8,983
Views
24
CrossRef citations to date
0
Altmetric
Secondary Literature Review Article

Mapping essential urban land use categories (EULUC) using geospatial big data: Progress, challenges, and opportunities

ORCID Icon, &
Pages 410-441 | Received 27 Apr 2021, Accepted 01 Jun 2021, Published online: 05 Jul 2021

References

  • Abbasi, A., Rashidi, T. H., Maghrebi, M., & Waller, S. T. (2015). Utilising location based social media in travel survey methods: Bringing Twitter data into the play. In, Proceedings of the 8th ACM SIGSPATIAL international workshop on location-based social networks (pp. 1–9), Bellevue, WA, USA.
  • Abdi, H., & Williams, L. J. (2010). Principal component analysis. Wiley Interdisciplinary Reviews. Computational Statistics, 2, 433–459.
  • Akar, Ö. (2018). The Rotation Forest algorithm and object-based classification method for land use mapping through UAV images. Geocarto International, 33, 538–553.
  • Al-Najjar, H. A., Kalantar, B., Pradhan, B., Saeidi, V., Halin, A. A., Ueda, N., & Mansor, S. (2019). Land cover classification from fused DSM and UAV images using convolutional neural networks. Remote Sensing, 11, 1461.
  • Antonarakis, A., Richards, K. S., & Brasington, J. (2008). Object-based land cover classification using airborne LiDAR. Remote Sensing of Environment, 112, 2988–2998.
  • Arino, O., Ramos Perez, J. J., Kalogirou, V., Bontemps, S., Defourny, P., & Van Bogaert, E. (2012). Global land cover map for 2009 (GlobCover 2009). European Space Agency (ESA) & Université Catholique de Louvain (UCL): Frascati, Italy.
  • Banzhaf, E., Kabisch, S., Knapp, S., Rink, D., Wolff, M., & Kindler, A. (2017). Integrated research on land-use changes in the face of urban transformations–an analytic framework for further studies. Land Use Policy, 60, 403–407.
  • Bao, H., Ming, D., Guo, Y., Zhang, K., Zhou, K., & Du, S. (2020). DFCNN-Based Semantic Recognition of Urban Functional Zones by Integrating Remote Sensing Data and POI Data. Remote Sensing, 12, 1088.
  • Barnsley, M. J., & Barr, S. L. (1996). Inferring urban land use from satellite sensor images using kernel-based spatial reclassification. Photogrammetric Engineering and Remote Sensing, 62, 949–958.
  • Bartholome, E., & Belward, A. S. (2005). GLC2000: A new approach to global land cover mapping from Earth observation data. International Journal of Remote Sensing, 26, 1959–1977.
  • Batty, M., & Longley, P. (1988). The morphology of urban land use. Environment and Planning. B, Planning & Design, 15, 461–488.
  • Beelen, R., Hoek, G., Vienneau, D., Eeftens, M., Dimakopoulou, K., Pedeli, X., … Marcon, A. (2013). Development of NO2 and NOx land use regression models for estimating air pollution exposure in 36 study areas in Europe–the ESCAPE project. Atmospheric Environment, 72, 10–23.
  • Blaschke, T., Hay, G. J., Weng, Q., & Resch, B. (2011). Collective sensing: Integrating geospatial technologies to understand urban systems—An overview. Remote Sensing, 3, 1743–1776.
  • Boarnet, M., & Crane, R. (2001). The influence of land use on travel behavior: Specification and estimation strategies. Transportation Research Part A: Policy and Practice, 35, 823–845.
  • Bu, H., Meng, W., Zhang, Y., & Wan, J. (2014). Relationships between land use patterns and water quality in the Taizi River basin, China. Ecological Indicators, 41, 187–197.
  • Buchhorn, M., Lesiv, M., Tsendbazar, N.-E., Herold, M., Bertels, L., & Smets, B. (2020). Copernicus global land cover layers—collection 2. Remote Sensing, 12, 1044.
  • Cao, R., Zhu, J., Tu, W., Li, Q., Cao, J., Liu, B., … Qiu, G. (2018). Integrating aerial and street view images for urban land use classification. Remote Sensing, 10, 1553.
  • Cao, X., Chen, J., Imura, H., & Higashi, O. (2009). A SVM-based method to extract urban areas from DMSP-OLS and SPOT VGT data. Remote Sensing of Environment, 113, 2205–2209.
  • Castle, C. J., & Crooks, A. T. (2006). Principles and concepts of agent-based modelling for developing geospatial simulations. Working Paper 110. Centre For Advanced Spatial Analysis, University College London, London, UK.
  • Chalkias, C., Petrakis, M., Psiloglou, B., & Lianou, M. (2006). Modelling of light pollution in suburban areas using remotely sensed imagery and GIS. Journal of Environmental Management, 79, 57–63.
  • Chen, B., Chen, L., Lu, M., & Xu, B. (2017a). Wetland mapping by fusing fine spatial and hyperspectral resolution images. Ecological Modelling, 353, 95–106.
  • Chen, B., Huang, B., & Xu, B. (2017b). Multi-source remotely sensed data fusion for improving land cover classification. ISPRS Journal of Photogrammetry and Remote Sensing, 124, 27–39.
  • Chen, B., Jin, Y., Scaduto, E., Moritz, M. A., Goulden, M. L., & Randerson, J. T. (2021a). Climate, fuel, and land use shaped the spatial pattern of wildfire in California’s Sierra Nevada. Journal of Geophysical Research: Biogeosciences, 126, e2020JG005786.
  • Chen, B., Nie, Z., Chen, Z., & Xu, B. (2017c). Quantitative estimation of 21st-century urban greenspace changes in Chinese populous cities. Science of the Total Environment, 609, 956–965.
  • Chen, B., Song, Y., Huang, B., & Xu, B. (2020). A novel method to extract urban human settlements by integrating remote sensing and mobile phone locations. Science of Remote Sensing, 1, 100003.
