We proposed a building-scale height map at the conterminous U.S.
The mapped building heights show distinct variation within and across cities.
Limited living spaces in urban regions reduce the inequality compared to rural areas.
Building volume (3D) reveals larger gap of living spaces than building footprints (2D).
| [1] | United Nations. (2019). World urbanization prospects: The 2018 revision (UN). |
| [2] | Foley, J.A., DeFries, R., Asner, G.P., et al. (2005). Global Consequences of Land Use. Science 309(5734): 570−574. DOI: 10.1126/science.1111772. |
| [3] | Stewart, I.D., and Oke, T.R. (2012). Local climate zones for urban temperature studies. Bulletin of the American Meteorological Society 93(12): 1879−1900. DOI: 10.1175/BAMS-D-11-00019.1. |
| [4] | Solecki, W., Seto, K.C., and Marcotullio, P.J. (2013). It's time for an urbanization science. Environment: Science and Policy for Sustainable Development 55(1): 12−17. DOI: 10.1080/00139157.2013.748387. |
| [5] | Zhou, Y. (2022). Understanding urban plant phenology for sustainable cities and planet. Nature Climate Change 12(4): 302−304. DOI: 10.1038/s41558-022-01331-7. |
| [6] | Liu, X., Pei, F., Wen, Y., et al. (2019). Global urban expansion offsets climate-driven increases in terrestrial net primary productivity. Nature Communications 10(1): 5558. DOI: 10.1038/s41467-019-13462-1. |
| [7] | Moran, D., Kanemoto, K., Jiborn, M., et al. (2018). Carbon footprints of 13 000 cities. Environmental Research Letters 13(6): 064041. DOI: 10.1088/1748-9326/aac72a. |
| [8] | Seto, K.C., Davis, S.J., Mitchell, R.B., et al. (2016). Carbon lock-in: Types, causes, and policy implications. Annual Review of Environment and Resources 41: 425−452. DOI: 10.1146/annurev-environ-110615-085934. |
| [9] | Flanner, M.G. (2009). Integrating anthropogenic heat flux with global climate models. Geophysical Research Letters 36 (2). DOI: 10.1029/2008GL036465. |
| [10] | Gong, P., Li, Z., Huang, H., et al. (2011). ICEsat GLAS data for urban environment monitoring. IEEE transactions on Geoscience and Remote Sensing 49(3): 1158−1172. DOI: 10.1109/TGRS.2010.2070514. |
| [11] | Seto, K.C., Dhakal, S., Bigio, A., et al. (2014). Human settlements, infrastructure and spatial planning. Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. https://www.ipcc.ch/site/assets/uploads/2018/02/ipcc_wg3_ar5_chapter12.pdf. |
| [12] | Zhang, W., Villarini, G., Vecchi, G.A., et al. (2018). Urbanization exacerbated the rainfall and flooding caused by hurricane Harvey in Houston. Nature 563(7731): 384. DOI: 10.1038/s41586-018-0676-z. |
| [13] | Xi, F., Davis, S.J., Ciais, P., et al. (2016). Substantial global carbon uptake by cement carbonation. Nature Geoscience 9(12): 880. DOI: 10.1038/ngeo2840. |
| [14] | Li, M., Koks, E., Taubenböck, H., et al. (2020). Continental-scale mapping and analysis of 3D building structure. Remote Sensing Environment 245: 111859. DOI: 10.1016/j.rse.2020.111859. |
| [15] | Frantz, D., Schug, F., Okujeni, A., et al. (2021). National-scale mapping of building height using Sentinel-1 and Sentinel-2 time series. Remote Sensing of Environment 252: 112128. DOI: 10.1016/j.rse.2020.112128. |
| [16] | Li, X., Zhou, Y., Gong, P., et al. (2020). Developing a method to estimate building height from Sentinel-1 data. Remote Sensing Environment 240: 111705. DOI: 10.1016/j.rse.2020.111705. |
| [17] | Huang, H., Chen, P., Xu, X., et al. (2022). Estimating building height in China from ALOS AW3D30. ISPRS Journal of Photogrammetry and Remote Sensing 185: 146−157. DOI: 10.1016/j.isprsjprs.2022.01.022. |
| [18] | Wang, X., Yu, X., and Ling, F. (2014). Building heights estimation using ZY3 data — A case study of Shanghai, China. IEEE Geoscience and Remote Sensing Symposium. DOI: 10.1109/IGARSS.2014.6946790. |
| [19] | Hao, L., Zhang, Y., and Cao, Z. (2016). Building extraction from stereo aerial images based on multi-layer line grouping with height constraint. IEEE International Geoscience and Remote Sensing Symposium (IGARSS). DOI: 10.1109/IGARSS.2016.7729110. |
| [20] | Yao, S., Shahzad, M., and Zhu, X.X. (2017). Building height estimation in single SAR image using OSM building footprints. 2017 Joint Urban Remote Sensing Event (JURSE). DOI: 10.1109/JURSE.2017.7924549. |
| [21] | Xu, Y., Ma, P., Ng, E., et al. (2015). Fusion of WorldView-2 stereo and multitemporal terraSAR-X images for building height extraction in Urban Areas. IEEE Geoscience and Remote Sensing Letters 12(8): 1795−1799. DOI: 10.1109/LGRS.2015.2427738. |
