A first Chinese building height estimate at 10 m resolution (CNBH-10 m) using multi-source earth observations and machine learning

[1]  L. Kaack,et al.  EUBUCCO v0.1: European building stock characteristics in a common and open database for 200+ million individual buildings , 2023, Scientific Data.

[2]  Yuyu Zhou,et al.  Satellite mapping of urban built-up heights reveals extreme infrastructure gaps and inequalities in the Global South , 2022, Proceedings of the National Academy of Sciences of the United States of America.

[3]  Jianping Wu,et al.  How Good Are Global Layers for Mapping Rural Settlements? Evidence from China , 2022, Land.

[4]  M. Song,et al.  Global 1 km × 1 km gridded revised real gross domestic product and electricity consumption during 1992–2019 based on calibrated nighttime light data , 2022, Scientific data.

[5]  Pratyush Tripathy,et al.  Generating megacity-scale building height maps without DGNSS surveyed GCPs: An open-source approach , 2022, Environment and Planning B: Urban Analytics and City Science.

[6]  Shuqing Zhao,et al.  A building height dataset across China in 2017 estimated by the spatially-informed approach , 2022, Scientific data.

[7]  N. Clinton,et al.  Estimating building height in China from ALOS AW3D30 , 2022, ISPRS Journal of Photogrammetry and Remote Sensing.

[8]  Bin Zhao,et al.  Quantifying the influence of 2D and 3D urban morphology on the thermal environment across climatic zones , 2022, Landscape and Urban Planning.

[9]  U. Schlink,et al.  3D building configuration as the driver of diurnal and nocturnal land surface temperatures: Application in Beijing's old city , 2021, Building and Environment.

[10]  Junqi Wang,et al.  Impacts of urban-scale building height diversity on urban climates: A case study of Nanjing, China , 2021 .

[11]  Miao Liu,et al.  High-resolution mapping of mainland China’s urban floor area , 2021 .

[12]  J. Zhang,et al.  An enhanced pixel-based phenological feature for accurate paddy rice mapping with Sentinel-2 imagery in Google Earth Engine , 2021, ISPRS Journal of Photogrammetry and Remote Sensing.

[13]  P. Hostert,et al.  Gridded population mapping for Germany based on building density, height and type from Earth Observation data using census disaggregation and bottom-up estimates , 2021, PloS one.

[14]  Patrick Hostert,et al.  National-scale mapping of building height using Sentinel-1 and Sentinel-2 time series , 2021, Remote sensing of environment.

[15]  Rozenn Dahyot,et al.  IM2ELEVATION: Building Height Estimation from Single-View Aerial Imagery , 2020, Remote. Sens..

[16]  Hannes Taubenböck,et al.  Continental-scale mapping and analysis of 3D building structure , 2020 .

[17]  P. Sutton,et al.  Building Volume Per Capita (BVPC): A Spatially Explicit Measure of Inequality Relevant to the SDGs , 2020, Frontiers in Sustainable Cities.

[18]  Le Yu,et al.  Mapping global urban boundaries from the global artificial impervious area (GAIA) data , 2020, Environmental Research Letters.

[19]  Pierre Soille,et al.  Convolutional neural networks for global human settlements mapping from Sentinel-2 satellite imagery , 2020, Neural Computing and Applications.

[20]  Karen C. Seto,et al.  Developing a method to estimate building height from Sentinel-1 data , 2020 .

[21]  W. Ju,et al.  Solar energy potential of urban buildings in 10 cities of China , 2020 .

[22]  Thomas Esch,et al.  Outlining where humans live, the World Settlement Footprint 2015 , 2019, Scientific Data.

[23]  Yuyu Zhou,et al.  Annual maps of global artificial impervious area (GAIA) between 1985 and 2018 , 2020 .

[24]  Jianping Wu,et al.  GDP spatialization in Ningbo City based on NPP/VIIRS night-time light and auxiliary data using random forest regression , 2020 .

[25]  K. Seto,et al.  Research gaps in knowledge of the impact of urban growth on biodiversity , 2019, Nature Sustainability.

[26]  Zhe Zhu,et al.  Understanding an urbanizing planet: Strategic directions for remote sensing , 2019, Remote Sensing of Environment.

[27]  Yun Zhao,et al.  Three dimensional (3D) spatial metrics for objects , 2019, Landscape Ecology.

[28]  Xin Huang,et al.  Investigating the effects of 3D urban morphology on the surface urban heat island effect in urban functional zones by using high-resolution remote sensing data: A case study of Wuhan, Central China , 2019, ISPRS Journal of Photogrammetry and Remote Sensing.