  • Chen, B., Song, Y., Jiang, T., Chen, Z., Huang, B., & Xu, B. (2018). Real-time estimation of population exposure to PM2. 5 using Mobile-and station-based big data. International Journal of Environmental Research and Public Health, 15, 573.
  • Chen, B., Tu, Y., Song, Y., Theobald David, M., Zhang, T., Ren, Z., … Xu, B. (2021b). 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 (In press).
  • Chen, J., Chen, J., Liao, A., Cao, X., Chen, L., Chen, X., … Lu, M. (2015). Global land cover mapping at 30 m resolution: A POK-based operational approach. ISPRS Journal of Photogrammetry and Remote Sensing, 103, 7–27.
  • Chen, Y., Liu, X., Li, X., Liu, X., Yao, Y., Hu, G., … Pei, F. (2017d). Delineating urban functional areas with building-level social media data: A dynamic time warping (DTW) distance based k-medoids method. Landscape and Urban Planning, 160, 48–60.
  • Cooley, S. W., Smith, L. C., Stepan, L., & Mascaro, J. (2017). Tracking dynamic northern surface water changes with high-frequency planet CubeSat imagery. Remote Sensing, 9, 1306.
  • De Colstoun, B., Huang, E., Wang, C., Tilton, P., Tan, J., Phillips, B., … Wolfe, R. (2017). Global Man-made Impervious Surface (GMIS) Dataset From Landsat, Palisades. NY: NASA Socioeconomic Data and Applications Center (SEDAC).
  • De Colstoun, E. C. B., & Walthall, C. L. (2006). Improving global scale land cover classifications with multi-directional POLDER data and a decision tree classifier. Remote Sensing of Environment, 100, 474–485.
  • Deng, Y., Wu, C., Li, M., & Chen, R. (2015). RNDSI: A ratio normalized difference soil index for remote sensing of urban/suburban environments. International Journal of Applied Earth Observation and Geoinformation, 39, 40–48.
  • Dong, R., Fang, W., Fu, H., Gan, L., Wang, J., & Gong, P. (2021). High-Resolution Land Cover Mapping Through Learning With Noise Correction. IEEE Transactions on Geoscience and Remote Sensing, 1–13.
  • Dovey, K., & Pafka, E. (2017). What is functional mix? An assemblage approach. Planning Theory & Practice, 18, 249–267.
  • Du, S., Du, S., Liu, B., Zhang, X., & Zheng, Z. (2020). Large-scale urban functional zone mapping by integrating remote sensing images and open social data. GIScience & Remote Sensing, 57, 411–430.
  • Du, X., & Huang, Z. (2017). Ecological and environmental effects of land use change in rapid urbanization: The case of hangzhou, China. Ecological Indicators, 81, 243–251.
  • Dugord, P.-A., Lauf, S., Schuster, C., & Kleinschmit, B. (2014). Land use patterns, temperature distribution, and potential heat stress risk–the case study Berlin, Germany. Computers, Environment and Urban Systems, 48, 86–98.
  • Duncan, M. J., Winkler, E., Sugiyama, T., Cerin, E., Leslie, E., & Owen, N. (2010). Relationships of land use mix with walking for transport: Do land uses and geographical scale matter? Journal of Urban Health, 87, 782–795.
  • Eeftens, M., Beelen, R., De Hoogh, K., Bellander, T., Cesaroni, G., Cirach, M., … De Nazelle, A. (2012). Development of land use regression models for PM2. 5, PM2. 5 absorbance, PM10 and PMcoarse in 20 European study areas; results of the ESCAPE project. Environmental Science & Technology, 46, 11195–11205.
  • Elvidge, C. D., Baugh, K., Zhizhin, M., Hsu, F. C., & Ghosh, T. (2017). VIIRS night-time lights. International Journal of Remote Sensing, 38, 5860–5879.
  • Elvidge, C. D., Baugh, K. E., Kihn, E. A., Kroehl, H. W., & Davis, E. R. (1997). Mapping city lights with nighttime data from the DMSP Operational Linescan System. Photogrammetric Engineering and Remote Sensing, 63, 727–734.
  • UCL-Geomatics (2017). Land Cover CCI Product User Guide Version 2.0. Available online:. http://maps.elie.ucl.ac.be/CCI/viewer/download/ESACCI-LC-Ph2-PUGv2_2.0.pdf
  • Feddema, J. J., Oleson, K. W., Bonan, G. B., Mearns, L. O., Buja, L. E., Meehl, G. A., & Washington, W. M. (2005). The importance of land-cover change in simulating future climates. Science, 310, 1674–1678.
  • Feng, Q., Zhu, D., Yang, J., & Li, B. (2019). Multisource hyperspectral and lidar data fusion for urban land-use mapping based on a modified two-branch convolutional neural network. ISPRS International Journal of Geo-Information, 8, 28.
  • Findell, K. L., Berg, A., Gentine, P., Krasting, J. P., Lintner, B. R., Malyshev, S., … Shevliakova, E. (2017). The impact of anthropogenic land use and land cover change on regional climate extremes. Nature Communications, 8, 1–10.
  • Foley, J. A., DeFries, R., Asner, G. P., Barford, C., Bonan, G., Carpenter, S. R., … Gibbs, H. K. (2005). Global consequences of land use. Science, 309, 570–574.
  • Foody, G. M. (2002). Status of land cover classification accuracy assessment. Remote Sensing of Environment, 80, 185–201.
  • Frias-Martinez, V., & Frias-Martinez, E. (2014). Spectral clustering for sensing urban land use using Twitter activity. Engineering Applications of Artificial Intelligence, 35, 237–245.
  • Friedl, M. A., Sulla-Menashe, D., Tan, B., Schneider, A., Ramankutty, N., Sibley, A., & Huang, X. (2010). MODIS Collection 5 global land cover: Algorithm refinements and characterization of new datasets. Remote Sensing of Environment, 114, 168–182.