| [22] | Ma, X., Zheng, G., Chi, X., et al. (2023). Mapping fine-scale building heights in urban agglomeration with spaceborne lidar. Remote Sensing of Environment 285: 113392. DOI: 10.1016/j.rse.2022.113392. |
| [23] | Li, J., Huang, X., Tu, L., et al. (2022). A review of building detection from very high resolution optical remote sensing images. GIScience & Remote Sensing 59(1): 1199−1225. DOI: 10.1080/15481603.2022.2101727. |
| [24] | Munawar, H.S., Aggarwal, R., Qadir, Z., et al. (2021). A Gabor Filter-Based Protocol for Automated Image-Based Building Detection. Buildings 11(7): 302. DOI: 10.3390/buildings11070302. |
| [25] | Maltezos, E., Doulamis, N.D., Doulamis, A.D., et al. (2017). Deep convolutional neural networks for building extraction from orthoimages and dense image matching point clouds. Journal of Applied Remote Sensing 11(4): 042620. DOI: 10.1117/1.JRS.11.042620. |
| [26] | Godoy-Shimizu, D., Steadman, P., Hamilton, I., et al. (2018). Energy use and height in office buildings. Building Research & Information 46(8): 845−863. DOI: 10.1080/09613218.2018.1479927. |
| [27] | Liu, P., Liu, X., Liu, M., et al. (2019). Building Footprint Extraction from High-Resolution Images via Spatial Residual Inception Convolutional Neural Network. Remote Sensing 11(7): 830. DOI: 10.3390/rs11070830. |
| [28] | Microsoft (2018). US Building Footprints. https://wiki.openstreetmap.org/wiki/Microsoft_Building_Footprint_Data#March_2017_Release. |
| [29] | Arehart, J.H., Pomponi, F., D’Amico, B., et al. (2021). A New Estimate of Building Floor Space in North America. Environmental Science & Technology 55(8): 5161−5170. DOI: 10.1021/acs.est.0c05081. |
| [30] | Koppel, K., Zalite, K., Voormansik, K., et al. (2017). Sensitivity of Sentinel-1 backscatter to characteristics of buildings. International Journal of Remote Sensing 38(22): 6298−6318. DOI: 10.1080/01431161.2017.1353160. |
| [31] | Falcone, J.A. (2016). US national categorical mapping of building heights by block group from Shuttle Radar Topography Mission data. DOI: 10.5066/F7W09416. |
| [32] | Wentz, E.A., York, A.M., Alberti, M., et al. (2018). Six fundamental aspects for conceptualizing multidimensional urban form: A spatial mapping perspective. Landscape Urban Plan 179: 55−62. DOI: 10.1016/j.landurbplan.2018.07.007. |
| [33] | Gong, P., Chen, B., Li, X., et al. (2020). Mapping essential urban land use categories in China (EULUC-China): preliminary results for 2018. Science Bulletin 65(3): 182−187. DOI: 10.1016/j.scib.2019.12.007. |
| [34] | Zhou, D., Xiao, J., Bonafoni, S., et al. (2019). Satellite remote sensing of surface urban heat islands: progress, challenges, and perspectives. Remote Sensing 11(1): 48. DOI: 10.3390/rs11010048. |
| [35] | Reddy, A., and Leslie, T.F. (2015). Volume per capita as a useful measure of residential space. Urban Geography 36(7): 1099−1112. DOI: 10.1080/02723638.2015.1060696. |
| [36] | Reia, S.M., Rao, P.S.C., Barthelemy, M., et al. (2022). Spatial structure of city population growth. Nature Communications 13(1): 5931. DOI: 10.1038/s41467-022-33527-y. |
| [37] | Li, X., and Gong, P. (2016). Urban growth models: progress and perspective. Science Bulletin 61(21): 1637−1650. DOI: 10.1007/s11434-016-1111-1. |
| [38] | Zhu, X., Zhang, P., Wei, Y., et al. (2019). Measuring the efficiency and driving factors of urban land use based on the DEA method and the PLS-SEM model—A case study of 35 large and medium-sized cities in China. Sustainable Cities and Society 50: 101646. DOI: 10.1016/j.scs.2019.101646. |
| [39] | Rodriguez, R.S., Ürge-Vorsatz, D., and Barau, A.S. (2018). Sustainable Development Goals and climate change adaptation in cities. Nature Climate Change 8(3): 181. DOI: 10.1038/s41558-018-0098-9. |
| [40] | Verbavatz, V., and Barthelemy, M. (2020). The growth equation of cities. Nature 587(7834): 397−401. DOI: 10.1038/s41586-020-2900-x. |
| [41] | Bettencourt, L.M., Lobo, J., Helbing, D., et al. (2007). Growth, innovation, scaling, and the pace of life in cities. Proceedings of the National Academy of Sciences 104(17): 7301−7306. DOI: 10.1073/pnas.0610172104. |
| [42] | Glaeser, E.L., and Gyourko, J. (2002). The impact of zoning on housing affordability. National Bureau of Economic Research Cambridge, Mass., USA. DOI: 10.3386/w8835. |
| [43] | Zhou, Y., Li, X., Chen, W., et al. (2022). Satellite mapping of urban built-up heights reveals extreme infrastructure gaps and inequalities in the Global South. Proceedings of the National Academy of Sciences 119(46): e2214813119. DOI. DOI: 10.1073/pnas.2214813119. |