[29]  Lin Sun,et al.  Combinational shadow index for building shadow extraction in urban areas from Sentinel-2A MSI imagery , 2019, Int. J. Appl. Earth Obs. Geoinformation.

[30]  Xi Li,et al.  Mapping Urban Extent Using Luojia 1-01 Nighttime Light Imagery , 2018, Sensors.

[31]  Dean M. Hanink,et al.  Analyzing horizontal and vertical urban expansions in three East Asian megacities with the SS-coMCRF model , 2018, Landscape and Urban Planning.

[32]  Christoph Rüdiger,et al.  Sensitivity of Sentinel-1 Backscatter to Vegetation Dynamics: An Austrian Case Study , 2018, Remote. Sens..

[33]  Bisheng Yang,et al.  Compositional Structure Recognition of 3D Building Models Through Volumetric Analysis , 2018, IEEE Access.

[34]  Monjur Mourshed,et al.  A Shadow-Overlapping Algorithm for Estimating Building Heights From VHR Satellite Images , 2018, IEEE Geoscience and Remote Sensing Letters.

[35]  Michael Dixon,et al.  Google Earth Engine: Planetary-scale geospatial analysis for everyone , 2017 .

[36]  Qiong Wu,et al.  Measuring landscape pattern in three dimensional space , 2017 .

[37]  Thomas Jagdhuber,et al.  Sensitivity of Sentinel-1 backscatter to characteristics of buildings , 2017 .

[38]  Roberto Tomás,et al.  Monitoring activity at the Daguangbao mega-landslide (China) using Sentinel-1 TOPS time series interferometry , 2016 .

[39]  Jardar Lohne,et al.  Impact of Urban Density and Building Height on Energy Use in Cities , 2016 .

[40]  Rainald Borck,et al.  Will skyscrapers save the planet? Building height limits and urban greenhouse gas emissions* , 2016 .

[41]  Catherine Linard,et al.  High-resolution gridded population datasets for Latin America and the Caribbean in 2010, 2015, and 2020 , 2015, Scientific Data.

[42]  A. Tatem,et al.  Detecting Change in Urban Areas at Continental Scales with MODIS Data , 2015 .

[43]  Rolf Scheiber,et al.  The Sentinel-1 Toolbox , 2014 .

[44]  J. Guldmann,et al.  Spatial statistical analysis and simulation of the urban heat island in high-density central cities , 2014 .

[45]  R. Lucas,et al.  New global forest/non-forest maps from ALOS PALSAR data (2007–2010) , 2014 .

[46]  Katia Perini,et al.  Effects of vegetation, urban density, building height, and atmospheric conditions on local temperatures and thermal comfort , 2014 .

[47]  Peter M. Atkinson,et al.  Estimating the spatial distribution of the population of Riyadh, Saudi Arabia using remotely sensed built land cover and height data , 2013, Comput. Environ. Urban Syst..

[48]  Fei Deng,et al.  Integration of orthoimagery and lidar data for object-based urban thematic mapping using random forests , 2013 .

[49]  K. Seto,et al.  The Vegetation Adjusted NTL Urban Index: A new approach to reduce saturation and increase variation in nighttime luminosity , 2013 .

[50]  Malcolm Davidson,et al.  GMES Sentinel-1 mission , 2012 .

[51]  Matthias Drusch,et al.  Sentinel-2: ESA's Optical High-Resolution Mission for GMES Operational Services , 2012 .

[52]  Conghe Song,et al.  Impacts of landscape structure on surface urban heat islands: A case study of Shanghai, China , 2011 .

[53]  Andy Liaw,et al.  Classification and Regression by randomForest , 2007 .

[54]  Hanqiu Xu.  Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery , 2006 .

[55]  Johannes R. Sveinsson,et al.  Random Forests for land cover classification , 2006, Pattern Recognit. Lett..

[56]  E. Rodríguez,et al.  A Global Assessment of the SRTM Performance , 2006 .

[57]  Rick L. Lawrence,et al.  Mapping invasive plants using hyperspectral imagery and Breiman Cutler classifications (RandomForest) , 2006 .

[58]  E. Davidson,et al.  Satellite-based modeling of gross primary production in an evergreen needleleaf forest , 2004 .

[59]  Jay Gao,et al.  Use of normalized difference built-up index in automatically mapping urban areas from TM imagery , 2003 .

[60]  A. Huete,et al.  Overview of the radiometric and biophysical performance of the MODIS vegetation indices , 2002 .

[61]  C. Tucker.  Red and photographic infrared linear combinations for monitoring vegetation , 1979 .