residential buildings: a case-study of Sweden in the international context. Resources, Conservation and Recycling, 130: 228-239
  23. 23.
    Goodchild M F. 2007. Citizens as sensors: the world of volunteered geography. GeoJournal, 69(4): 211-221
  24. 24.
    Göswein V, Silvestre J D, Habert G and Freire F. 2019. Dynamic assessment of construction materials in urban building stocks: a critical review. Environmental Science and Technology, 53(17): 9992-10006
  25. 25.
    Guo J, Fishman T, Wang Y, Miatto A, Wuyts W, Zheng L C, Wang H M and Tanikawa H. 2021. Urban development and sustainability challenges chronicled by a century of construction material flows and stocks in Tiexi, China. Journal of Industrial Ecology, 25(1): 162-175
  26. 26.
    Guo J, Miatto A, Shi F and Tanikawa H. 2019. Spatially explicit material stock analysis of buildings in Eastern China metropoles. Resources, Conservation and Recycling, 146: 45-54
  27. 27.
    Han G H, Zhou T, Sun Y H and Zhu S J. 2022. The relationship between night-time light and socioeconomic factors in China and ka. PLoS One, 17(1): e0262503
  28. 28.
    Han J, Chen W Q, Zhang L X and Liu G. 2018. Uncovering the spatiotemporal dynamics of urban infrastructure development: a high spatial resolution material stock and flow analysis. Environmental Science and Technology, 52(21): 12122-12132
  29. 29.
    Hashimoto S, Tanikawa H and Moriguchi Y. 2007. Where will large amounts of materials accumulated within the economy go?—A material flow analysis of construction minerals for Japan. Waste Management, 27(12): 1725-1738
  30. 30.
    Heeren N and Hellweg S. 2019. Tracking construction material over space and time: prospective and geo-referenced modeling of building stocks and construction material flows. Journal of Industrial Ecology, 23(1): 253-267
  31. 31.
    Hu M M, Bergsdal H, van der Voet E, Huppes G and Müller D B. 2010a. Dynamics of urban and rural housing stocks in China. Building Research and Information, 38(3): 301-317
  32. 32.
    Hu M M, Van Der Voet E and Huppes G. 2010b. Dynamic material flow analysis for strategic construction and demolition waste management in Beijing. Journal of Industrial Ecology, 14(3): 440-456
  33. 33.
    Huang B J, Chen Y X, McDowall W, Türkeli S, Bleischwitz R and Geng Y. 2019. Embodied GHG emissions of building materials in Shanghai. Journal of Cleaner Production, 210: 777-785
  34. 34.
    Huang H B, Chen P M, Xu X Q, Liu C X, Wang J, Liu C, Clinton N and Gong P. 2022. Estimating building height in China from ALOS AW3D30. ISPRS Journal of Photogrammetry and Remote Sensing, 185: 146-157
  35. 35.
    Huang T, Shi F, Tanikawa H, Fei J L and Han J. 2013. Materials demand and environmental impact of buildings construction and demolition in China based on dynamic material flow analysis. Resources, Conservation and Recycling, 72: 91-101
  36. 36.
    Huang Z, Qi H J, Kang C G, Su Y L and Liu Y. 2020. An ensemble learning approach for urban land use mapping based on remote sensing imagery and social sensing data. Remote Sensing, 12(19): 3254-3271
  37. 37.
    Jean N, Burke M, Xie M, Davis W M, Lobell D B and Ermon S. 2016. Combining satellite imagery and machine learning to predict poverty. Science, 353(6301): 790-794
  38. 38.
    Kalcher J, Praxmarer G and Teischinger A. 2017. Quantification of future availabilities of recovered wood from Austrian residential buildings. Resources, Conservation and Recycling, 123: 143-152
  39. 39.
    Kleemann F, Lederer J, Rechberger H and Fellner J. 2017. GIS-based analysis of Vienna’s material stock in buildings. Journal of Industrial Ecology, 21(2): 368-380
  40. 40.
    Krausmann F, Wiedenhofer D, Lauk C, Haas W, Tanikawa H, Fishman T, Miatto A, Schandl H and Haberl H. 2017. Global socioeconomic material stocks rise 23-fold over the 20th century and require half of annual resource use. Proceedings of the National Academy of Sciences of the United States of America, 114(8): 1880-1885
  41. 41.
    Krook J, Carlsson A, Eklund M, Frändegård P and Svensson N. 2011. Urban mining: hibernating copper stocks in local power grids. Journal of Cleaner Production, 19(9/10): 1052-1056
  42. 42.
    Lanau M, Liu G, Kral U, Wiedenhofer D, Keijzer E, Yu C and Ehlert C. 2019. Taking stock of built environment stock studies: progress and prospects. Environmental Science and Technology, 53(15): 8499-8515
  43. 43.
