Urban Remote Sensing. Группа авторов

Urban Remote Sensing - Группа авторов


Скачать книгу
K., Zhang, X., Ren, S. and Sun, J., 2016. Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 770–778.

      17 Hu, T., Yang, J., Li, X. and Gong, P., 2016. Mapping urban land use by using Landsat images and open social data. Remote Sensing, 8(2): 151.

      18 Huang, H., Chen, Y., Clinton, N., Wang, J., Wang, X., Liu, C., Gong, P., Yang, J., Bai, Y., Zheng, Y. and Zhu, Z., 2017. Mapping major land cover dynamics in Beijing using all Landsat images in Google Earth Engine. Remote Sensing of Environment, 202: 166–176.

      19 Jiang, B., Ma, D., Yin, J. and Sandberg, M., 2016. Spatial distribution of city tweets and their densities. Geographical Analysis, 48(3): 337–351.

      20 Johnson, B. and Shepherd, J.M., 2018. An urban‐based climatology of winter precipitation in the northeast United States. Urban Climate, 24: 205–220.

      21 Jürgens, C. and Rashed, T. (eds.) 2010. Remote Sensing of Urban and Suburban Areas. Springer, 352p.

      22 Kabisch, N., Selsam, P., Kirsten, T., Lausch, A. and Bumberger, J., 2019. A multi‐sensor and multi‐temporal remote sensing approach to detect land cover change dynamics in heterogeneous urban landscapes. Ecological Indicators, 99: 273–282.

      23 Kalantar, B., Mansor, S.B., Sameen, M.I., Pradhan, B. and Shafri, H.Z., 2017. Drone‐based land‐cover mapping using a fuzzy unordered rule induction algorithm integrated into object‐based image analysis. International Journal of Remote Sensing, 38 (8–10): 2535–2556.

      24 Khan, S., Aragão, L. and Iriarte, J., 2017. A UAV–lidar system to map Amazonian rainforest and its ancient landscape transformations. International Journal of Remote Sensing, 38 (8–10): 2313–2330.

      25 Kuffer, M., Pfeffer, K. and Sliuzas, R., 2016. Slums from space ‐15 years of slum mapping using remote sensing. Remote Sensing, 8(6): 455.

      26 Lai, F. and Yang, X., 2020. Integrating spectral and non‐spectral information to improve urban settlement mapping in a large Latin‐American City. GIScience and Remote Sensing, 57(6): 830–844.

      27 LeCun, Y., Bengio, Y. and Hinton, G., 2015. Deep learning. Nature, 521 (7553): 436–444.

      28 Li, X., Gong, P. and Liang, L., 2015a. A 30‐year (1984–2013) record of annual urban dynamics of Beijing City derived from Landsat data. Remote Sensing of Environment, 166: 78–90.

      29 Li, X., Zhang, C., Li, W., Ricard, R., Meng, Q. and Zhang, W., 2015b. Assessing street‐level urban greenery using Google Street View and a modified green view index. Urban Forestry and Urban Greening, 14(3): 675–685.

      30 Li, X., Zhou, Y., Asrar, G.R., Mao, J., Li, X. and Li, W., 2017. Response of vegetation phenology to urbanization in the conterminous United States. Global Change Biology, 23(7): 2818–2830.

      31 Liu, X., Hu, G., Chen, Y., Li, X., Xu, X., Li, S., Pei, F. and 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.

      32 Maggiori, E., Tarabalka, Y., Charpiat, G. and Alliez, P., 2017. Convolutional neural networks for large‐scale remote‐sensing image classification. IEEE Transactions on Geoscience and Remote Sensing, 55(2): 645–657.

      33 Meng, X., Currit, N., Wang, L. and Yang, X., 2012. Detect residential buildings from LIDAR and aerial photographs through object‐oriented land‐use classification. Photogrammetric Engineering & Remote Sensing, 78(1): 35–44.

      34 Mennis, J., Mason, M. and Ambrus, A., 2018. Urban greenspace is associated with reduced psychological stress among adolescents: a Geographic Ecological Momentary Assessment (GEMA) analysis of activity space. Landscape and Urban Planning, 174: 1–9.

      35 Netzband, M., Stefanov, W. L. and Redman, C. L. (eds.) 2007. Applied Remote Sensing for Urban Planning, Governance and Sustainability. Springer, 220p.

      36 Norton, B.A., Coutts, A.M., Livesley, S.J., Harris, R.J., Hunter, A.M. and Williams, N.S., 2015. Planning for cooler cities: a framework to prioritise green infrastructure to mitigate high temperatures in urban landscapes. Landscape and Urban Planning, 134: 127–138.