  • Gamba, P., Aldrighi, M., & Stasolla, M. (2010). Robust extraction of urban area extents in HR and VHR SAR images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 4, 27–34.
  • Gámez-Virués, S., Perović, D. J., Gossner, M. M., Börschig, C., Blüthgen, N., De Jong, H., … Maier, G. (2015). Landscape simplification filters species traits and drives biotic homogenization. Nature Communications, 6, 1–8.
  • Gao, B.-C. (1996). NDWI—A normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sensing of Environment, 58, 257–266.
  • Gao, J., & O’Neill, B. C. (2020). Mapping global urban land for the 21st century with data-driven simulations and Shared Socioeconomic Pathways. Nature Communications, 11, 2302.
  • Gao, L., Du, Q., Zhang, B., Yang, W., & Wu, Y. (2013). A comparative study on linear regression-based noise estimation for hyperspectral imagery. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 6, 488–498.
  • Gilbert, E., & Karahalios, K. (2009). Predicting tie strength with social media. In Proceedings of the SIGCHI conference on human factors in computing systems (pp. 211–220), Boston, MA, USA.
  • Gong, P., Chen, B., Li, X., Liu, H., Wang, J., Bai, Y., … Feng, S. (2020a). Mapping essential urban land use categories in China (EULUC-China): Preliminary results for 2018. Science Bulletin, 65, 182–187.
  • Gong, P., & Howarth, P. J. (1990). The use of structural information for improving land-cover classification accuracies at the rural-urban fringe. Photogrammetric Engineering and Remote Sensing, 56, 67–73.
  • Gong, P., & Howarth, P. J. (1992). Frequency-based contextual classification and gray-level vector reduction for land-use identification. Photogrammetric Engineering and Remote Sensing, 58, 423–437.
  • Gong, P., Li, X., Wang, J., Bai, Y., Chen, B., Hu, T., … Zhang, W. (2020b). Annual maps of global artificial impervious area (GAIA) between 1985 and 2018. Remote Sensing of Environment, 236, 111510.
  • Gong, P., Liang, S., Carlton, E. J., Jiang, Q., Wu, J., Wang, L., & Remais, J. V. (2012). Urbanisation and health in China. The Lancet, 379, 843–852.
  • Gong, P., Liu, H., Zhang, M., Li, C., Wang, J., Huang, H., … Bai, Y. (2019). Stable classification with limited sample: Transferring a 30-m resolution sample set collected in 2015 to mapping 10-m resolution global land cover in 2017. Science Bulletin, 64, 370–373.
  • Gong, P., Marceau, D. J., & Howarth, P. J. (1992). A comparison of spatial feature extraction algorithms for land-use classification with SPOT HRV data. Remote Sensing of Environment, 40, 137–151.
  • Gong, P., Wang, J., Yu, L., Zhao, Y., Zhao, Y., Liang, L., … Liu, S. (2013). Finer resolution observation and monitoring of global land cover: First mapping results with Landsat TM and ETM+ data. International Journal of Remote Sensing, 34, 2607–2654.
  • Goodchild, M. F. (2007). Citizens as sensors: The world of volunteered geography. GeoJournal, 69, 211–221.
  • Goodchild, M. F., & Li, L. (2012). Assuring the quality of volunteered geographic information. Spatial Statistics, 1, 110–120.
  • Grippa, T., Georganos, S., Zarougui, S., Bognounou, P., Diboulo, E., Forget, Y., … Wolff, E. (2018). Mapping urban land use at street block level using openstreetmap, remote sensing data, and spatial metrics. ISPRS International Journal of Geo-Information, 7, 246.
  • Groeneveld, J., Müller, B., Buchmann, C. M., Dressler, G., Guo, C., Hase, N., … Lauf, T. (2017). Theoretical foundations of human decision-making in agent-based land use models–A review. Environmental Modelling and Software, 87, 39–48.
  • Guan, D., Li, H., Inohae, T., Su, W., Nagaie, T., & Hokao, K. (2011). Modeling urban land use change by the integration of cellular automaton and Markov model. Ecological Modelling, 222, 3761–3772.
  • Guan, Q., Cheng, S., Pan, Y., Yao, Y., & Zeng, W. (2021). Sensing Mixed Urban Land-Use Patterns Using Municipal Water Consumption Time Series. Annals of the American Association of Geographers, 111, 68–86.
  • Hagenauer, J., & Helbich, M. (2012). Mining urban land-use patterns from volunteered geographic information by means of genetic algorithms and artificial neural networks. International Journal of Geographical Information Science, 26, 963–982.
  • Handy, S. L., Boarnet, M. G., Ewing, R., & Killingsworth, R. E. (2002). How the built environment affects physical activity: Views from urban planning. American Journal of Preventive Medicine, 23, 64–73.
  • Hansen, M. C., DeFries, R. S., Townshend, J. R., & Sohlberg, R. (2000). Global land cover classification at 1 km spatial resolution using a classification tree approach. International Journal of Remote Sensing, 21, 1331–1364.
  • Hansen, M. C., Potapov, P. V., Moore, R., Hancher, M., Turubanova, S. A., Tyukavina, A., … Loveland, T. R. (2013). High-resolution global maps of 21st-century forest cover change. Science, 342, 850–853.
  • He, C., Shi, P., Chen, J., Li, X., Pan, Y., Li, J., … Li, J. (2005). Developing land use scenario dynamics model by the integration of system dynamics model and cellular automata model. Science in China Series D: Earth Sciences, 48, 1979–1989.
  • Henderson, S. B., Beckerman, B., Jerrett, M., & Brauer, M. (2007). Application of land use regression to estimate long-term concentrations of traffic-related nitrogen oxides and fine particulate matter. Environmental Science & Technology, 41, 2422–2428.
