Spatial calculation of urban built environment stock： Progress and prospects
- Vol. 26, Issue 10, Pages: 1909-1919(2022)
Published： 07 October 2022
DOI： 10.11834/jrs.20222083
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摘要
城市建成环境是人类赖以生存的人造环境，城市建成环境存量是指城市中建筑物和基础设施的材料质量。反演城市建成环境存量的空间分布，是数字城市建设的新方向，它能够帮助我们了解城市发展模式，更加有效管理城市资源和废弃物等，对发展城市循环经济、实现城市的可持续发展有着十分重要的意义。本文详细介绍了城市建成环境存量空间计算的3种方法（自上而下、自下而上和遥感计算方法）的理论基础和发展现状，总结了目前的几中方法都存在着过度依赖统计数据、无法兼顾研究区域大尺度和高空间分辨率等问题。在地理大数据时代，更多的数据源为存量的计算带来了新的研究方向。本文总结了新数据源的优势，并展望了结合地理大数据和机器学习方法的存量计算方法，为城市建成环境存量的空间计算提供了一种新的思路。
Abstract
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.
关键词
城市建成环境存量城市矿产城市代谢可持续发展机器学习
Keywords
urban built environment stockurban miningurban metabolismsustainable developmentmachine learning
references
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 [DOI: 10.1016/j.resconrec.2020.105026http://dx.doi.org/10.1016/j.resconrec.2020.105026]
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 [DOI: 10.1080/09613218.2016.1152040http://dx.doi.org/10.1080/09613218.2016.1152040]
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 [DOI: 10.1080/09613210701287588http://dx.doi.org/10.1080/09613210701287588]
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. [DOI: 10.1080/09613210903186901http://dx.doi.org/10.1080/09613210903186901]
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 [DOI: 10.1080/02665433.2012.680283http://dx.doi.org/10.1080/02665433.2012.680283]
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. [DOI: 10.1016/j.rse.2020.111730http://dx.doi.org/10.1016/j.rse.2020.111730]
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 [DOI: 10.1016/j.resconrec.2012.05.003http://dx.doi.org/10.1016/j.resconrec.2012.05.003]
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
陈颖彪, 郑子豪, 吴志峰, 千庆兰. 2019. 夜间灯光遥感数据应用综述和展望. 地理科学进展, 38(2): 205-223 [DOI: 10.18306/dlkxjz.2019.02.005http://dx.doi.org/10.18306/dlkxjz.2019.02.005]
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 [DOI: 10.1016/j.resconrec.2018.02.003http://dx.doi.org/10.1016/j.resconrec.2018.02.003]
Daigo I, Igarashi Y, Matsuno Y and Adachi Y. 2007. Accounting for steel stock in Japan ISIJ International 47(7):1065-1069. [DOI: 10.2355/isijinternational.47.1065http://dx.doi.org/10.2355/isijinternational.47.1065]
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 [DOI: 10.1579/0044-7447-29.3.157http://dx.doi.org/10.1579/0044-7447-29.3.157]
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 [DOI: 10.1038/s41586-020-3010-5http://dx.doi.org/10.1038/s41586-020-3010-5]
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 [DOI: 10.1016/j.wasman.2015.03.036http://dx.doi.org/10.1016/j.wasman.2015.03.036]
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 [DOI: 10.1016/j.rse.2021.112877http://dx.doi.org/10.1016/j.rse.2021.112877]
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 [DOI: 10.1186/s12302-020-00397-4http://dx.doi.org/10.1186/s12302-020-00397-4]
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 [DOI: 10.1016/j.ecolecon.2015.03.001http://dx.doi.org/10.1016/j.ecolecon.2015.03.001]
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: 10.1021/acs.est.5b05790http://dx.doi.org/10.1021/acs.est.5b05790
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 [DOI: 10.1016/j.rse.2020.112128http://dx.doi.org/10.1016/j.rse.2020.112128]
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 [DOI: 10.1080/09613218.2014.872547http://dx.doi.org/10.1080/09613218.2014.872547]
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 [DOI: 10.1080/22797254.2017.1416676http://dx.doi.org/10.1080/22797254.2017.1416676]
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 [DOI: 10.1037/a0013620http://dx.doi.org/10.1037/a0013620]
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 [DOI: 10.1016/j.resconrec.2017.11.022http://dx.doi.org/10.1016/j.resconrec.2017.11.022]
Goodchild M F. 2007. Citizens as sensors: the world of volunteered geography. GeoJournal, 69(4): 211-221 [DOI: 10.1007/s10708-007-9111-yhttp://dx.doi.org/10.1007/s10708-007-9111-y]
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 [DOI: 10.1021/acs.est.9b01952http://dx.doi.org/10.1021/acs.est.9b01952]