      37 Patel, N.N., Angiuli, E., Gamba, P., Gaughan, A., Lisini, G., Stevens, F.R., Tatem, A.J. and Trianni, G., 2015. Multitemporal settlement and population mapping from Landsat using Google Earth Engine. International Journal of Applied Earth Observation and Geoinformation, 35: 199–208.

      38 Patidar, N. and Keshari, A.K., 2018. A multi‐model ensemble approach for quantifying sub‐pixel land cover fractions in the urban environments. International Journal of Remote Sensing, 39 (12): 3939–3962.

      39 Schneider, A., 2012. Monitoring land cover change in urban and peri‐urban areas using dense time stacks of Landsat satellite data and a data mining approach. Remote Sensing of Environment, 124: 689–704.

      40 Seiferling, I., Naik, N., Ratti, C. and Proulx, R., 2017. Green streets ‐ quantifying and mapping urban trees with street‐level imagery and computer vision. Landscape and Urban Planning, 165: 93–101.

      41 Seto, K.C., Sánchez‐Rodríguez, R. and Fragkias, M., 2010. The new geography of contemporary urbanization and the environment. Annual Review of Environment and Resources, 35: 167–194.

      42 Seto, K.C., Reenberg, A., Boone, C.G., Fragkias, M., Haase, D., Langanke, T., Marcotullio, P., Munroe, D.K., Olah, B. and Simon, D., 2012. Urban land teleconnections and sustainability. Proceedings of the National Academy of Sciences, 109 (20): 7687–7692.

      43 Seto, K.C., Golden, J.S., Alberti, M. and Turner, B.L., 2017. Sustainability in an urbanizing planet. Proceedings of the National Academy of Sciences, 114 (34): 8935–8938.

      44 Sharma, A., Liu, X., Yang, X. and Shi, D., 2017. A patch‐based convolutional neural network for remote sensing image classification. Neural Networks, 95: 19–28.

      45 Sharma, A., Liu, X. and Yang, X., 2018. Land cover classification from multi‐temporal, multi‐spectral remotely sensed imagery using patch‐based recurrent neural networks. Neural Networks, 105: 346–355.

      46 Shen, H., Lin, Y., Tian, Q., Xu, K. and Jiao, J., 2018. A comparison of multiple classifier combinations using different voting‐weights for remote sensing image classification. International Journal of Remote Sensing, 39 (11): 3705–3722.

      47 Shi, D. and Yang, X., 2017. Mapping vegetation and land cover in a large urban area using a multiple classifier system. International Journal of Remote Sensing, 38 (16): 4700–4721.

      48 Smits, P.C., 2002. Multiple classifier systems for supervised remote sensing image classification based on dynamic classifier selection. IEEE Transactions on Geoscience and Remote Sensing, 40(4): 801–813.

      49 Soille, P., Burger, A., De Marchi, D., Kempeneers, P., Rodriguez, D., Syrris, V. and Vasilev, V., 2018. A versatile data‐intensive computing platform for information retrieval from big geospatial data. Future Generation Computer Systems, 81: 30–40.

      50 Stokes, E.C. and Seto, K.C., 2019. Characterizing urban infrastructural transitions for the Sustainable Development Goals using multi‐temporal land, population, and nighttime light data. Remote Sensing of Environment, 234: 111430.

      51 Wang, Q. and Atkinson, P.M., 2018. Spatio‐temporal fusion for daily Sentinel‐2 images. Remote Sensing of Environment, 204: 31–42.

      52 Wang, J., Huang, B., Fu, D., Atkinson, P.M. and Zhang, X., 2016. Response of urban heat island to future urban expansion over the Beijing–Tianjin–Hebei metropolitan area. Applied Geography, 70: 26–36.

      53 Weng, Q. and Quattrochi, D. (eds.) 2007. Urban Remote Sensing. CRC Press, 412p.

      54 Weng Q., Quattrochi, D. and Gamba, P. (eds.) 2018. Urban Remote Sensing (2nd). CRC Press, 315p.

      55 Wulder, M.A., Loveland, T.R., Roy, D.P., Crawford, C.J., Masek, J.G., Woodcock, C.E., Allen, R.G., Anderson, M.C., Belward, A.S., Cohen, W.B. and Dwyer, J., 2019. Current status of Landsat program, science, and applications. Remote Sensing of Environment, 225: 127–147.

      56 Yang, X., 2011. What is urban remote


Скачать книгу