  • Herold, M., Mayaux, P., Woodcock, C., Baccini, A., & Schmullius, C. (2008). Some challenges in global land cover mapping: An assessment of agreement and accuracy in existing 1 km datasets. Remote Sensing of Environment, 112, 2538–2556.
  • Heusinkveld, B. G., Steeneveld, G. V., Van Hove, L., Jacobs, C., & Holtslag, A. (2014). Spatial variability of the Rotterdam urban heat island as influenced by urban land use. Journal of Geophysical Research: Atmospheres, 119, 677–692.
  • Hoek, G., Beelen, R., De Hoogh, K., Vienneau, D., Gulliver, J., Fischer, P., & Briggs, D. (2008). A review of land-use regression models to assess spatial variation of outdoor air pollution. Atmospheric Environment, 42, 7561–7578.
  • Hu, S., & Wang, L. (2013). Automated urban land-use classification with remote sensing. International Journal of Remote Sensing, 34, 790–803.
  • Hu, T., Yang, J., Li, X., & Gong, P. (2016). Mapping urban land use by using landsat images and open social data. Remote Sensing, 8, 151.
  • Huang, B., Zhang, H., & Yu, L. (2012). Improving Landsat ETM+ urban area mapping via spatial and angular fusion with MISR multi-angle observations. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 5, 101–109.
  • Huang, B., Zhao, B., & Song, Y. (2018). Urban land-use mapping using a deep convolutional neural network with high spatial resolution multispectral remote sensing imagery. Remote Sensing of Environment, 214, 73–86.
  • Huang, X., Yang, J., Li, J., & Wen, D. (2021). Urban functional zone mapping by integrating high spatial resolution nighttime light and daytime multi-view imagery. ISPRS Journal of Photogrammetry and Remote Sensing, 175, 403–415.
  • Huete, A., Didan, K., Miura, T., Rodriguez, E. P., Gao, X., & Ferreira, L. G. (2002). Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing of Environment, 83, 195–213.
  • Jia, P., Pan, X., Liu, F., He, P., Zhang, W., Liu, L., … Chen, L. (2021). Land use mix in the neighbourhood and childhood obesity. Obesity Reviews, 22, e13098.
  • Jia, Y., Ge, Y., Ling, F., Guo, X., Wang, J., Wang, L., … Li, X. (2018). Urban land use mapping by combining remote sensing imagery and mobile phone positioning data. Remote Sensing, 10, 446.
  • Jusuf, S. K., Wong, N. H., Hagen, E., Anggoro, R., & Hong, Y. (2007). The influence of land use on the urban heat island in Singapore. Habitat International, 31, 232–242.
  • Kaplan, A. M., & Haenlein, M. (2010). Users of the world, unite! The challenges and opportunities of Social Media. Business Horizons, 53, 59–68.
  • Kennedy, R. E., Yang, Z., & Cohen, W. B. (2010). Detecting trends in forest disturbance and recovery using yearly Landsat time series: 1. LandTrendr—Temporal segmentation algorithms. Remote Sensing of Environment, 114, 2897–2910.
  • LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521, 436–444.
  • Lee, S.-W., Hwang, S.-J., Lee, S.-B., Hwang, H.-S., & Sung, H.-C. (2009). Landscape ecological approach to the relationships of land use patterns in watersheds to water quality characteristics. Landscape and Urban Planning, 92, 80–89.
  • Lesiv, M., Fritz, S., McCallum, I., Tsendbazar, N., Herold, M., Pekel, J.-F., … Van De Kerchove, R. (2017). Evaluation of ESA CCI prototype land cover map at 20 m. IIASA Working Paper Series, WP-17-021. International Institute for Aplplied Systems Analysis: Laxenburg, Austria.
  • Li, W., Bai, Y., Zhou, W., Han, C., & Han, L. (2015). Land use significantly affects the distribution of urban green space: Case study of Shanghai, China. Journal of Urban Planning and Development, 141, A4014001.
  • Li, X., & Gong, P. (2016). Urban growth models: Progress and perspective. Science Bulletin, 61, 1637–1650.
  • Li, X., Levin, N., Xie, J., & Li, D. (2020a). Monitoring hourly night-time light by an unmanned aerial vehicle and its implications to satellite remote sensing. Remote Sensing of Environment, 247, 111942.
  • Li, X., & Li, D. (2014). Can night-time light images play a role in evaluating the Syrian Crisis? International Journal of Remote Sensing, 35, 6648–6661.
  • Li, X., & Yeh, A. G.-O. (2000). Modelling sustainable urban development by the integration of constrained cellular automata and GIS. International Journal of Geographical Information Science, 14, 131–152.
  • Li, X., Zhao, L., Li, D., & Xu, H. (2018). Mapping urban extent using Luojia 1-01 nighttime light imagery. Sensors, 18, 3665.
  • Li, X., & Zhou, Y. (2017). Urban mapping using DMSP/OLS stable night-time light: A review. International Journal of Remote Sensing, 38, 6030–6046.
  • Li, X., Zhou, Y., Gong, P., Seto, K. C., & Clinton, N. (2020b). Developing a method to estimate building height from Sentinel-1 data. Remote Sensing of Environment, 240, 111705.
  • Liu, H., Gong, P., Wang, J., Wang, X., Ning, G., & Xu, B. (2021). Production of global daily seamless data cubes and quantification of global land cover change from 1985 to 2020-iMap World 1.0. Remote Sensing of Environment, 258, 112364.
  • Liu, R., Zhang, K., Zhang, Z., & Borthwick, A. G. (2014). Land-use suitability analysis for urban development in Beijing. Journal of Environmental Management, 145, 170–179.
  • Liu, S., Qi, Z., Li, X., & Yeh, A. G.-O. (2019). Integration of convolutional neural networks and object-based post-classification refinement for land use and land cover mapping with optical and sar data. Remote Sensing, 11, 690.