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 [DOI: 10.1111/jiec.13054http://dx.doi.org/10.1111/jiec.13054]
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 [DOI: 10.1016/j.resconrec.2019.03.031http://dx.doi.org/10.1016/j.resconrec.2019.03.031]
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 [DOI: 10.1371/journal.pone.0262503http://dx.doi.org/10.1371/journal.pone.0262503]
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 [DOI: 10.1021/acs.est.8b03111http://dx.doi.org/10.1021/acs.est.8b03111]
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 [DOI: 10.1016/j.wasman.2006.10.009http://dx.doi.org/10.1016/j.wasman.2006.10.009]
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 [DOI: 10.1111/jiec.12739http://dx.doi.org/10.1111/jiec.12739]
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 [DOI: 10.1080/09613211003729988http://dx.doi.org/10.1080/09613211003729988]
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 [DOI: 10.1111/j.1530-9290.2010.00245.xhttp://dx.doi.org/10.1111/j.1530-9290.2010.00245.x]
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 [DOI: 10.1016/j.jclepro.2018.11.030http://dx.doi.org/10.1016/j.jclepro.2018.11.030]
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 [DOI: 10.1016/j.isprsjprs.2022.01.022http://dx.doi.org/10.1016/j.isprsjprs.2022.01.022]
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 [DOI: 10.1016/j.resconrec.2012.12.013http://dx.doi.org/10.1016/j.resconrec.2012.12.013]
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 [DOI: 10.3390/rs12193254http://dx.doi.org/10.3390/rs12193254]
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 [DOI: 10.1126/science.aaf7894http://dx.doi.org/10.1126/science.aaf7894]
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 [DOI: 10.1016/j.resconrec.2016.09.001http://dx.doi.org/10.1016/j.resconrec.2016.09.001]
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 [DOI: 10.1111/jiec.12446http://dx.doi.org/10.1111/jiec.12446]
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 [DOI: 10.1073/pnas.1613773114http://dx.doi.org/10.1073/pnas.1613773114]
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 [DOI: 10.1016/j.jclepro.2011.01.015http://dx.doi.org/10.1016/j.jclepro.2011.01.015]
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 [DOI: 10.1021/acs.est.8b06652http://dx.doi.org/10.1021/acs.est.8b06652]
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
李朝奎，曾强国，方军，吴馁，武凯华. 2021. 改进全卷积网络方法的高分二号影像农村道路提取.遥感学报，25（9）： 1978-1988 [DOI： 10.11834/jrs.20219209http://dx.doi.org/10.11834/jrs.20219209]
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
李德仁, 李熙. 2015. 论夜光遥感数据挖掘. 测绘学报, 44(6): 591-601 [DOI: 10.11947/j.AGCS.2015.20150149http://dx.doi.org/10.11947/j.AGCS.2015.20150149]
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 [DOI: 10.1016/j.rse.2020.111859http://dx.doi.org/10.1016/j.rse.2020.111859]
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[DOI: 10.1016/j.rse.2020.111705http://dx.doi.org/10.1016/j.rse.2020.111705]
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 [DOI: 10.3390/rs6064780http://dx.doi.org/10.3390/rs6064780]
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 [DOI: 10.1016/j.resconrec.2016.07.004http://dx.doi.org/10.1016/j.resconrec.2016.07.004]
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 [DOI: 10.1057/s41599-021-00757-2http://dx.doi.org/10.1057/s41599-021-00757-2]
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 [DOI: 10.1021/acs.est.9b07229http://dx.doi.org/10.1021/acs.est.9b07229]
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 [DOI: 10.1111/jiec.12327http://dx.doi.org/10.1111/jiec.12327]
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 [DOI: 10.1016/j.rser.2017.02.060http://dx.doi.org/10.1016/j.rser.2017.02.060]
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 [DOI: 10.1016/j.resconrec.2017.06.016http://dx.doi.org/10.1016/j.resconrec.2017.06.016]
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 [DOI: 10.1111/jiec.12723http://dx.doi.org/10.1111/jiec.12723]
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 [DOI: 10.1117/12.2074163http://dx.doi.org/10.1117/12.2074163]