    Li C K，Zeng Q G，Fang J，Wu N and Wu K H. 2021. Road extraction in rural areas from high resolution remote sensing image using a improved Full Convolution Network. National Remote Sensing Bulletin， 25（9）：1978-1988
  44. 44.
    Li D R and Li X. 2015. An overview on data mining of nighttime light remote sensing. Acta Geodaetica et Cartographica Sinica, 44(6): 591-601
  45. 45.
    Li M M, Koks E, Taubenböck H and van Vliet J. 2020a. Continental-scale mapping and analysis of 3D building structure. Remote Sensing of Environment, 245: 111859-111874
  46. 46.
    Li X C, Zhou Y Y, Gong P, Seto K C and Clinton N. 2020b. Developing a method to estimate building height from Sentinel-1 data. Remote Sensing of Environment, 240: 111705-111713
  47. 47.
    Liang H W, Tanikawa H, Matsuno Y and Dong L. 2014. Modeling in-use steel stock in China’s buildings and civil engineering infrastructure using time-series of DMSP/OLS nighttime lights. Remote Sensing, 6(6): 4780-4800
  48. 48.
    Lin C, Liu G and Müller D B. 2017. Characterizing the role of built environment stocks in human development and emission growth. Resources, Conservation and Recycling, 123: 67-72
  49. 49.
    Mao R C, Bao Y, Duan H B and Liu G. 2021. Global urban subway development, construction material stocks, and embodied carbon emissions. Humanities and Social Sciences Communications, 8(1): 83-94
  50. 50.
    Mao R C, Bao Y, Huang Z, Liu Q C and Liu G. 2020. High-resolution mapping of the urban built environment stocks in Beijing. Environmental Science and Technology, 54(9): 5345-5355
  51. 51.
    Marcellus-Zamora K A, Gallagher P M, Spatari S and Tanikawa H. 2016. Estimating materials stocked by land-use type in historic urban buildings using spatio-temporal analytical tools. Journal of Industrial Ecology, 20(5): 1025-1037
  52. 52.
    Mastrucci A, Marvuglia A, Leopold U and Benetto E. 2017. Life cycle assessment of building stocks from urban to transnational scales: a review. Renewable and Sustainable Energy Reviews, 74: 316-332
  53. 53.
    Maung K N, Yoshida T, Liu G, Lwin C M, Muller D B and Hashimoto S. 2017. Assessment of secondary aluminum reserves of nations. Resources, Conservation and Recycling, 126: 34-41
  54. 54.
    Mesta C, Kahhat R and Santa-Cruz S. 2019. Geospatial characterization of material stock in the residential sector of a Latin-American city. Journal of Industrial Ecology, 23(1): 280-291
  55. 55.
    Murthy K, Shearn M, Smiley B D, Chau A H, Levine J and Robinson M D. 2014. SkySat-1: very high-resolution imagery from a small satellite//Proceedings Volume 9241, Sensors, Systems, and Next-Generation Satellites XVIII. Amsterdam: SPIE: 367-378
  56. 56.
    Müller D B. 2006. Stock dynamics for forecasting material flows— case study for housing in the Netherlands. Ecological Economics, 59(1): 142-156
  57. 57.
    Müller D B, Liu G, Løvik A N, Modaresi R, Pauliuk S, Steinhoff F S and Brattebø H. 2013. Carbon emissions of infrastructure development. Environmental Science and Technology, 47(20): 11739-11746
  58. 58.
    Müller E, Hilty L M, Widmer R, Schluep M and Faulstich M. 2014. Modeling metal stocks and flows: a review of dynamic material flow analysis methods. Environmental Science and Technology, 48(4): 2102-2113
  59. 59.
    Nguyen T C, Fishman T, Miatto A and Tanikawa H. 2019. Estimating the material stock of roads: the Vietnamese case study. Journal of Industrial Ecology, 23(3): 663-673
  60. 60.
    Oezdemir O, Krause K and Hafner A. 2017. Creating a resource cadaster—a case study of a district in the Rhine-Ruhr metropolitan area. Buildings, 7(2): 45-61
  61. 61.
    Ostrolenk B. 1941. Economic Geography. Chicago: Richard D. Irwin
  62. 62.
    Pauliuk S G, Venkatesh G, Brattebø H and Müller D B. 2014. Exploring urban mines: pipe length and material stocks in urban water and wastewater networks. Urban Water Journal, 11(4): 274-283
  63. 63.
    Pei T, Huang Q, Wang X, Chen X, Liu Y X, Song C, Chen J and Zhou C H. 2021. Big geodata aggregation: connotation, classification, and framework. National Remote Sensing Bulletin, 25(11): 2153-2162
  64. 64.
    Peled Y and Fishman T. 2021. Estimation and mapping of the material stocks of buildings of Europe: a novel nighttime lights-based approach. Resources, Conservation and Recycling, 169: 105509-105519
  65. 65.