  • Liu, S., & Shi, Q. (2020). Local climate zone mapping as remote sensing scene classification using deep learning: A case study of metropolitan China. ISPRS Journal of Photogrammetry and Remote Sensing, 164, 229–242.
  • Liu, X. (2008). Airborne LiDAR for DEM generation: Some critical issues. Progress in Physical Geography, 32, 31–49.
  • Liu, X., He, J., Yao, Y., Zhang, J., Liang, H., Wang, H., & Hong, Y. (2017a). Classifying urban land use by integrating remote sensing and social media data. International Journal of Geographical Information Science, 31, 1675–1696.
  • Liu, X., Hu, G., Chen, Y., Li, X., Xu, X., Li, S., … Wang, S. (2018). High-resolution multi-temporal mapping of global urban land using Landsat images based on the Google Earth Engine Platform. Remote Sensing of Environment, 209, 227–239.
  • Liu, X., Li, X., Chen, Y., Tan, Z., Li, S., & Ai, B. (2010). A new landscape index for quantifying urban expansion using multi-temporal remotely sensed data. Landscape Ecology, 25, 671–682.
  • Liu, X., Liang, X., Li, X., Xu, X., Ou, J., Chen, Y., … Pei, F. (2017b). A future land use simulation model (FLUS) for simulating multiple land use scenarios by coupling human and natural effects. Landscape and Urban Planning, 168, 94–116.
  • Liu, X., & Long, Y. (2016). Automated identification and characterization of parcels with OpenStreetMap and points of interest. Environment and Planning. B, Planning & Design, 43, 341–360.
  • Liu, Y., Wang, F., Xiao, Y., & Gao, S. (2012). Urban land uses and traffic ‘source-sink areas’: Evidence from GPS-enabled taxi data in Shanghai. Landscape and Urban Planning, 106, 73–87.
  • Lo, C., & Choi, J. (2004). A hybrid approach to urban land use/cover mapping using Landsat 7 Enhanced Thematic Mapper Plus (ETM+) images. International Journal of Remote Sensing, 25, 2687–2700.
  • Louail, T., Lenormand, M., Ros, O. G. C., Picornell, M., Herranz, R., Frias-Martinez, E., … Barthelemy, M. (2014). From mobile phone data to the spatial structure of cities. Scientific Reports, 4, 1–12.
  • Loveland, T. R., Reed, B. C., Brown, J. F., Ohlen, D. O., Zhu, Z., Yang, L., & Merchant, J. W. (2000). Development of a global land cover characteristics database and IGBP DISCover from 1 km AVHRR data. International Journal of Remote Sensing, 21, 1303–1330.
  • Lu, D., & Weng, Q. (2006). Use of impervious surface in urban land-use classification. Remote Sensing of Environment, 102, 146–160.
  • Lucht, W., Schaaf, C. B., & Strahler, A. H. (2000). An algorithm for the retrieval of albedo from space using semiempirical BRDF models. IEEE Transactions on Geoscience and Remote Sensing, 38, 977–998.
  • Ma, T., Zhou, C., Pei, T., Haynie, S., & Fan, J. (2012). Quantitative estimation of urbanization dynamics using time series of DMSP/OLS nighttime light data: A comparative case study from China’s cities. Remote Sensing of Environment, 124, 99–107.
  • Malarvizhi, K., Kumar, S. V., & Porchelvan, P. (2016). Use of high resolution google earth satellite imagery in landuse map preparation for urban related applications. Procedia Technology, 24, 1835–1842.
  • Malinverni, E. S., Tassetti, A. N., Mancini, A., Zingaretti, P., Frontoni, E., & Bernardini, A. (2011). Hybrid object-based approach for land use/land cover mapping using high spatial resolution imagery. International Journal of Geographical Information Science, 25, 1025–1043.
  • Man, Q., Dong, P., & Guo, H. (2015a). Pixel- and feature-level fusion of hyperspectral and lidar data for urban land-use classification. International Journal of Remote Sensing, 36, 1618–1644.
  • Man, Q., Dong, P., & Guo, H. (2015b). Pixel-and feature-level fusion of hyperspectral and lidar data for urban land-use classification. International Journal of Remote Sensing, 36, 1618–1644.
  • Masiliūnas, D., Tsendbazar, N.-E., Herold, M., Lesiv, M., Buchhorn, M., & Verbesselt, J. (2021). Global land characterisation using land cover fractions at 100 m resolution. Remote Sensing of Environment, 259, 112409.
  • Matthews, R. B., Gilbert, N. G., Roach, A., Polhill, J. G., & Gotts, N. M. (2007). Agent-based land-use models: A review of applications. Landscape Ecology, 22, 1447–1459.
  • Mesev, T., Longley, P. A., Batty, M., & Xie, Y. (1995). Morphology from imagery: Detecting and measuring the density of urban land use. Environment & Planning A, 27, 759–780.
  • Mikolov, T., Sutskever, I., Chen, K., Corrado, G., & Dean, J. (2013). Distributed representations of words and phrases and their compositionality. arXiv Preprint arXiv, 1310, 4546.
  • Myint, S. W., Gober, P., Brazel, A., Grossman-Clarke, S., & Weng, Q. (2011). Per-pixel vs. object-based classification of urban land cover extraction using high spatial resolution imagery. Remote Sensing of Environment, 115, 1145–1161.
  • Nations, U. (2018). 2018 Revision of World Urbanization Prospects. In. New York: The Population Division of the Department of Economic and Social Affairs.
  • Özkan, C., & Sunar Erbek, F. (2005). Comparing feature extraction techniques for urban land‐use classification. International Journal of Remote Sensing, 26, 747–757.
  • Pacifici, F., Chini, M., & Emery, W. J. (2009). A neural network approach using multi-scale textural metrics from very high-resolution panchromatic imagery for urban land-use classification. Remote Sensing of Environment, 113, 1276–1292.