| [44] | Guo, H., Luo, L., Wang, H., et al. (2023). The STEP to facilitate achieving Sustainable Development Goals. The Innovation Geoscience 1(3): 100037. DOI: 10.59717/j.xinn-geo.2023.100037. |
| [45] | Balk, D.L., Deichmann, U., Yetman, G., et al. (2006). Determining global population distribution: Methods, applications and data. Advances in Parasitology 62: 119−156. DOI: 10.1016/S0065-308X(05)62004-0. |
| [46] | Kavouras, I., Sardis, E., Protopapadakis, E., et al. (2023). A low-cost gamified Urban planning methodology enhanced with co-creation and participatory approaches. Sustainability 15(3): 2297. DOI: 10.3390/su15032297. |
| [47] | Ronchi, S., Salata, S., and Arcidiacono, A. (2020). Which urban design parameters provide climate-proof cities. An application of the Urban Cooling InVEST Model in the city of Milan comparing historical planning morphologies. Sustainable Cities and Society 63: 102459. DOI: 10.1016/j.scs.2020.102459. |
| [48] | Zheng, X., Chen, L., and Yang, J. (2023). Simulation framework for early design guidance of urban streets to improve outdoor thermal comfort and building energy efficiency in summer. Building and Environment 228: 109815. DOI: 10.1016/j.buildenv.2022.109815. |
| [49] | Zhao, X., Zhou, Y., Chen, W., et al. (2021). Mapping hourly population dynamics using remotely sensed and geospatial data: a case study in Beijing, China. GIScience & Remote Sensing 58(5): 717−732. DOI: 10.1080/15481603.2021.1935128. |
| [50] | Hu, T., Yang, J., Li, X., et al. (2016). Mapping urban land use by using landsat images and open social data. Remote Sensing 8(2): 151. DOI: 10.3390/rs8020151. |
| [51] | Arehart, J.H., Pomponi, F., D'Amico, B., et al. (2022). Structural material demand and associated embodied carbon emissions of the United States building stock: 2020–2100. Resources, Conservation and Recycling 186: 106583. DOI: 10.1016/j.resconrec.2022.106583. |
| [52] | Chen, F., Kusaka, H., Bornstein, R., et al. (2011). The integrated WRF/urban modelling system: development, evaluation, and applications to urban environmental problems. International Journal of Climatology 31(2): 273−288. DOI: 10.1002/joc.2158. |
| [53] | Chen, W., Zhou, Y., Stokes, E.C., et al. (2023). Large-scale urban building function mapping by integrating multi-source web-based geospatial data. Geo-spatial Information Science:1-15. DOI: 10.1080/10095020.2023.2264342. |
| [54] | Chen, B., Tu, Y., Song, Y., et al. (2021). Mapping essential urban land use categories with open big data: Results for five metropolitan areas in the United States of America. ISPRS Journal of Photogrammetry and Remote Sensing 178: 203−218. DOI: 10.1016/j.isprsjprs.2021.06.010. |
| [55] | Zhang, W., Li, W., Zhang, C., et al. (2017). Parcel-based urban land use classification in megacity using airborne LiDAR, high resolution orthoimagery, and Google Street View. Computers, Environment and Urban Systems 64: 215−228. DOI: 10.1016/j.compenvurbsys.2017.03.001. |
| [56] | Heris, M.P., Foks, N.L., Bagstad, K.J., et al. (2020). A rasterized building footprint dataset for the United States. Scientific Data 7(1): 207. DOI: 10.1038/s41597-020-0542-3. |
| [57] | Microsoft (2018). Microsoft buildings footprint training data with heights. https://www.arcgis.com/home/item.html?id=f40326b0dea54330ae39584012807126. |
| [58] | Pandey, B., Brelsford, C., and Seto, K.C. (2022). Infrastructure inequality is a characteristic of urbanization. Proceedings of the National Academy of Sciences 119(15): e2119890119. DOI: 10.1073/pnas.2119890119. |
| [59] | Brelsford, C., Lobo, J., Hand, J., et al. (2017). Heterogeneity and scale of sustainable development in cities. Proceedings of the National Academy of Sciences 114(34): 8963−8968. DOI: 10.1073/pnas.1606033114. |
| Che Y., Li X., Liu X., et al., (2024). Mapping of individual building heights reveals the large gap of urban-rural living spaces in the contiguous US. The Innovation Geoscience 2(2): 100069. https://doi.org/10.59717/j.xinn-geo.2024.100069 |
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Spatial variations of building heights in the conterminous US
Quantification of the spatial pattern of building heights within the city in the conterminous US
Comparison of per capita building volume and inequality between urban and rural areas
Comparison of per capita building volume and area between urban and rural areas in the US.