    Rauch J N. 2009. Global mapping of Al, Cu, Fe, and Zn in-use stocks and in-ground resources. Proceedings of the National Academy of Sciences of the United States of America, 106(45): 18920-18925
  66. 66.
    Runting R K, Phinn S, Xie Z Y, Venter O and Watson J E M. 2020. Opportunities for big data in conservation and sustainability. Nature Communications, 11(1): 2003-2007
  67. 67.
    Schebek L, Schnitzer B, Blesinger D, Köhn A, Miekley B, Linke H J, Lohmann A, Motzko C and Seemann A. 2017. Material stocks of the non-residential building sector: the case of the Rhine-Main area. Resources, Conservation and Recycling, 123: 24-36
  68. 68.
    Schiller G, Müller F and Ortlepp R. 2017. Mapping the anthropogenic stock in Germany: metabolic evidence for a circular economy. Resources, Conservation and Recycling, 123: 93-107
  69. 69.
    Shao Z F, Tang P H, Wang Z Y, Saleem N, Yam S and Sommai C. 2020. BRRNet: a fully convolutional neural network for automatic building extraction from high-resolution remote sensing images. Remote Sensing, 12(6): 1050-1066
  70. 70.
    Stephan A and Athanassiadis A. 2017. Quantifying and mapping embodied environmental requirements of urban building stocks. Building and Environment, 114: 187-202
  71. 71.
    Takahashi K I, Terakado R, Nakamura J, Adachi Y, Elvidge C D and Matsuno Y. 2010. In-use stock analysis using satellite nighttime light observation data. Resources, Conservation and Recycling, 55(2): 196-200
  72. 72.
    Tanikawa H and Hashimoto S. 2009. Urban stock over time: spatial material stock analysis using 4D-GIS. Building Research and Information, 37(5/6): 483-502
  73. 73.
    UN Habitat. 2019. The strategic plan 2020—2023. United Nations: Geneva, Switzerland
  74. 74.
    Vilaysouk X, Islam K, Miatto A, Schandl H, Murakami S and Hashimoto S. 2021. Estimating the total in-use stock of Laos using dynamic material flow analysis and nighttime light. Resources, Conservation and Recycling, 170: 105608-105615
  75. 75.
    Wang T, Zhou J, Yue Y, Yang J and Hashimoto S. 2016. Weight under steel wheels: material stock and flow analysis of high-speed rail in China. Journal of Industrial Ecology, 20(6): 1349-1359
  76. 76.
    Wen Z G, Zhang C K, Ji X L and Xue Y Y. 2015. Urban mining’s potential to relieve China’s coming resource crisis. Journal of Industrial Ecology, 19(6): 1091-1102
  77. 77.
    Wu R, Wang J, Zhang D and Wang S. 2021. Identifying different types of urban land use dynamics using Point-of-interest (POI) and Random Forest algorithm: The case of Huizhou, China. Cities, 114:103202-103220
  78. 78.
    Xiao H W, Ma Z Y, Mi Z F, Kelsey J, Zheng J L, Yin W H and Yan M. 2018. Spatio-temporal simulation of energy consumption in China’s provinces based on satellite night-time light data. Applied Energy, 231: 1070-1078
  79. 79.
    Xing X Y, Huang Z, Cheng X M, Zhu D, Kang C G, Zhang F and Liu Y. 2020. Mapping human activity volumes through remote sensing imagery. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13: 5652-5668
  80. 80.
    Yu B L, Deng S Q, Liu G, Yang C S, Chen Z Q, Hill C J and Wu J P. 2018. Nighttime light images reveal spatial-temporal dynamics of global anthropogenic resources accumulation above ground. Environmental Science and Technology, 52(20): 11520-11527
  81. 81.
    Yu B L, Lian T, Huang Y X, Yao S J, Ye X Y, Chen Z Q, Yang C S and Wu J P. 2019. Integration of nighttime light remote sensing images and taxi GPS tracking data for population surface enhancement. International Journal of Geographical Information Science, 33(4): 687-706
  82. 82.
    Zhang F and Liu Y. 2021. Street view imagery: methods and applications based on artificial intelligence. National Remote Sensing Bulletin, 25(5): 1043-1054
  83. 83.
    Zhang L L, Wu J S, Fan Y, Gao H M and Shao Y H. 2020. An efficient building extraction method from high spatial resolution remote sensing images based on improved mask R-CNN. Sensors, 20(5): 1465
  84. 84.
    Zhuo L, Shi Q L, Zhang C Y, Li Q P and Tao H Y. 2019. Identifying building functions from the spatiotemporal population density and the interactions of people among buildings. ISPRS International Journal of Geo-Information, 8(6): 247

Leer el texto completo

The above content is generated by Large Model Translation. The translated content is for reference only. We do not assume any commercial or legal responsibilty for any consequences arising from the use of our website