  • Pan, G., Qi, G., Wu, Z., Zhang, D., & Li, S. (2012). Land-use classification using taxi GPS traces. IEEE Transactions on Intelligent Transportation Systems, 14, 113–123.
  • Pan, S. J., & Yang, Q. (2009). A survey on transfer learning. IEEE Transactions on Knowledge and Data Engineering, 22, 1345–1359.
  • Pan, Y., Zeng, W., Guan, Q., Yao, Y., Liang, X., Yue, H., … Wang, J. (2020). Spatiotemporal dynamics and the contributing factors of residential vacancy at a fine scale: A perspective from municipal water consumption. Cities, 103, 102745.
  • Paola, J. D., & Schowengerdt, R. A. (1995). A detailed comparison of backpropagation neural network and maximum-likelihood classifiers for urban land use classification. IEEE Transactions on Geoscience and Remote Sensing, 33, 981–996.
  • Parker, D. C., Manson, S. M., Janssen, M. A., Hoffmann, M. J., & Deadman, P. (2003). Multi-agent systems for the simulation of land-use and land-cover change: A review. Annals of the Association of American Geographers, 93, 314–337.
  • Patz, J. A., Daszak, P., Tabor, G. M., Aguirre, A. A., Pearl, M., Epstein, J., … Molyneux, D. (2004). Unhealthy landscapes: Policy recommendations on land use change and infectious disease emergence. Environmental Health Perspectives, 112, 1092–1098.
  • Pei, T., Sobolevsky, S., Ratti, C., Shaw, S.-L., Li, T., & Zhou, C. (2014). A new insight into land use classification based on aggregated mobile phone data. International Journal of Geographical Information Science, 28, 1988–2007.
  • Pelizari, P. A., Spröhnle, K., Geiß, C., Schoepfer, E., Plank, S., & Taubenböck, H. (2018). Multi-sensor feature fusion for very high spatial resolution built-up area extraction in temporary settlements. Remote Sensing of Environment, 209, 793–807.
  • Pesaresi, M., Huadong, G., Blaes, X., Ehrlich, D., Ferri, S., Gueguen, L., … Lu, L. (2013). A global human settlement layer from optical HR/VHR RS data: Concept and first results. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 6, 2102–2131.
  • Pielke Sr, R. A., Pitman, A., Niyogi, D., Mahmood, R., McAlpine, C., Hossain, F., … Fall, S. (2011). Land use/land cover changes and climate: Modeling analysis and observational evidence. Wiley Interdisciplinary Reviews: Climate Change, 2, 828–850.
  • Poghosyan, A., & Golkar, A. (2017). CubeSat evolution: Analyzing CubeSat capabilities for conducting science missions. Progress in Aerospace Sciences, 88, 59–83.
  • Puig-Suari, J., Turner, C., & Ahlgren, W. (2001). Development of the standard CubeSat deployer and a CubeSat class PicoSatellite. In, 2001 IEEE aerospace conference proceedings (Cat. No. 01TH8542) (pp. 1/347–341/353 vol. 341): IEEE, Big Sky, MT, USA.
  • Rockström, J., Steffen, W., Noone, K., Persson, Å., Chapin III, F. S., Lambin, E., … Schellnhuber, H. J. (2009). Planetary boundaries: Exploring the safe operating space for humanity. Ecology and Society, 14, 32.
  • Rogers, A., & Kearney, M. (2004). Reducing signature variability in unmixing coastal marsh Thematic Mapper scenes using spectral indices. International Journal of Remote Sensing, 25, 2317–2335.
  • Ruiz Hernandez, I. E., & Shi, W. (2018). A Random Forests classification method for urban land-use mapping integrating spatial metrics and texture analysis. International Journal of Remote Sensing, 39, 1175–1198.
  • Running, S. W. (2008). Ecosystem disturbance, carbon, and climate. Science, 321, 652–653.
  • Sakieh, Y., Salmanmahiny, A., Jafarnezhad, J., Mehri, A., Kamyab, H., & Galdavi, S. (2015). Evaluating the strategy of decentralized urban land-use planning in a developing region. Land Use Policy, 48, 534–551.
  • Schmidhuber, J. (2015). Deep learning in neural networks: An overview. Neural Networks, 61, 85–117.
  • Schneider, A., Friedl, M. A., & Potere, D. (2010). Mapping global urban areas using MODIS 500-m data: New methods and datasets based on ‘urban ecoregions’. Remote Sensing of Environment, 114, 1733–1746.
  • Schneider, A., & Woodcock, C. E. (2008). Compact, dispersed, fragmented, extensive? A comparison of urban growth in twenty-five global cities using remotely sensed data, pattern metrics and census information. Urban Studies, 45, 659–692.
  • Seto, K. C., & Fragkias, M. (2005). Quantifying spatiotemporal patterns of urban land-use change in four cities of China with time series landscape metrics. Landscape Ecology, 20, 871–888.
  • Shi, Y., Ren, C., Lau, K. K.-L., & Ng, E. (2019). Investigating the influence of urban land use and landscape pattern on PM2. 5 spatial variation using mobile monitoring and WUDAPT. Landscape and Urban Planning, 189, 15–26.
  • Silva, E. A., & Clarke, K. C. (2002). Calibration of the SLEUTH urban growth model for Lisbon and Porto, Portugal. Computers, Environment and Urban Systems, 26, 525–552.
  • Son, J.-Y., Kim, H., & Bell, M. L. (2015). Does urban land-use increase risk of asthma symptoms? Environmental Research, 142, 309–318.
  • Song, Y., Chen, B., & Kwan, M.-P. (2020). How does urban expansion impact people’s exposure to green environments? A comparative study of 290 Chinese cities. Journal of Cleaner Production, 246, 119018.
  • Song, Y., Huang, B., Cai, J., & Chen, B. (2018). Dynamic assessments of population exposure to urban greenspace using multi-source big data. Science of the Total Environment, 634, 1315–1325.