Müller D B. 2006. Stock dynamics for forecasting material flows— case study for housing in the Netherlands. Ecological Economics, 59(1): 142-156 [DOI: 10.1016/j.ecolecon.2005.09.025http://dx.doi.org/10.1016/j.ecolecon.2005.09.025]
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 [DOI: 10.1021/es402618 mhttp://dx.doi.org/10.1021/es402618m]
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 [DOI: 10.1021/es403506ahttp://dx.doi.org/10.1021/es403506a]
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 [DOI: 10.1111/jiec.12773http://dx.doi.org/10.1111/jiec.12773]
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 [DOI: 10.3390/buildings7020045http://dx.doi.org/10.3390/buildings7020045]
Ostrolenk B. 1941. Economic Geography. Chicago: Richard D. Irwin
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 [DOI: 10.1080/1573062X.2013.795234http://dx.doi.org/10.1080/1573062X.2013.795234]
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
裴韬，黄强，王席，陈晓，刘亚溪，宋辞，陈洁，周成虎. 2021. 地理大数据聚合的内涵、分类与框架. 遥感学报，25(11): 2153-2162 [DOI:10.11834/jrs.20210480http://dx.doi.org/10.11834/jrs.20210480]
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 [DOI: 10.1016/j.resconrec.2021.105509http://dx.doi.org/10.1016/j.resconrec.2021.105509]
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 [DOI: 10.1073/pnas.0900658106http://dx.doi.org/10.1073/pnas.0900658106]
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 [DOI: 10.1038/s41467-020-15870-0http://dx.doi.org/10.1038/s41467-020-15870-0]
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 [DOI: 10.1016/j.resconrec.2016.06.001http://dx.doi.org/10.1016/j.resconrec.2016.06.001]
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 [DOI: 10.1016/j.resconrec.2016.08.007http://dx.doi.org/10.1016/j.resconrec.2016.08.007]
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 [DOI: 10.3390/rs12061050http://dx.doi.org/10.3390/rs12061050]
Stephan A and Athanassiadis A. 2017. Quantifying and mapping embodied environmental requirements of urban building stocks. Building and Environment, 114: 187-202 [DOI: 10.1016/j.buildenv.2016.11.043http://dx.doi.org/10.1016/j.buildenv.2016.11.043]
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 [DOI: 10.1016/j.resconrec.2010.09.008http://dx.doi.org/10.1016/j.resconrec.2010.09.008]
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 [DOI: 10.1080/09613210903169394http://dx.doi.org/10.1080/09613210903169394]
UN Habitat. 2019. The strategic plan 2020—2023. United Nations: Geneva, Switzerland
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 [DOI: 10.1016/j.resconrec.2021.105608http://dx.doi.org/10.1016/j.resconrec.2021.105608]
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 [DOI: 10.1111/jiec.12383http://dx.doi.org/10.1111/jiec.12383]
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 [DOI: 10.1111/jiec.12271http://dx.doi.org/10.1111/jiec.12271]
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 [DOI: 10.1016/j.cities.2021.103202http://dx.doi.org/10.1016/j.cities.2021.103202]
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 [DOI: 10.1016/j.apenergy.2018.09.200http://dx.doi.org/10.1016/j.apenergy.2018.09.200]
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 [DOI: 10.1109/JSTARS.2020.3023730http://dx.doi.org/10.1109/JSTARS.2020.3023730]
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 [DOI: 10.1021/acs.est.8b02838http://dx.doi.org/10.1021/acs.est.8b02838]
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 [DOI: 10.1080/13658816.2018.1555642http://dx.doi.org/10.1080/13658816.2018.1555642]
Zhang F and Liu Y. 2021. Street view imagery: methods and applications based on artificial intelligence. National Remote Sensing Bulletin, 25(5): 1043-1054
张帆, 刘瑜. 2021. 街景影像——基于人工智能的方法与应用. 遥感学报, 25(5): 1043-1054 [DOI: 10.11834/jrs.20219341http://dx.doi.org/10.11834/jrs.20219341]
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 [DOI: 10.3390/s20051465http://dx.doi.org/10.3390/s20051465]
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 [DOI: 10.3390/ijgi8060247http://dx.doi.org/10.3390/ijgi8060247]
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