  • Srivastava, S., Vargas-Munoz, J. E., & Tuia, D. (2019). Understanding urban landuse from the above and ground perspectives: A deep learning, multimodal solution. Remote Sensing of Environment, 228, 129–143.
  • Stone, B., & Norman, J. M. (2006). Land use planning and surface heat island formation: A parcel-based radiation flux approach. Atmospheric Environment, 40, 3561–3573.
  • Su, M., Guo, R., Chen, B., Hong, W., Wang, J., Feng, Y., & Xu, B. (2020). Sampling Strategy for Detailed Urban Land Use Classification: A Systematic Analysis in Shenzhen. Remote Sensing, 12, 1497.
  • Sun, J., Wang, H., Song, Z., Lu, J., Meng, P., & Qin, S. (2020). Mapping Essential Urban Land Use Categories in Nanjing by Integrating Multi-Source Big Data. Remote Sensing, 12, 2386.
  • Tateishi, R., Uriyangqai, B., Al-Bilbisi, H., Ghar, M. A., Tsend-Ayush, J., Kobayashi, T., … Alsaaideh, B. (2011). Production of global land cover data–GLCNMO. International Journal of Digital Earth, 4, 22–49.
  • Theobald, D. M. (2014). Development and Applications of a Comprehensive Land Use Classification and Map for the US. PLoS ONE, 9, e94628.
  • Theobald, D. M., Kennedy, C., Chen, B., Oakleaf, J., Baruch-Mordo, S., & Kiesecker, J. (2020). Earth transformed: Detailed mapping of global human modification from 1990 to 2017. Earth System Science Data, 12, 1953–1972.
  • Treitz, P., & Rogan, J. (2004). Remote sensing for mapping and monitoring land-cover and land-use change-an introduction. Progress in Planning, 61, 269–279.
  • Tu, Y., Chen, B., Yu, L., Xin, Q., Gong, P., & Xu, B. (2021). How does urban expansion interact with cropland loss? A comparison of 14 Chinese cities from 1980 to 2015. Landscape Ecology, 36, 243–263.
  • Tu, Y., Chen, B., Zhang, T., & Xu, B. (2020). Regional Mapping of Essential Urban Land Use Categories in China: A Segmentation-Based Approach. Remote Sensing, 12, 1058.
  • Tucker, C. J. (1979). Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment, 8, 127–150.
  • Turner, B. L., Lambin, E. F., & Reenberg, A. (2007). The emergence of land change science for global environmental change and sustainability. Proceedings of the National Academy of Sciences, 104, 20666–20671.
  • Uriarte, M., Yackulic, C. B., Lim, Y., & Arce-Nazario, J. A. (2011). Influence of land use on water quality in a tropical landscape: A multi-scale analysis. Landscape Ecology, 26, 1151–1164.
  • van de Coevering, P., & Schwanen, T. (2006). Re-evaluating the impact of urban form on travel patternsin Europe and North-America. Transport Policy, 13, 229–239.
  • Verbesselt, J., Hyndman, R., Newnham, G., & Culvenor, D. (2010). Detecting trend and seasonal changes in satellite image time series. Remote Sensing of Environment, 114, 106–115.
  • Verburg, P. H., Van De Steeg, J., Veldkamp, A., & Willemen, L. (2009). From land cover change to land function dynamics: A major challenge to improve land characterization. Journal of Environmental Management, 90, 1327–1335.
  • Wakamiya, S., Lee, R., & Sumiya, K. (2011). Urban area characterization based on semantics of crowd activities in twitter. In International Conference on GeoSpatial Sematics (pp. 108–123): Springer, Brest, France.
  • Wang, J., Lin, Y., Glendinning, A., & Xu, Y. (2018). Land-use changes and land policies evolution in China’s urbanization processes. Land Use Policy, 75, 375–387.
  • Wang, P., Huang, C., Brown De Colstoun, E., Tilton, J., & Tan, B. (2017). Global Human Built-up And Settlement Extent (HBASE) Dataset From Landsat. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC)..
  • Wang, Y., Wang, T., Tsou, M.-H., Li, H., Jiang, W., & Guo, F. (2016). Mapping dynamic urban land use patterns with crowdsourced geo-tagged social media (Sina-Weibo) and commercial points of interest collections in Beijing, China. Sustainability, 8, 1202.
  • Weng, Q., & Yang, S. (2006). Urban air pollution patterns, land use, and thermal landscape: An examination of the linkage using GIS. Environmental Monitoring and Assessment, 117, 463–489.
  • White, R., Uljee, I., & Engelen, G. (2012). Integrated modelling of population, employment and land-use change with a multiple activity-based variable grid cellular automaton. International Journal of Geographical Information Science, 26, 1251–1280.
  • Whitmee, S., Haines, A., Beyrer, C., Boltz, F., Capon, A. G., de Souza Dias, B. F., … Head, P. (2015). Safeguarding human health in the Anthropocene epoch: Report of The Rockefeller Foundation–Lancet Commission on planetary health. The Lancet, 386, 1973–2028.
  • Wong, N. H., Jusuf, S. K., Syafii, N. I., Chen, Y., Hajadi, N., Sathyanarayanan, H., & Manickavasagam, Y. V. (2011). Evaluation of the impact of the surrounding urban morphology on building energy consumption. Solar Energy, 85, 57–71.
  • Wu, F. (2002). Calibration of stochastic cellular automata: The application to rural-urban land conversions. International Journal of Geographical Information Science, 16, 795–818.
  • Xie, Y., & Weng, Q. (2016). World energy consumption pattern as revealed by DMSP-OLS nighttime light imagery. GIScience & Remote Sensing, 53, 265–282.
  • Xu, B., & Gong, P. (2007). Land-use/land-cover classification with multispectral and hyperspectral EO-1 data. Photogrammetric Engineering & Remote Sensing, 73, 955–965.
  • Xu, B., & Gong, P. (2008). Noise estimation in a noise-adjusted principal component transformation and hyperspectral image restoration. Canadian Journal of Remote Sensing, 34, 271–286.
  • Xu, H. (2006). Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery. International Journal of Remote Sensing, 27, 3025–3033.
  • Xu, H. (2008). A new index for delineating built‐up land features in satellite imagery. International Journal of Remote Sensing, 29, 4269–4276.
  • Yan, Y., Schultz, M., & Zipf, A. (2019). An exploratory analysis of usability of Flickr tags for land use/land cover attribution. Geo-Spatial Information Science, 22, 12–22.
  • Yao, Y., Li, X., Liu, X., Liu, P., Liang, Z., Zhang, J., & Mai, K. (2017a). Sensing spatial distribution of urban land use by integrating points-of-interest and Google Word2Vec model. International Journal of Geographical Information Science, 31, 825–848.
  • Yao, Y., Liu, X., Li, X., Liu, P., Hong, Y., Zhang, Y., & Mai, K. (2017b). Simulating urban land-use changes at a large scale by integrating dynamic land parcel subdivision and vector-based cellular automata. International Journal of Geographical Information Science, 31, 2452–2479.
  • Yu, B., Shi, K., Hu, Y., Huang, C., Chen, Z., & Wu, J. (2015). Poverty evaluation using NPP-VIIRS nighttime light composite data at the county level in China. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8, 1217–1229.
  • Yu, X., Zhao, G., Chang, C., Yuan, X., & Heng, F. (2019). Bgvi: A new index to estimate street-side greenery using baidu street view image. Forests, 10, 3.
  • Yuan, C., Ng, E., & Norford, L. K. (2014). Improving air quality in high-density cities by understanding the relationship between air pollutant dispersion and urban morphologies. Building and Environment, 71, 245–258.
  • Yuan, J., Zheng, Y., & Xie, X. (2012). Discovering regions of different functions in a city using human mobility and POIs. In Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 186–194), Edmonton Alberta, Canada.
  • Zahnow, R. (2018). Mixed land use: Implications for violence and property crime. Los Angeles, CA: SAGE Publications Sage CA.
  • Zha, Y., Gao, J., & Ni, S. (2003). Use of normalized difference built-up index in automatically mapping urban areas from TM imagery. International Journal of Remote Sensing, 24, 583–594.
  • Zhai, W., Bai, X., Shi, Y., Han, Y., Peng, Z.-R., & Gu, C. (2019). Beyond Word2vec: An approach for urban functional region extraction and identification by combining Place2vec and POIs. Computers, Environment and Urban Systems, 74, 1–12.
  • Zhang, C., Sargent, I., Pan, X., Li, H., Gardiner, A., Hare, J., & Atkinson, P. M. (2018). An object-based convolutional neural network (OCNN) for urban land use classification. Remote Sensing of Environment, 216, 57–70.
  • Zhang, J., Li, X., Yao, Y., Hong, Y., He, J., Jiang, Z., & Sun, J. (2021). The Traj2Vec model to quantify residents’ spatial trajectories and estimate the proportions of urban land-use types. International Journal of Geographical Information Science, 35, 193–211.
  • Zhang, M. (2004). The role of land use in travel mode choice: Evidence from Boston and Hong Kong. Journal of the American Planning Association, 70, 344–360.
  • Zhang, Q., & Seto, K. C. (2011). Mapping urbanization dynamics at regional and global scales using multi-temporal DMSP/OLS nighttime light data. Remote Sensing of Environment, 115, 2320–2329.
  • Zhang, R., & Ma, J. (2009). Feature selection for hyperspectral data based on recursive support vector machines. International Journal of Remote Sensing, 30, 3669–3677.
  • Zhang, W., Li, W., Zhang, C., Hanink, D. M., Li, X., & Wang, W. (2017a). 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.
  • Zhang, X., Chen, G., Wang, W., Wang, Q., & Dai, F. (2017b). Object-based land-cover supervised classification for very-high-resolution UAV images using stacked denoising autoencoders. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10, 3373–3385.
  • Zhang, X., Liu, L., Chen, X., Gao, Y., Xie, S., & Mi, J. (2020a). GLC_FCS30: Global land-cover product with fine classification system at 30 m using time-series Landsat imagery. Earth System Science Data Discussions, 13, 1–31.
  • Zhang, X., Liu, L., Wu, C., Chen, X., Gao, Y., Xie, S., & Zhang, B. (2020b). Development of a global 30 m impervious surface map using multisource and multitemporal remote sensing datasets with the Google Earth Engine platform. Earth System Science Data, 12, 1625–1648.
  • Zhang, Y., Li, Q., Huang, H., Wu, W., Du, X., & Wang, H. (2017c). The combined use of remote sensing and social sensing data in fine-grained urban land use mapping: A case study in Beijing, China. Remote Sensing, 9, 865.
  • Zhong, Y., Su, Y., Wu, S., Zheng, Z., Zhao, J., Ma, A., … Pellikka, P. (2020). Open-source data-driven urban land-use mapping integrating point-line-polygon semantic objects: A case study of Chinese cities. Remote Sensing of Environment, 247, 111838.
  • Zhou, H., & Gao, H. (2020). The impact of urban morphology on urban transportation mode: A case study of Tokyo. Case Studies on Transport Policy, 8, 197–205.
  • Zhou, W. (2013). An object-based approach for urban land cover classification: Integrating LiDAR height and intensity data. IEEE Geoscience and Remote Sensing Letters, 10, 928–931.
  • Zhu, Z., & Woodcock, C. E. (2014). Continuous change detection and classification of land cover using all available Landsat data. Remote Sensing of Environment, 144, 152–171.
  • Zong, L., He, S., Lian, J., Bie, Q., Wang, X., Dong, J., & Xie, Y. (2020). Detailed Mapping of Urban Land Use Based on Multi-Source Data: A Case Study of Lanzhou. Remote Sensing, 12, 1987.